cora.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CORA - Categorizing academic publications\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": [],
   "source": [
    "%pip install -q \"getml==1.5.0\" \"matplotlib==3.9.2\" \"ipywidgets==8.1.5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getML API version: 1.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import getml\n",
    "\n",
    "%matplotlib inline  \n",
    "\n",
    "print(f\"getML API version: {getml.__version__}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching ./getML --allow-push-notifications=true --allow-remote-ips=true --home-directory=/home/user --in-memory=true --install=false --launch-browser=true --log=false --token=token in /home/user/.getML/getml-1.5.0-x64-linux...\n",
      "Launched the getML Engine. The log output will be stored in /home/user/.getML/logs/20240912134108.log.\n",
      "\u001b[2K  Loading pipelines... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Connected to project <span style=\"color: #008000; text-decoration-color: #008000\">'cora'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Connected to project \u001b[32m'cora'\u001b[0m.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getml.engine.launch(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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Connection(dbname='CORA', dialect='mysql', host='relational.fel.cvut.cz', port=3306)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = getml.database.connect_mysql(\n",
    "    host=\"relational.fel.cvut.cz\",\n",
    "    dbname=\"CORA\",\n",
    "    port=3306,\n",
    "    user=\"guest\",\n",
    "    password=\"ctu-relational\"\n",
    ")\n",
    "\n",
    "conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_if_needed(name):\n",
    "    \"\"\"\n",
    "    Loads the data from the relational learning\n",
    "    repository, if the data frame has not already\n",
    "    been loaded.\n",
    "    \"\"\"\n",
    "    if 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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
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       "\n",
       "  <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",
       "    \n",
       "  </p>\n"
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       "name       paper_id   class_label           \n",
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       "                ...   ...                   \n",
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       "2708 rows x 2 columns\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paper"
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  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">35</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1956</span\n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">35</span\n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">8865</span\n",
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       "            <td class=\"char-align unused_float\">\n",
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       "            <td class=\"char-align unused_float\">\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5424</th>\n",
       "        \n",
       "          \n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">19621</span\n",
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       "        <th>5425</th>\n",
       "        \n",
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       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">853116</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "          \n",
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       "              \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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       "          \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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       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "                >&nbsp;</span\n",
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       "          \n",
       "        \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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       "              \n",
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       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">853118</span\n",
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       "                >&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",
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       "          \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",
       "            </td>\n",
       "          \n",
       "        \n",
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       "    \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",
       "   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: 0.09 MB\n",
       "type: getml.DataFrame"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "        <th>49211</th>\n",
       "        \n",
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       "            <td class=\"char-align unused_float\">\n",
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       "                >&nbsp;</span\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",
       "  <p>\n",
       "    49216 rows x 2 columns<br />\n",
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       "    name: content<br />\n",
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      ],
      "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",
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       "49215        1155073   word979      \n",
       "\n",
       "\n",
       "49216 rows x 2 columns\n",
       "memory usage: 1.20 MB\n",
       "type: getml.DataFrame"
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     },
     "execution_count": 9,
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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": 10,
   "metadata": {},
   "outputs": [
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       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Genetic_Algorithms</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">40</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Genetic_Algorithms</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">114</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Reinforcement_Learning</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">117</td>\n",
       "          \n",
       "        \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": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      \n",
       "        \n",
       "          <th class=\"join_key\">citing_paper_id</th>\n",
       "        \n",
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       "        <th>0</th>\n",
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       "            <td class=\"join_key\">35</td>\n",
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       "            <td class=\"join_key\">887</td>\n",
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       "          \n",
       "            <td class=\"join_key\">1033</td>\n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
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       "            <td class=\"join_key\">35</td>\n",
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       "          \n",
       "            <td class=\"join_key\">1688</td>\n",
       "          \n",
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       "        <th>3</th>\n",
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       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1956</td>\n",
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       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">8865</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
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       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5424</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">853116</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">19621</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5425</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">853116</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">853155</td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5426</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">853118</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1140289</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \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",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5428</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">954315</td>\n",
       "          \n",
       "        \n",
       "          \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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  th.sub-header {\n",
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       "  .join_key,\n",
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       "\n",
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       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th> name</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"join_key\">paper_id</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"categorical\">word_cited_id</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "      <tr>\n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header\"> role</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header join_key\">join_key</th>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <th class=\"sub-header categorical\">categorical  </th>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">35</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word100</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \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",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">word1228</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \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": 12,
     "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": 13,
   "metadata": {},
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       "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_full = getml.data.make_target_columns(paper, \"class_label\")\n",
    "data_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "\n",
       "  <p>\n",
       "    infinite number of  rows<br />\n",
       "    \n",
       "    type: StringColumnView<br />\n",
       "    \n",
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      ],
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       "          \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": 14,
     "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": 15,
   "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",
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       "  th:nth-child(1) {\n",
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       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">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",
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       "          \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",
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       "    \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",
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       "  </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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       "  }\n",
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       "  td.float {\n",
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       "  th.int {\n",
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       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name   </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type     </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">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",
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       "          \n",
       "            \n",
       "              <td class=\"int\">2708</td>\n",
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       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
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       "    \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": 15,
     "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": 16,
   "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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style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"573.0\" y1=\"41.0\" x2=\"573.0\" y2=\"100\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"573.0, 110 567.0, 100 579.0, 100 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"10.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"45.0\" x=\"324.0\" font-size=\"7pt\" >paper_id = citing_paper_id</tspan></text><line x1=\"150\" y1=\"153.0\" x2=\"490\" y2=\"153.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"500, 153.0 490, 147.0 490, 159.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"120.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"150.0\" x=\"324.0\" font-size=\"7pt\" >paper_id = citing_paper_id</tspan><tspan y=\"160.0\" x=\"324.0\" font-size=\"7pt\" >Relationship: many-to-one</tspan></text><line x1=\"150\" y1=\"263.0\" x2=\"573.0\" y2=\"263.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"573.0\" y1=\"261.0\" x2=\"573.0\" y2=\"320\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"573.0, 330 567.0, 320 579.0, 320 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"230.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"265.0\" x=\"324.0\" font-size=\"7pt\" >paper_id = cited_paper_id</tspan></text><line x1=\"150\" y1=\"373.0\" x2=\"490\" y2=\"373.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"500, 373.0 490, 367.0 490, 379.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"340.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"370.0\" x=\"324.0\" font-size=\"7pt\" >paper_id = cited_paper_id</tspan><tspan y=\"380.0\" x=\"324.0\" font-size=\"7pt\" >Relationship: many-to-one</tspan></text><line x1=\"650\" y1=\"153.0\" x2=\"1073.0\" y2=\"153.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"1073.0\" y1=\"151.0\" x2=\"1073.0\" y2=\"430\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"1073.0, 440 1067.0, 430 1079.0, 430 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"120.0\" x=\"749.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"155.0\" x=\"824.0\" font-size=\"7pt\" >cited_paper_id = paper_id</tspan></text><line x1=\"650\" y1=\"373.0\" x2=\"1073.0\" y2=\"373.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"1073.0\" y1=\"371.0\" x2=\"1073.0\" y2=\"430\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"1073.0, 440 1067.0, 430 1079.0, 430 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"340.0\" x=\"749.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"375.0\" x=\"824.0\" font-size=\"7pt\" >citing_paper_id = paper_id</tspan></text><line x1=\"650\" y1=\"483.0\" x2=\"990\" y2=\"483.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"1000, 483.0 990, 477.0 990, 489.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"450.0\" x=\"749.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"485.0\" x=\"824.0\" font-size=\"7pt\" >paper_id = paper_id</tspan></text></svg></div>\n",
       "            </div>\n",
       "\n",
       "            <div style='margin-top: 15px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\n",
       "            <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">data frames </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">staging table              </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">population</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": 16,
     "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": 17,
   "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": 18,
   "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": 18,
     "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": 19,
   "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": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K⠏ Preprocessing...                                            0% • 00:00"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">The pipeline check generated <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> issues labeled INFO and <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> issues labeled WARNING.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "The pipeline check generated \u001b[1;36m3\u001b[0m issues labeled INFO and \u001b[1;36m0\u001b[0m issues labeled WARNING.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "pipe1.check(container.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">The pipeline check generated <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> issues labeled INFO and <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> issues labeled WARNING.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "The pipeline check generated \u001b[1;36m3\u001b[0m issues labeled INFO and \u001b[1;36m0\u001b[0m issues labeled WARNING.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">To see the issues in full, run <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.check</span><span style=\"font-weight: bold\">()</span> on the pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "To see the issues in full, run \u001b[1;35m.check\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m on the pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Trying 3780 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:06\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:00:16.670446.\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-BRPpU2'])</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-BRPpU2'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.fit(container.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K⠧ Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━      90% • 00:01"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">The pipeline check generated <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> issues labeled INFO and <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> issues labeled WARNING.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "The pipeline check generated \u001b[1;36m3\u001b[0m issues labeled INFO and \u001b[1;36m0\u001b[0m issues labeled WARNING.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
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       "  th.float {\n",
       "    text-align: right !important;\n",
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       "  td.float {\n",
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       "    text-align: right !important;\n",
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       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">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": 22,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">The pipeline check generated <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> issues labeled INFO and <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> issues labeled WARNING.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "The pipeline check generated \u001b[1;36m3\u001b[0m issues labeled INFO and \u001b[1;36m0\u001b[0m issues labeled WARNING.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">To see the issues in full, run <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.check</span><span style=\"font-weight: bold\">()</span> on the pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "To see the issues in full, run \u001b[1;35m.check\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m on the pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:01\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:00:39.645936.\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-BRPpU2'])</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-BRPpU2'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.fit(container.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
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       "          <th class=\"float\">    auc</th>\n",
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       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:07</td>\n",
       "            \n",
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       "              <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",
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       "        <th>1</th>\n",
       "          \n",
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       "              <td class=\"datetime\">2024-09-12 13:05:07</td>\n",
       "            \n",
       "          \n",
       "            \n",
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       "            \n",
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       "            \n",
       "              <td class=\"str\">class_label=Genetic_Algorithms</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">1.0</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"float\">1.</td>\n",
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       "        <th>2</th>\n",
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       "              <td class=\"datetime\">2024-09-12 13:05:07</td>\n",
       "            \n",
       "          \n",
       "            \n",
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       "            \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\">2024-09-12 13:05:07</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\">0.027649</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:07</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\">0.008878</td>\n",
       "            \n",
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       "        <th></th>\n",
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       "              <td class=\"datetime\">...</td>\n",
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       "            \n",
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       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">...</td>\n",
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       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">...</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>9</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.163577</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>10</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.9744</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9873</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.082802</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>11</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.073926</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>12</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.052303</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>13</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.9562</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.977</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.128617</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "     date time             set used   target                             accuracy       auc   cross entropy\n",
       " 0   2024-09-12 13:05:07   train      class_label=Case_Based               0.9979    0.9999        0.02323 \n",
       " 1   2024-09-12 13:05:07   train      class_label=Genetic_Algorithms       1.0       1.            0.004862\n",
       " 2   2024-09-12 13:05:07   train      class_label=Neural_Networks          0.9846    0.9983        0.065852\n",
       " 3   2024-09-12 13:05:07   train      class_label=Probabilistic_Method...     0.9958    0.9998        0.027649\n",
       " 4   2024-09-12 13:05:07   train      class_label=Reinforcement_Learni...     0.9995    1.            0.008878\n",
       "     ...                   ...        ...                                     ...       ...             ...\n",
       " 9   2024-09-12 13:05:48   test       class_label=Neural_Networks          0.9513    0.9787        0.163577\n",
       "10   2024-09-12 13:05:48   test       class_label=Probabilistic_Method...     0.9744    0.9873        0.082802\n",
       "11   2024-09-12 13:05:48   test       class_label=Reinforcement_Learni...     0.9805    0.9736        0.073926\n",
       "12   2024-09-12 13:05:48   test       class_label=Rule_Learning            0.9842    0.9937        0.052303\n",
       "13   2024-09-12 13:05:48   test       class_label=Theory                   0.9562    0.977         0.128617"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.score(container.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
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       "            \n",
       "              <td class=\"float\">0.9745</td>\n",
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      ],
      "text/plain": [
       "     date time             set used   target                             accuracy       auc   cross entropy\n",
       " 0   2024-09-12 13:05:47   train      class_label=Case_Based               1.0       1.            0.009385\n",
       " 1   2024-09-12 13:05:47   train      class_label=Genetic_Algorithms       1.0       1.            0.004222\n",
       " 2   2024-09-12 13:05:47   train      class_label=Neural_Networks          0.991     0.9996        0.03766 \n",
       " 3   2024-09-12 13:05:47   train      class_label=Probabilistic_Method...     0.9989    1.            0.013846\n",
       " 4   2024-09-12 13:05:47   train      class_label=Reinforcement_Learni...     1.0       1.            0.004409\n",
       "     ...                   ...        ...                                     ...       ...             ...\n",
       " 9   2024-09-12 13:05:51   test       class_label=Neural_Networks          0.9391    0.9757        0.193588\n",
       "10   2024-09-12 13:05:51   test       class_label=Probabilistic_Method...     0.9769    0.9892        0.072601\n",
       "11   2024-09-12 13:05:51   test       class_label=Reinforcement_Learni...     0.9769    0.9773        0.094757\n",
       "12   2024-09-12 13:05:51   test       class_label=Rule_Learning            0.9842    0.9912        0.060603\n",
       "13   2024-09-12 13:05:51   test       class_label=Theory                   0.9488    0.9745        0.142024"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
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       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.16358</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.9744</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9873</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0828</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.07393</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.0523</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>6</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:48</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\">0.9562</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.977</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.12862</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    date time             set used   target                             accuracy       auc   cross entropy\n",
       "0   2024-09-12 13:05:48   test       class_label=Case_Based               0.9708    0.9861         0.08689\n",
       "1   2024-09-12 13:05:48   test       class_label=Genetic_Algorithms       0.9842    0.9981         0.04915\n",
       "2   2024-09-12 13:05:48   test       class_label=Neural_Networks          0.9513    0.9787         0.16358\n",
       "3   2024-09-12 13:05:48   test       class_label=Probabilistic_Method...     0.9744    0.9873         0.0828 \n",
       "4   2024-09-12 13:05:48   test       class_label=Reinforcement_Learni...     0.9805    0.9736         0.07393\n",
       "5   2024-09-12 13:05:48   test       class_label=Rule_Learning            0.9842    0.9937         0.0523 \n",
       "6   2024-09-12 13:05:48   test       class_label=Theory                   0.9562    0.977          0.12862"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.scores.filter(lambda score: score.set_used == \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          <th class=\"float\">    auc</th>\n",
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       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:51</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Case_Based</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9744</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9895</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.08319</td>\n",
       "            \n",
       "          \n",
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       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:51</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">class_label=Genetic_Algorithms</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9903</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9988</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.03866</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:51</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\">0.9391</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9757</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.19359</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:51</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\">0.9769</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9892</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0726</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:51</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\">0.9769</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9773</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.09476</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>5</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:51</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\">0.9912</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.0606</td>\n",
       "            \n",
       "          \n",
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       "    \n",
       "      <tr>\n",
       "        <th>6</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 13:05:51</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\">0.9488</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9745</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.14202</td>\n",
       "            \n",
       "          \n",
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       "</table>"
      ],
      "text/plain": [
       "    date time             set used   target                             accuracy       auc   cross entropy\n",
       "0   2024-09-12 13:05:51   test       class_label=Case_Based               0.9744    0.9895         0.08319\n",
       "1   2024-09-12 13:05:51   test       class_label=Genetic_Algorithms       0.9903    0.9988         0.03866\n",
       "2   2024-09-12 13:05:51   test       class_label=Neural_Networks          0.9391    0.9757         0.19359\n",
       "3   2024-09-12 13:05:51   test       class_label=Probabilistic_Method...     0.9769    0.9892         0.0726 \n",
       "4   2024-09-12 13:05:51   test       class_label=Reinforcement_Learni...     0.9769    0.9773         0.09476\n",
       "5   2024-09-12 13:05:51   test       class_label=Rule_Learning            0.9842    0.9912         0.0606 \n",
       "6   2024-09-12 13:05:51   test       class_label=Theory                   0.9488    0.9745         0.14202"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9849300280909873\n",
      "0.9851644641057435\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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9716373760222724\n",
      "0.9700713415695145\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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    }
   ],
   "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": 31,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Share of accurately predicted class labels (pipe1):\n",
      "0.9001218026796589\n",
      "\n",
      "Share of accurately predicted class labels (pipe2):\n",
      "0.8964677222898904\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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "TARGET_NUM = 0"
   ]
  },
  {
   "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, 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": 35,
   "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": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_51\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_51\" AS\n",
       "SELECT AVG( t2.\"t4__class_label__mapping_2_target_3_avg\" ) AS \"feature_1_51\",\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_51\";\\n\\nCREATE TABLE \"FEATURE_1_51\" AS\\nSELECT AVG( t2.\"t4__class_label__mapping_2_target_3_avg\" ) AS \"feature_1_51\",\\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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.features.to_sql()[pipe1.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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\" > 5.516436 ) 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\" > 5.516436 ) 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\" <= 5.516436 ) AND ( f_6_2.\"feature_6_2_20\" > 0.747603 ) THEN 13.8749941754607\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" <= 5.516436 ) AND ( f_6_2.\"feature_6_2_20\" <= 0.747603 ) THEN 8.209072454235654\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_13\" > 0.575234 ) THEN 5.856092769106291\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_13\" <= 0.575234 ) AND ( f_6_2.\"feature_6_2_4\" > 1.058131 ) THEN -2.241272133429655\n",
       "        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_13\" <= 0.575234 ) AND ( f_6_2.\"feature_6_2_4\" <= 1.058131 ) THEN -0.6025668375656026\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\" > 5.516436 ) 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\" > 5.516436 ) 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\" <= 5.516436 ) AND ( f_6_2.\"feature_6_2_20\" > 0.747603 ) THEN 13.8749941754607\\n        WHEN ( f_6_2.\"feature_6_2_2\" > 3.142350 ) AND ( f_6_2.\"feature_6_2_11\" <= 5.516436 ) AND ( f_6_2.\"feature_6_2_20\" <= 0.747603 ) THEN 8.209072454235654\\n        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_13\" > 0.575234 ) THEN 5.856092769106291\\n        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_13\" <= 0.575234 ) AND ( f_6_2.\"feature_6_2_4\" > 1.058131 ) THEN -2.241272133429655\\n        WHEN ( f_6_2.\"feature_6_2_2\" <= 3.142350 ) AND ( f_6_2.\"feature_6_2_13\" <= 0.575234 ) AND ( f_6_2.\"feature_6_2_4\" <= 1.058131 ) THEN -0.6025668375656026\\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": 38,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a folder containing the SQL code.\n",
    "pipe1.features.to_sql().save(\"cora_pipeline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 41,
   "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>90.0%</td>\n",
       "      <td>98.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Relboost</td>\n",
       "      <td>89.6%</td>\n",
       "      <td>98.5%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Approach Accuracy    AUC\n",
       "0  FastProp    90.0%  98.5%\n",
       "1  Relboost    89.6%  98.5%"
      ]
     },
     "execution_count": 41,
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "getml.engine.shutdown()"
   ]
  },
  {
   "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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