movie_lens.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MovieLens - Predicting a user's gender based on the movies they have watched\n",
    "\n",
    "In this notebook, we will apply getML to a dataset that is often used for benchmarking in the relational learning literature: The MovieLens dataset.\n",
    "\n",
    "Summary:\n",
    "\n",
    "- Prediction type: __Classification model__\n",
    "- Domain: __Entertainment__\n",
    "- Prediction target: __The gender of a user__ \n",
    "- Population size: __6039__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "The MovieLens dataset is often used in the relational learning literature has a benchmark for newly developed algorithms. Following the tradition, we benchmark getML's own algorithms on this dataset as well. The task is to predict a user's gender based on the movies they have watched.\n",
    "\n",
    "\n",
    "It has been downloaded from the [CTU Prague relational learning repository](https://relational.fit.cvut.cz/dataset/MovieLens) (Motl and Schulte, 2015) (Now residing at [relational-data.org](https://relational-data.org/dataset/MovieLens).)."
   ]
  },
  {
   "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 os\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import getml\n",
    "\n",
    "os.environ[\"PYARROW_IGNORE_TIMEZONE\"] = \"1\"\n",
    "%matplotlib inline  \n",
    "\n",
    "print(f\"getML API version: {getml.__version__}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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/20240912151421.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\">'MovieLens'</span>.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Connected to project \u001b[32m'MovieLens'\u001b[0m.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getml.engine.launch(allow_remote_ips=True, token=\"token\")\n",
    "getml.engine.set_project(\"MovieLens\")"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Connection(dbname='imdb_MovieLens',\n",
       "           dialect='mysql',\n",
       "           host='relational.fel.cvut.cz',\n",
       "           port=3306)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = getml.database.connect_mysql(\n",
    "    host=\"relational.fel.cvut.cz\",\n",
    "    dbname=\"imdb_MovieLens\",\n",
    "    port=3306,\n",
    "    user=\"guest\",\n",
    "    password=\"ctu-relational\",\n",
    ")\n",
    "\n",
    "conn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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(name=name, table_name=name, conn=conn)\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": [
    "users = load_if_needed(\"users\")\n",
    "u2base = load_if_needed(\"u2base\")\n",
    "movies = load_if_needed(\"movies\")\n",
    "movies2directors = load_if_needed(\"movies2directors\")\n",
    "directors = load_if_needed(\"directors\")\n",
    "movies2actors = load_if_needed(\"movies2actors\")\n",
    "actors = load_if_needed(\"actors\")"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "users[\"target\"] = users.u_gender == \"F\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">56</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">M</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>6038</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">5980</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">5</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">56</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"unused_string\">M</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    6039 rows x 5 columns<br />\n",
       "    memory usage: 0.21 MB<br />\n",
       "    name: users<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name     userid   target   occupation          age   u_gender     \n",
       "role   join_key   target   categorical   numerical   unused_string\n",
       "   0          1        1   2                     1   F            \n",
       "   1         51        1   2                     1   F            \n",
       "   2         75        1   2                     1   F            \n",
       "   3         86        1   2                     1   F            \n",
       "   4         99        1   2                     1   F            \n",
       "            ...      ...   ...                 ...   ...          \n",
       "6034       5658        0   5                    56   M            \n",
       "6035       5669        0   5                    56   M            \n",
       "6036       5703        0   5                    56   M            \n",
       "6037       5948        0   5                    56   M            \n",
       "6038       5980        0   5                    56   M            \n",
       "\n",
       "\n",
       "6039 rows x 5 columns\n",
       "memory usage: 0.21 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.set_role(\"userid\", getml.data.roles.join_key)\n",
    "users.set_role(\"age\", getml.data.roles.numerical)\n",
    "users.set_role(\"occupation\", getml.data.roles.categorical)\n",
    "users.set_role(\"target\", getml.data.roles.target)\n",
    "\n",
    "users.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            <td class=\"join_key\">4</td>\n",
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       "          \n",
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       "        <th>996157</th>\n",
       "        \n",
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       "            <td class=\"join_key\">6040</td>\n",
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       "          \n",
       "            <td class=\"join_key\">2581428</td>\n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
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       "        <th>996158</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">6040</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2593112</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">5</span\n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    996159 rows x 3 columns<br />\n",
       "    memory usage: 15.94 MB<br />\n",
       "    name: u2base<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "  name     userid    movieid      rating\n",
       "  role   join_key   join_key   numerical\n",
       "     0          2    1964242           1\n",
       "     1          2    2219779           1\n",
       "     2          3    1856939           1\n",
       "     3          4    2273044           1\n",
       "     4          5    1681655           1\n",
       "              ...        ...         ...\n",
       "996154       6040    2560616           5\n",
       "996155       6040    2564194           5\n",
       "996156       6040    2581228           5\n",
       "996157       6040    2581428           5\n",
       "996158       6040    2593112           5\n",
       "\n",
       "\n",
       "996159 rows x 3 columns\n",
       "memory usage: 15.94 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u2base.set_role([\"userid\", \"movieid\"], getml.data.roles.join_key)\n",
    "u2base.set_role(\"rating\", getml.data.roles.numerical)\n",
    "\n",
    "u2base.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "            <td class=\"join_key\">1672052</td>\n",
       "          \n",
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       "          \n",
       "            <td class=\"categorical\">T</td>\n",
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       "            <td class=\"categorical\">other</td>\n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
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       "              <span class=\"left\">2</span\n",
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       "        <th>1</th>\n",
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       "            <td class=\"join_key\">1672111</td>\n",
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       "          \n",
       "            <td class=\"categorical\">T</td>\n",
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       "        <th>2</th>\n",
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       "            <td class=\"join_key\">1672580</td>\n",
       "          \n",
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       "            <td class=\"categorical\">T</td>\n",
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       "            <td class=\"categorical\">USA</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1672716</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">T</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">USA</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1672946</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">T</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">USA</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">0</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3827</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2591814</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">T</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">other</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3828</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2592334</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">T</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">USA</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3829</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2592963</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">France</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3830</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2593112</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">T</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">USA</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3831</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2593313</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">other</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    3832 rows x 5 columns<br />\n",
       "    memory usage: 0.11 MB<br />\n",
       "    name: movies<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name    movieid   isEnglish     country            year   runningtime\n",
       "role   join_key   categorical   categorical   numerical     numerical\n",
       "   0    1672052   T             other                 3             2\n",
       "   1    1672111   T             other                 4             2\n",
       "   2    1672580   T             USA                   4             3\n",
       "   3    1672716   T             USA                   4             2\n",
       "   4    1672946   T             USA                   4             0\n",
       "            ...   ...           ...                 ...           ...\n",
       "3827    2591814   T             other                 4             2\n",
       "3828    2592334   T             USA                   4             2\n",
       "3829    2592963   F             France                2             2\n",
       "3830    2593112   T             USA                   4             1\n",
       "3831    2593313   F             other                 4             3\n",
       "\n",
       "\n",
       "3832 rows x 5 columns\n",
       "memory usage: 0.11 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.set_role(\"movieid\", getml.data.roles.join_key)\n",
    "movies.set_role([\"year\", \"runningtime\"], getml.data.roles.numerical)\n",
    "movies.set_role([\"isEnglish\", \"country\"], getml.data.roles.categorical)\n",
    "\n",
    "movies.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th>name</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"join_key\"> movieid</th>\n",
       "        \n",
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       "        \n",
       "          <th class=\"join_key\">directorid</th>\n",
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       "          <th class=\"categorical\">genre      </th>\n",
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       "            <th class=\"sub-header join_key\">  join_key</th>\n",
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       "            <th class=\"sub-header categorical\">categorical</th>\n",
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       "    \n",
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       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1672111</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">54934</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Action</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1672946</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">188940</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Action</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1679461</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">179783</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Action</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1691387</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">291700</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Action</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">1693305</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">14663</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Action</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=\"join_key\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4136</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2570825</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">265215</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Other</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4137</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2572478</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">149311</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Other</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4138</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2577062</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">304827</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Other</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4139</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2590181</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">270707</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Other</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4140</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2591814</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">57348</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">Other</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    4141 rows x 3 columns<br />\n",
       "    memory usage: 0.05 MB<br />\n",
       "    name: movies2directors<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name    movieid   directorid   genre      \n",
       "role   join_key     join_key   categorical\n",
       "   0    1672111        54934   Action     \n",
       "   1    1672946       188940   Action     \n",
       "   2    1679461       179783   Action     \n",
       "   3    1691387       291700   Action     \n",
       "   4    1693305        14663   Action     \n",
       "            ...          ...   ...        \n",
       "4136    2570825       265215   Other      \n",
       "4137    2572478       149311   Other      \n",
       "4138    2577062       304827   Other      \n",
       "4139    2590181       270707   Other      \n",
       "4140    2591814        57348   Other      \n",
       "\n",
       "\n",
       "4141 rows x 3 columns\n",
       "memory usage: 0.05 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies2directors.set_role([\"movieid\", \"directorid\"], getml.data.roles.join_key)\n",
    "movies2directors.set_role(\"genre\", getml.data.roles.categorical)\n",
    "\n",
    "movies2directors.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          <th>name</th>\n",
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       "          <th class=\"numerical\">d_quality</th>\n",
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       "          <th class=\"numerical\">avg_revenue</th>\n",
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       "            <th class=\"sub-header join_key\">  join_key</th>\n",
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       "            <td class=\"join_key\">67</td>\n",
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       "            <td class=\"char-align numerical\">\n",
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       "          \n",
       "            <td class=\"char-align numerical\">\n",
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       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>2196</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">305962</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
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       "              \n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "      <tr>\n",
       "        <th>2197</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">305978</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
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       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>2198</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">306168</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
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       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">2</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>2199</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">306343</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2200</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">306351</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    2201 rows x 3 columns<br />\n",
       "    memory usage: 0.04 MB<br />\n",
       "    name: directors<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "name   directorid   d_quality   avg_revenue\n",
       "role     join_key   numerical     numerical\n",
       "   0           67           4             1\n",
       "   1           92           2             3\n",
       "   2          284           4             0\n",
       "   3          708           4             1\n",
       "   4          746           4             4\n",
       "              ...         ...           ...\n",
       "2196       305962           4             4\n",
       "2197       305978           4             2\n",
       "2198       306168           3             2\n",
       "2199       306343           4             1\n",
       "2200       306351           4             1\n",
       "\n",
       "\n",
       "2201 rows x 3 columns\n",
       "memory usage: 0.04 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors.set_role(\"directorid\", getml.data.roles.join_key)\n",
    "directors.set_role([\"d_quality\", \"avg_revenue\"], getml.data.roles.numerical)\n",
    "\n",
    "directors.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "          <th class=\"join_key\"> movieid</th>\n",
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       "            <td class=\"join_key\">149985</td>\n",
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       "            <td class=\"join_key\">261595</td>\n",
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       "            <td class=\"join_key\">...</td>\n",
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       "            <td class=\"join_key\">...</td>\n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">...</span\n",
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       "      <tr>\n",
       "        <th>138344</th>\n",
       "        \n",
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       "            <td class=\"join_key\">2593313</td>\n",
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       "          \n",
       "            <td class=\"join_key\">947005</td>\n",
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       "            \n",
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       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>138345</th>\n",
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       "            <td class=\"join_key\">2593313</td>\n",
       "          \n",
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       "          \n",
       "            <td class=\"join_key\">1090590</td>\n",
       "          \n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
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       "        <th>138346</th>\n",
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       "            <td class=\"join_key\">2593313</td>\n",
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       "            <td class=\"join_key\">1347419</td>\n",
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       "            <td class=\"char-align numerical\">\n",
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       "                >&nbsp;</span\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>138347</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2593313</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2099917</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>138348</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2593313</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"join_key\">2633550</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align numerical\">\n",
       "              <span class=\"left\">3</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    138349 rows x 3 columns<br />\n",
       "    memory usage: 2.21 MB<br />\n",
       "    name: movies2actors<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "  name    movieid    actorid    cast_num\n",
       "  role   join_key   join_key   numerical\n",
       "     0    1672580     981535           0\n",
       "     1    1672946    1094968           0\n",
       "     2    1673647     149985           0\n",
       "     3    1673647     261595           0\n",
       "     4    1673647     781357           0\n",
       "              ...        ...         ...\n",
       "138344    2593313     947005           3\n",
       "138345    2593313    1090590           3\n",
       "138346    2593313    1347419           3\n",
       "138347    2593313    2099917           3\n",
       "138348    2593313    2633550           3\n",
       "\n",
       "\n",
       "138349 rows x 3 columns\n",
       "memory usage: 2.21 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies2actors.set_role([\"movieid\", \"actorid\"], getml.data.roles.join_key)\n",
    "movies2actors.set_role(\"cast_num\", getml.data.roles.numerical)\n",
    "\n",
    "movies2actors.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to separate our data set into a training, testing and validation set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            <td class=\"join_key\">2749377</td>\n",
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       "            <td class=\"join_key\">2749386</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">F</td>\n",
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       "              \n",
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       "          \n",
       "            <td class=\"char-align numerical\">\n",
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       "</table>\n",
       "\n",
       "  <p>\n",
       "    98690 rows x 3 columns<br />\n",
       "    memory usage: 1.58 MB<br />\n",
       "    name: actors<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       " name    actorid   a_gender      a_quality\n",
       " role   join_key   categorical   numerical\n",
       "    0          4   M                     4\n",
       "    1         16   M                     0\n",
       "    2         28   M                     4\n",
       "    3        566   M                     4\n",
       "    4        580   M                     4\n",
       "             ...   ...                 ...\n",
       "98685    2749162   F                     3\n",
       "98686    2749168   F                     3\n",
       "98687    2749204   F                     3\n",
       "98688    2749377   F                     4\n",
       "98689    2749386   F                     4\n",
       "\n",
       "\n",
       "98690 rows x 3 columns\n",
       "memory usage: 1.58 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors.set_role(\"actorid\", getml.data.roles.join_key)\n",
    "actors.set_role(\"a_quality\", getml.data.roles.numerical)\n",
    "actors.set_role(\"a_gender\", getml.data.roles.categorical)\n",
    "\n",
    "actors.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "        \n",
       "          \n",
       "            <td>...</td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    infinite number of  rows<br />\n",
       "    \n",
       "    type: StringColumnView<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       "          \n",
       " 0   train\n",
       " 1   train\n",
       " 2   train\n",
       " 3   test \n",
       " 4   test \n",
       "     ...  \n",
       "\n",
       "\n",
       "infinite number of  rows\n",
       "type: StringColumnView"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split = getml.data.split.random(train=0.75, test=0.25)\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style='margin-top: 15px;'>\n",
       "<div style='float: left; margin-right: 50px;'>\n",
       "<div style='margin-bottom: 10px; font-size: 1rem;'>population</div>\n",
       "    <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">subset</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"int\">rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">users</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">1511</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">View</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">users</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">4528</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">View</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div style='float: left;'>\n",
       "<div style='margin-bottom: 10px; font-size: 1rem;'>peripheral</div>\n",
       "    <style>\n",
       "  th {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  td {\n",
       "    text-align: left !important;\n",
       "  }\n",
       "  th:nth-child(1) {\n",
       "    text-align: right;\n",
       "    border-right: 1px solid LightGray;\n",
       "  }\n",
       "  th.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.float {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  th.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "  td.int {\n",
       "    text-align: right !important;\n",
       "  }\n",
       "</style>\n",
       "\n",
       "<table class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">name            </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"int\">  rows</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type     </th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">u2base</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">996159</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\">movies</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">3832</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\">movies2directors</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">4141</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">directors</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">2201</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">movies2actors</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">138349</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>5</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">actors</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"int\">98690</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">DataFrame</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "population\n",
       "    subset   name    rows   type\n",
       "0   test     users   1511   View\n",
       "1   train    users   4528   View\n",
       "\n",
       "peripheral\n",
       "    name                 rows   type     \n",
       "0   u2base             996159   DataFrame\n",
       "1   movies               3832   DataFrame\n",
       "2   movies2directors     4141   DataFrame\n",
       "3   directors            2201   DataFrame\n",
       "4   movies2actors      138349   DataFrame\n",
       "5   actors              98690   DataFrame"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "container = getml.data.Container(population=users, split=split)\n",
    "\n",
    "container.add(\n",
    "    u2base=u2base,\n",
    "    movies=movies,\n",
    "    movies2directors=movies2directors,\n",
    "    directors=directors,\n",
    "    movies2actors=movies2actors,\n",
    "    actors=actors,\n",
    ")\n",
    "\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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:210px;width:2160px;position:relative;\"><svg height=\"200\" width=\"2150\"><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\">directors</text><rect x=\"51\" y=\"10\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"10\" x2=\"67.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"10\" x2=\"83.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"26.0\" x2=\"99\" y2=\"26.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"42.0\" x2=\"99\" y2=\"42.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"0\" x=\"500\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"73.8\" x=\"575.0\" dominant-baseline=\"middle\" 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/><line x1=\"83.0\" y1=\"120\" x2=\"83.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"136.0\" x2=\"99\" y2=\"136.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"152.0\" x2=\"99\" y2=\"152.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"110\" x=\"500\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"183.8\" x=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">movies2actors</text><rect x=\"551\" y=\"120\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"567.0\" y1=\"120\" x2=\"567.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"583.0\" y1=\"120\" x2=\"583.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"136.0\" x2=\"599\" y2=\"136.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"152.0\" x2=\"599\" y2=\"152.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"110\" x=\"1000\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"183.8\" x=\"1075.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">movies</text><rect x=\"1051\" y=\"120\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"1067.0\" y1=\"120\" x2=\"1067.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"1083.0\" y1=\"120\" x2=\"1083.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"1051\" y1=\"136.0\" x2=\"1099\" y2=\"136.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"1051\" y1=\"152.0\" x2=\"1099\" y2=\"152.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"110\" x=\"1500\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"183.8\" x=\"1575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">u2base</text><rect x=\"1551\" y=\"120\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"1567.0\" y1=\"120\" x2=\"1567.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"1583.0\" y1=\"120\" x2=\"1583.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"1551\" y1=\"136.0\" x2=\"1599\" y2=\"136.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"1551\" y1=\"152.0\" x2=\"1599\" y2=\"152.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"110\" x=\"2000\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"183.8\" x=\"2075.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">users</text><rect x=\"2051\" y=\"120\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"2067.0\" y1=\"120\" x2=\"2067.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"2083.0\" y1=\"120\" x2=\"2083.0\" y2=\"168\" style=\"stroke:white;stroke-width:3\" /><line x1=\"2051\" y1=\"136.0\" x2=\"2099\" y2=\"136.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"2051\" y1=\"152.0\" x2=\"2099\" y2=\"152.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"150\" y1=\"43.0\" x2=\"490\" y2=\"43.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"500, 43.0 490, 37.0 490, 49.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"10.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"40.0\" x=\"324.0\" font-size=\"7pt\" >directorid = directorid</tspan><tspan y=\"50.0\" x=\"324.0\" font-size=\"7pt\" >Relationship: many-to-one</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\" >actorid = actorid</tspan><tspan y=\"160.0\" x=\"324.0\" font-size=\"7pt\" >Relationship: many-to-one</tspan></text><line x1=\"650\" y1=\"43.0\" x2=\"1073.0\" y2=\"43.0\" style=\"stroke:#808080;;stroke-width:4\" /><line x1=\"1073.0\" y1=\"41.0\" x2=\"1073.0\" y2=\"100\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"1073.0, 110 1067.0, 100 1079.0, 100 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"10.0\" 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=\"40.0\" x=\"824.0\" font-size=\"7pt\" >movieid = movieid</tspan><tspan y=\"50.0\" x=\"824.0\" font-size=\"7pt\" >Relationship: propositionalization</tspan></text><line x1=\"650\" y1=\"153.0\" x2=\"990\" y2=\"153.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"1000, 153.0 990, 147.0 990, 159.0 \" 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=\"150.0\" x=\"824.0\" font-size=\"7pt\" >movieid = movieid</tspan><tspan y=\"160.0\" x=\"824.0\" font-size=\"7pt\" >Relationship: propositionalization</tspan></text><line x1=\"1150\" y1=\"153.0\" x2=\"1490\" y2=\"153.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"1500, 153.0 1490, 147.0 1490, 159.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"120.0\" x=\"1249.0\" 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=\"1324.0\" font-size=\"7pt\" >movieid = movieid</tspan><tspan y=\"160.0\" x=\"1324.0\" font-size=\"7pt\" >Relationship: many-to-one</tspan></text><line x1=\"1650\" y1=\"153.0\" x2=\"1990\" y2=\"153.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"2000, 153.0 1990, 147.0 1990, 159.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"120.0\" x=\"1749.0\" 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=\"1824.0\" font-size=\"7pt\" >userid = userid</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\">users</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">USERS__STAGING_TABLE_1</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">movies2actors, actors</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">MOVIES2ACTORS__STAGING_TABLE_2</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>2</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">movies2directors, directors</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">MOVIES2DIRECTORS__STAGING_TABLE_3</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">u2base, movies</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">U2BASE__STAGING_TABLE_4</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "            </div>\n",
       "            "
      ],
      "text/plain": [
       "users:\n",
       "  columns:\n",
       "  - occupation: categorical\n",
       "  - userid: join_key\n",
       "  - age: numerical\n",
       "  - target: target\n",
       "  - u_gender: unused_string\n",
       "\n",
       "  joins:\n",
       "  - right: 'u2base'\n",
       "    on: (users.userid, u2base.userid)\n",
       "    relationship: 'many-to-many'\n",
       "    lagged_targets: False\n",
       "\n",
       "u2base:\n",
       "  columns:\n",
       "  - userid: join_key\n",
       "  - movieid: join_key\n",
       "  - rating: numerical\n",
       "\n",
       "  joins:\n",
       "  - right: 'movies'\n",
       "    on: (u2base.movieid, movies.movieid)\n",
       "    relationship: 'many-to-one'\n",
       "    lagged_targets: False\n",
       "\n",
       "movies:\n",
       "  columns:\n",
       "  - isEnglish: categorical\n",
       "  - country: categorical\n",
       "  - movieid: join_key\n",
       "  - year: numerical\n",
       "  - runningtime: numerical\n",
       "\n",
       "  joins:\n",
       "  - right: 'movies2directors'\n",
       "    on: (movies.movieid, movies2directors.movieid)\n",
       "    relationship: 'propositionalization'\n",
       "    lagged_targets: False\n",
       "  - right: 'movies2actors'\n",
       "    on: (movies.movieid, movies2actors.movieid)\n",
       "    relationship: 'propositionalization'\n",
       "    lagged_targets: False\n",
       "\n",
       "movies2directors:\n",
       "  columns:\n",
       "  - genre: categorical\n",
       "  - movieid: join_key\n",
       "  - directorid: join_key\n",
       "\n",
       "  joins:\n",
       "  - right: 'directors'\n",
       "    on: (movies2directors.directorid, directors.directorid)\n",
       "    relationship: 'many-to-one'\n",
       "    lagged_targets: False\n",
       "\n",
       "directors:\n",
       "  columns:\n",
       "  - directorid: join_key\n",
       "  - d_quality: numerical\n",
       "  - avg_revenue: numerical\n",
       "\n",
       "movies2actors:\n",
       "  columns:\n",
       "  - movieid: join_key\n",
       "  - actorid: join_key\n",
       "  - cast_num: numerical\n",
       "\n",
       "  joins:\n",
       "  - right: 'actors'\n",
       "    on: (movies2actors.actorid, actors.actorid)\n",
       "    relationship: 'many-to-one'\n",
       "    lagged_targets: False\n",
       "\n",
       "actors:\n",
       "  columns:\n",
       "  - a_gender: categorical\n",
       "  - actorid: join_key\n",
       "  - a_quality: numerical"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm = getml.data.DataModel(users.to_placeholder())\n",
    "\n",
    "dm.add(\n",
    "    getml.data.to_placeholder(\n",
    "        u2base=u2base,\n",
    "        movies=movies,\n",
    "        movies2directors=movies2directors,\n",
    "        directors=directors,\n",
    "        movies2actors=movies2actors,\n",
    "        actors=actors,\n",
    "    )\n",
    ")\n",
    "\n",
    "dm.population.join(dm.u2base, on=\"userid\")\n",
    "\n",
    "dm.u2base.join(\n",
    "    dm.movies, on=\"movieid\", relationship=getml.data.relationship.many_to_one\n",
    ")\n",
    "\n",
    "dm.movies.join(\n",
    "    dm.movies2directors,\n",
    "    on=\"movieid\",\n",
    "    relationship=getml.data.relationship.propositionalization,\n",
    ")\n",
    "\n",
    "dm.movies2directors.join(\n",
    "    dm.directors, on=\"directorid\", relationship=getml.data.relationship.many_to_one\n",
    ")\n",
    "\n",
    "dm.movies.join(\n",
    "    dm.movies2actors,\n",
    "    on=\"movieid\",\n",
    "    relationship=getml.data.relationship.propositionalization,\n",
    ")\n",
    "\n",
    "dm.movies2actors.join(\n",
    "    dm.actors, on=\"actorid\", relationship=getml.data.relationship.many_to_one\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 will set up two pipelines. One of them uses `FastProp`, the other one uses `Relboost`. Note that we have marked some of the joins in the data model with the `propositionalization` tag. This means that `FastProp` will be used for these relationships, even for the second pipeline. This can significantly speed up the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "    loss_function=getml.feature_learning.loss_functions.CrossEntropyLoss,\n",
    "    num_subfeatures=50,\n",
    "    num_threads=1,\n",
    ")\n",
    "\n",
    "predictor = getml.predictors.XGBoostClassifier(\n",
    "    max_depth=5,\n",
    "    n_jobs=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Build the pipeline__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='users',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='users',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop'])"
      ]
     },
     "execution_count": 19,
     "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='users',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='users',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2 = getml.pipeline.Pipeline(\n",
    "    tags=[\"relboost\"],\n",
    "    data_model=dm,\n",
    "    preprocessors=[mapping],\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": 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% • 00:12\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\">2</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;36m2\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\">FOREIGN KEYS NOT FOUND</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">When joining U2BASE__STAGING_TABLE_4 and MOVIES2DIRECTORS__STAGING_TABLE_3 over &#x27;movieid&#x27; and &#x27;movieid&#x27;, there are no corresponding entries for 0.159513% of entries in &#x27;movieid&#x27; in &#x27;U2BASE__STAGING_TABLE_4&#x27;. You might want to double-check your join keys.</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 U2BASE__STAGING_TABLE_4 and MOVIES2ACTORS__STAGING_TABLE_2 over &#x27;movieid&#x27; and &#x27;movieid&#x27;, there are no corresponding entries for 0.336492% of entries in &#x27;movieid&#x27; in &#x27;U2BASE__STAGING_TABLE_4&#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   FOREIGN KEYS NOT FOUND   When joining U2BASE__STAGING_TAB...\n",
       "1   INFO   FOREIGN KEYS NOT FOUND   When joining U2BASE__STAGING_TAB..."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.check(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  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\">2</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;36m2\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 941 features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:34\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:18\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:02\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:03\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:00:59.177060.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='users',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop', 'container-AA9NXV'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='users',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['fast_prop', 'container-AA9NXV'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.fit(container.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Checking... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">The pipeline check generated <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</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;36m2\u001b[0m issues labeled INFO and \u001b[1;36m0\u001b[0m issues labeled WARNING.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">type</th>\n",
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       "      \n",
       "        \n",
       "          <th class=\"str\">label                 </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">message                         </th>\n",
       "        \n",
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       "    </tr>\n",
       "    \n",
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       "    \n",
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       "        <th>0</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 U2BASE__STAGING_TABLE_4 and MOVIES2DIRECTORS__STAGING_TABLE_3 over &#x27;movieid&#x27; and &#x27;movieid&#x27;, there are no corresponding entries for 0.159513% of entries in &#x27;movieid&#x27; in &#x27;U2BASE__STAGING_TABLE_4&#x27;. You might want to double-check your join keys.</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 U2BASE__STAGING_TABLE_4 and MOVIES2ACTORS__STAGING_TABLE_2 over &#x27;movieid&#x27; and &#x27;movieid&#x27;, there are no corresponding entries for 0.336492% of entries in &#x27;movieid&#x27; in &#x27;U2BASE__STAGING_TABLE_4&#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   FOREIGN KEYS NOT FOUND   When joining U2BASE__STAGING_TAB...\n",
       "1   INFO   FOREIGN KEYS NOT FOUND   When joining U2BASE__STAGING_TAB..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.check(container.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Checking data model<span style=\"color: #808000; text-decoration-color: #808000\">...</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Checking data model\u001b[33m...\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K  Staging... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[2K  Preprocessing... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?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\">2</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;36m2\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: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:04\n",
      "\u001b[2K  Relboost: Training features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 11:48\n",
      "\u001b[2K  FastProp: Building subfeatures... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:05\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 06:18\n",
      "\u001b[2K  XGBoost: Training as predictor... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:03\n",
      "\u001b[?25h"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Trained pipeline.\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Trained pipeline.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken: 0:18:19.843564.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='users',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost', 'container-AA9NXV'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='users',\n",
       "         feature_learners=['Relboost'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='CrossEntropyLoss',\n",
       "         peripheral=['actors', 'directors', 'movies', 'movies2actors', 'movies2directors',\n",
       "                     'u2base'],\n",
       "         predictors=['XGBoostClassifier'],\n",
       "         preprocessors=['Mapping'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['relboost', 'container-AA9NXV'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.fit(container.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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% • 00:19\n",
      "\u001b[2K  FastProp: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 00:00\n",
      "\u001b[?25h"
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    },
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       "        \n",
       "          <th class=\"str\">set used</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">target</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">accuracy</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">    auc</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"float\">cross entropy</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 15:15:36</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">target</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9139</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9686</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.2788</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 15:34:18</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">target</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.775</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.7859</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.4779</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 15:15:36   train      target     0.9139    0.9686          0.2788\n",
       "1   2024-09-12 15:34:18   test       target     0.775     0.7859          0.4779"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fastprop_score = pipe1.score(container.test)\n",
    "fastprop_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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% • 00:04\n",
      "\u001b[2K  Relboost: Building features... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% • 02:01\n",
      "\u001b[?25h"
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       "        \n",
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       "        \n",
       "          <th class=\"float\">    auc</th>\n",
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       "      \n",
       "        \n",
       "          <th class=\"float\">cross entropy</th>\n",
       "        \n",
       "      \n",
       "    </tr>\n",
       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 15:33:57</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">train</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">target</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9755</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9962</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.1488</td>\n",
       "            \n",
       "          \n",
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       "    \n",
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       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"datetime\">2024-09-12 15:36:24</td>\n",
       "            \n",
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       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">target</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.8048</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.8377</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.4429</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 15:33:57   train      target     0.9755    0.9962          0.1488\n",
       "1   2024-09-12 15:36:24   test       target     0.8048    0.8377          0.4429"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relboost_score = pipe2.score(container.test)\n",
    "relboost_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6 Studying features"
   ]
  },
  {
   "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.\n",
    "\n",
    "As we can see, most of the predictive accuracy is drawn from the roles played by the actors. This suggests that the text fields contained in this relational database have a higher impact on predictive accuracy than for most other data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe1.columns.importances()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances)\n",
    "\n",
    "plt.title(\"Columns importances\")\n",
    "plt.xlabel(\"Columns\")\n",
    "plt.ylabel(\"Importances\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe2.columns.importances()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances)\n",
    "\n",
    "plt.title(\"Columns importances\")\n",
    "plt.xlabel(\"Columns\")\n",
    "plt.ylabel(\"Importances\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.7 Features\n",
    "\n",
    "The most important features look as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_155\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_155\" AS\n",
       "SELECT MEDIAN( COALESCE( f_1_1_76.\"feature_1_1_76\", 0.0 ) ) AS \"feature_1_155\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"USERS__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\n",
       "ON t1.\"userid\" = t2.\"userid\"\n",
       "LEFT JOIN \"FEATURE_1_1_76\" f_1_1_76\n",
       "ON t2.rowid = f_1_1_76.rownum\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_155\";\\n\\nCREATE TABLE \"FEATURE_1_155\" AS\\nSELECT MEDIAN( COALESCE( f_1_1_76.\"feature_1_1_76\", 0.0 ) ) AS \"feature_1_155\",\\n       t1.rowid AS rownum\\nFROM \"USERS__STAGING_TABLE_1\" t1\\nINNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\\nON t1.\"userid\" = t2.\"userid\"\\nLEFT JOIN \"FEATURE_1_1_76\" f_1_1_76\\nON t2.rowid = f_1_1_76.rownum\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe1.features.to_sql()[pipe1.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_1\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_1\" AS\n",
       "SELECT AVG( \n",
       "    CASE\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" > 0.007531 ) AND ( t2.\"t3__year\" > 3.000000 ) THEN -14.47282772262984\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" > 0.007531 ) AND ( t2.\"t3__year\" <= 3.000000 OR t2.\"t3__year\" IS NULL ) THEN 18.80608436507123\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" <= 0.007531 OR p_1_1.\"feature_1_1_91\" IS NULL ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" > 0.250270 ) THEN 17.15219491452244\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" <= 0.007531 OR p_1_1.\"feature_1_1_91\" IS NULL ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" <= 0.250270 OR t2.\"t3__year__mapping_1_target_1_avg\" IS NULL ) THEN 7.625903043390911\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" > 0.251831 ) AND ( p_1_1.\"feature_1_1_58\" > 2.309180 ) THEN -9.39027739930436\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" > 0.251831 ) AND ( p_1_1.\"feature_1_1_58\" <= 2.309180 OR p_1_1.\"feature_1_1_58\" IS NULL ) THEN -3.045979108396568\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" <= 0.251831 OR p_1_1.\"feature_1_1_40\" IS NULL ) AND ( p_1_1.\"feature_1_1_73\" > 0.009437 ) THEN 0.007265281402375446\n",
       "        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" <= 0.251831 OR p_1_1.\"feature_1_1_40\" IS NULL ) AND ( p_1_1.\"feature_1_1_73\" <= 0.009437 OR p_1_1.\"feature_1_1_73\" IS NULL ) THEN -3.672817201353017\n",
       "        ELSE NULL\n",
       "    END\n",
       ") AS \"feature_1_1\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"USERS__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\n",
       "ON t1.\"userid\" = t2.\"userid\"\n",
       "LEFT JOIN \"FEATURES_1_1_PROPOSITIONALIZATION\" p_1_1\n",
       "ON t2.rowid = p_1_1.\"rownum\"\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_1\";\\n\\nCREATE TABLE \"FEATURE_1_1\" AS\\nSELECT AVG( \\n    CASE\\n        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" > 0.007531 ) AND ( t2.\"t3__year\" > 3.000000 ) THEN -14.47282772262984\\n        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" > 0.007531 ) AND ( t2.\"t3__year\" <= 3.000000 OR t2.\"t3__year\" IS NULL ) THEN 18.80608436507123\\n        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" <= 0.007531 OR p_1_1.\"feature_1_1_91\" IS NULL ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" > 0.250270 ) THEN 17.15219491452244\\n        WHEN ( p_1_1.\"feature_1_1_76\" > 0.241788 ) AND ( p_1_1.\"feature_1_1_91\" <= 0.007531 OR p_1_1.\"feature_1_1_91\" IS NULL ) AND ( t2.\"t3__year__mapping_1_target_1_avg\" <= 0.250270 OR t2.\"t3__year__mapping_1_target_1_avg\" IS NULL ) THEN 7.625903043390911\\n        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" > 0.251831 ) AND ( p_1_1.\"feature_1_1_58\" > 2.309180 ) THEN -9.39027739930436\\n        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" > 0.251831 ) AND ( p_1_1.\"feature_1_1_58\" <= 2.309180 OR p_1_1.\"feature_1_1_58\" IS NULL ) THEN -3.045979108396568\\n        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" <= 0.251831 OR p_1_1.\"feature_1_1_40\" IS NULL ) AND ( p_1_1.\"feature_1_1_73\" > 0.009437 ) THEN 0.007265281402375446\\n        WHEN ( p_1_1.\"feature_1_1_76\" <= 0.241788 OR p_1_1.\"feature_1_1_76\" IS NULL ) AND ( p_1_1.\"feature_1_1_40\" <= 0.251831 OR p_1_1.\"feature_1_1_40\" IS NULL ) AND ( p_1_1.\"feature_1_1_73\" <= 0.009437 OR p_1_1.\"feature_1_1_73\" IS NULL ) THEN -3.672817201353017\\n        ELSE NULL\\n    END\\n) AS \"feature_1_1\",\\n       t1.rowid AS rownum\\nFROM \"USERS__STAGING_TABLE_1\" t1\\nINNER JOIN \"U2BASE__STAGING_TABLE_4\" t2\\nON t1.\"userid\" = t2.\"userid\"\\nLEFT JOIN \"FEATURES_1_1_PROPOSITIONALIZATION\" p_1_1\\nON t2.rowid = p_1_1.\"rownum\"\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.features.to_sql()[pipe2.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.8 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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a folder named movie_lens_pipeline containing\n",
    "# the SQL code.\n",
    "pipe2.features.to_sql().save(\"movie_lens_pipeline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe2.features.to_sql(dialect=getml.pipeline.dialect.spark_sql).save(\"movie_lens_spark\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.9 Benchmarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "State-of-the-art approaches on this dataset perform as follows:\n",
    "\n",
    "\n",
    "| Approach                          | Study                       | Accuracy     | AUC     |\n",
    "| :------------------------------   | :-------------------------  | -----------: | ------: |\n",
    "| Probabalistic Relational Model    | Ghanem (2009)               | --           |  69.2%  |\n",
    "| Multi-Relational Bayesian Network | Schulte and Khosravi (2012) | 69%          |  --     |\n",
    "| Multi-Relational Bayesian Network | Schulte et al (2013)        | 66%          |  --     |\n",
    "\n",
    "\n",
    "By contrast, getML's algorithms, as used in this notebook, perform as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Approach</th>\n",
       "      <th>Accuracy</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FastProp</td>\n",
       "      <td>77.5%</td>\n",
       "      <td>78.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Relboost</td>\n",
       "      <td>80.5%</td>\n",
       "      <td>83.8%</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Approach Accuracy    AUC\n",
       "0  FastProp    77.5%  78.6%\n",
       "1  Relboost    80.5%  83.8%"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = [fastprop_score, relboost_score]\n",
    "pd.DataFrame(\n",
    "    data={\n",
    "        \"Approach\": [\"FastProp\", \"Relboost\"],\n",
    "        \"Accuracy\": [f\"{score.accuracy:.1%}\" for score in scores],\n",
    "        \"AUC\": [f\"{score.auc:,.1%}\" for score in scores],\n",
    "    }\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 how getML can be applied to the MovieLens dataset. We have demonstrated the our  approach outperforms state-of-the-art relational learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Motl, Jan, and Oliver Schulte. \"The CTU prague relational learning repository.\" arXiv preprint arXiv:1511.03086 (2015).\n",
    "\n",
    "Ghanem, Amal S. \"Probabilistic models for mining imbalanced relational data.\" Doctoral dissertation, Curtin University (2009).\n",
    "\n",
    "Schulte, Oliver, and Hassan Khosravi. \"Learning graphical models for relational data via lattice search.\" Machine Learning 88.3 (2012): 331-368.\n",
    "\n",
    "Schulte, Oliver, et al. \"A hierarchy of independence assumptions for multi-relational Bayes net classifiers.\" 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2013.\n"
   ]
  }
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