Learning Equality.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88080462",
   "metadata": {},
   "source": [
    "# Kaggle Challenge - Learning Equality\n",
    "https://www.kaggle.com/competitions/learning-equality-curriculum-recommendations/overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e44162a6",
   "metadata": {},
   "source": [
    "## Goal of the Competition\n",
    "\n",
    "The National Football League (NFL) is back with another Big Data Bowl, where contestants use Next Gen Stats player tracking data to generate actionable, creative, and novel stats. Previous iterations have considered running backs, defensive backs, and special teams, and have generated metrics that have been used on television and by NFL teams. In this year’s competition, you’ll have more subtle performances to consider—and potentially more players to measure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d827eb0b",
   "metadata": {},
   "source": [
    "## Submission File\n",
    "\n",
    "For each **topic_id** in the test set, you must predict a space-delimited list of recommended **content_ids** for that topic. The file should contain a header and have the following format:\n",
    "\n",
    "~~~\n",
    "topic_id,content_ids\n",
    "t_00004da3a1b2,c_1108dd0c7a5d c_376c5a8eb028 c_5bc0e1e2cba0 c_76231f9d0b5e\n",
    "t_00068291e9a4,c_639ea2ef9c95 c_89ce9367be10 c_ac1672cdcd2c c_ebb7fdf10a7e\n",
    "t_00069b63a70a,c_11a1dc0bfb99\n",
    "...\n",
    "~~~"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5f7ef4",
   "metadata": {},
   "source": [
    "## Scoring\n",
    "Mean F2 score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86e3e62",
   "metadata": {},
   "source": [
    "# Evaluation Metric - Efficiency Scoring\n",
    "We compute a submission's efficiency score by:\n",
    "\n",
    "\\begin{equation} \\text{Efficiency} = \\frac{1}{ \\text{Benchmark} - \\max\\text{F2} }\\text{F2} + \\frac{1}{32400}\\text{RuntimeSeconds} \\end{equation}\n",
    "\n",
    "\n",
    "where **F2** is the submission's score on the main competition metric, **Benchmark** is the score of the benchmark sample_submission.csv, **maxF2** is the maximum  of all submissions on the Private Leaderboard, and **RuntimeSeconds** is the number of seconds it takes for the submission to be evaluated. The objective is to minimize the efficiency score.\n",
    "\n",
    "During the training period of the competition, you may see a leaderboard for the public test data in the following notebook, updated daily: Efficiency Leaderboard. After the competition ends, we will update this leaderboard with efficiency scores on the private data. During the training period, this leaderboard will show only the rank of each team, but not the complete score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cc9cb4",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb712db6",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5f3a3b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import fbeta_score\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.multioutput import MultiOutputClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a0c2d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download files with API\n",
    "#!kaggle competitions download -c learning-equality-curriculum-recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55f4587",
   "metadata": {},
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "debc1002",
   "metadata": {},
   "outputs": [
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       "                   id                                              title  \\\n",
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       "...               ...                                                ...   \n",
       "76967  t_fffb0bf2801d                             4.3 Graph of functions   \n",
       "76968  t_fffbe1d5d43c                   Inscribed shapes problem solving   \n",
       "76969  t_fffe14f1be1e                                          Lección 7   \n",
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       "3                                                         6e3ba4   source   \n",
       "4      Научи повече за графиките на сложните показате...  000cf7   source   \n",
       "...                                                  ...     ...      ...   \n",
       "76967                                                     e77b55  aligned   \n",
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       "76969                                                     6e90a7  aligned   \n",
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       "76971                                                     c7ca13  aligned   \n",
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   ],
   "source": [
    "# load the data into pandas dataframes\n",
    "df_topics = pd.read_csv(\"topics.csv\").fillna({\"title\": \"\", \"description\": \"\"})\n",
    "df_content = pd.read_csv(\"content.csv\").fillna(\"\")\n",
    "df_corr = pd.read_csv(\"correlations.csv\")\n",
    "df_topics"
   ]
  },
  {
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   "execution_count": 5,
   "id": "f29e0027",
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   "outputs": [
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       "                    id                                             title  \\\n",
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       "1       c_000087304a9e                    Trovare i fattori di un numero   \n",
       "2       c_0000ad142ddb                           Sumar curvas de demanda   \n",
       "3       c_0000c03adc8d                               Nado de aproximação   \n",
       "4       c_00016694ea2a                  geometry-m3-topic-a-overview.pdf   \n",
       "...                ...                                               ...   \n",
       "154042  c_fffcbdd4de8b                                  2. 12: Diffusion   \n",
       "154043  c_fffe15a2d069                      Sommare facendo gruppi da 10   \n",
       "154044  c_fffed7b0d13a                            Introdução à subtração   \n",
       "154045  c_ffff04ba7ac7                                      SA of a Cone   \n",
       "154046  c_ffffe5254266                                          The Jats   \n",
       "\n",
       "                                              description      kind  \\\n",
       "0       Suma 48,029+233,930 mediante el algoritmo está...     video   \n",
       "1                         Sal trova i fattori di 120.\\n\\n     video   \n",
       "2                       Cómo añadir curvas de demanda\\n\\n     video   \n",
       "3       Neste vídeo você vai aprender o nado de aproxi...  document   \n",
       "4                        geometry-m3-topic-a-overview.pdf  document   \n",
       "...                                                   ...       ...   \n",
       "154042                                                        html5   \n",
       "154043  Sal somma 5+68 spezzando il 5 in un 2 e un 3.\\n\\n     video   \n",
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       "             topic_id     content_ids\n",
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    "y_train"
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   "id": "667f0aef",
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       "      <th>279917</th>\n",
       "      <td>Lección 7</td>\n",
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       "      <td>6e90a7</td>\n",
       "      <td>aligned</td>\n",
       "      <td>6</td>\n",
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       "      <td>t_d448c707984d</td>\n",
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       "      <td></td>\n",
       "      <td>document</td>\n",
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       "      <th>279918</th>\n",
       "      <td>تحديد العلاقة بين الإحداثيّات القطبية والإحداث...</td>\n",
       "      <td>5b9e5ca86571f90499ea987f</td>\n",
       "      <td>9fd860</td>\n",
       "      <td>source</td>\n",
       "      <td>2</td>\n",
       "      <td>ar</td>\n",
       "      <td>t_5b4f3ba4eb7d</td>\n",
       "      <td>True</td>\n",
       "      <td>التقييم</td>\n",
       "      <td>5b0a30e7b42e8704a45b2bfd</td>\n",
       "      <td>exercise</td>\n",
       "      <td>كم سيكون حاصل الجداء السلمي لشعاعين متوازيين ؟...</td>\n",
       "      <td>ar</td>\n",
       "      <td>Edraak</td>\n",
       "      <td>CC BY-NC-SA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279919 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              title_topic  \\\n",
       "0                              Откриването на резисторите   \n",
       "1                              Откриването на резисторите   \n",
       "2                              Откриването на резисторите   \n",
       "3                              Откриването на резисторите   \n",
       "4                         Entradas e saídas de uma função   \n",
       "...                                                   ...   \n",
       "279914                              NA_U06 - El periódico   \n",
       "279915                   Inscribed shapes problem solving   \n",
       "279916                   Inscribed shapes problem solving   \n",
       "279917                                          Lección 7   \n",
       "279918  تحديد العلاقة بين الإحداثيّات القطبية والإحداث...   \n",
       "\n",
       "                                        description_topic channel  \\\n",
       "0       Изследване на материали, които предизвикват на...  000cf7   \n",
       "1       Изследване на материали, които предизвикват на...  000cf7   \n",
       "2       Изследване на материали, които предизвикват на...  000cf7   \n",
       "3       Изследване на материали, които предизвикват на...  000cf7   \n",
       "4                    Entenda um pouco mais sobre funções.  8e286a   \n",
       "...                                                   ...     ...   \n",
       "279914                                                     71fd51   \n",
       "279915  Use properties of inscribed angles to prove pr...  0c929f   \n",
       "279916  Use properties of inscribed angles to prove pr...  0c929f   \n",
       "279917                                                     6e90a7   \n",
       "279918                           5b9e5ca86571f90499ea987f  9fd860   \n",
       "\n",
       "            category  level language_x          parent  has_content  \\\n",
       "0             source      4         bg  t_16e29365b50d         True   \n",
       "1             source      4         bg  t_16e29365b50d         True   \n",
       "2             source      4         bg  t_16e29365b50d         True   \n",
       "3             source      4         bg  t_16e29365b50d         True   \n",
       "4             source      4         pt  t_d14b6c2a2b70         True   \n",
       "...              ...    ...        ...             ...          ...   \n",
       "279914  supplemental      2         es  t_5bd8f6ae9f7d         True   \n",
       "279915        source      4         sw  t_50145b9bab3f         True   \n",
       "279916        source      4         sw  t_50145b9bab3f         True   \n",
       "279917       aligned      6         es  t_d448c707984d         True   \n",
       "279918        source      2         ar  t_5b4f3ba4eb7d         True   \n",
       "\n",
       "                                            title_content  \\\n",
       "0                                     Молив като резистор   \n",
       "1                      Да чуем променливото съпротивление   \n",
       "2          Променлив резистор (реостат) с графит от молив   \n",
       "3       Последователно свързване на галваничен елемент...   \n",
       "4                 Dados e resultados de funções: gráficos   \n",
       "...                                                   ...   \n",
       "279914                         Introducción: El periódico   \n",
       "279915  Proof: Right triangles inscribed in circles -d...   \n",
       "279916  Area of inscribed equilateral triangle -dubbed...   \n",
       "279917                             Juego con las palabras   \n",
       "279918                                            التقييم   \n",
       "\n",
       "                                      description_content      kind  \\\n",
       "0       Моливът причинява промяна в отклонението, подо...     video   \n",
       "1       Тук чертаем линия на лист хартия и я използвам...     video   \n",
       "2       Използваме сърцевината на молива (неговия граф...     video   \n",
       "3       Защо отклонението се променя, когато се свърже...     video   \n",
       "4       Encontre todas as entradas que correspondem a ...  exercise   \n",
       "...                                                   ...       ...   \n",
       "279914                                                        html5   \n",
       "279915  Proof showing that a triangle inscribed in a c...     video   \n",
       "279916  A worked example of finding the area of an equ...     video   \n",
       "279917                                                     document   \n",
       "279918                           5b0a30e7b42e8704a45b2bfd  exercise   \n",
       "\n",
       "                                                     text language_y  \\\n",
       "0                                                                 bg   \n",
       "1                                                                 bg   \n",
       "2                                                                 bg   \n",
       "3                                                                 bg   \n",
       "4                                                                 pt   \n",
       "...                                                   ...        ...   \n",
       "279914                                                            es   \n",
       "279915                                                            sw   \n",
       "279916                                                            sw   \n",
       "279917                                                            es   \n",
       "279918  كم سيكون حاصل الجداء السلمي لشعاعين متوازيين ؟...         ar   \n",
       "\n",
       "       copyright_holder      license  \n",
       "0                                     \n",
       "1                                     \n",
       "2                                     \n",
       "3                                     \n",
       "4                                     \n",
       "...                 ...          ...  \n",
       "279914                                \n",
       "279915                                \n",
       "279916                                \n",
       "279917                                \n",
       "279918           Edraak  CC BY-NC-SA  \n",
       "\n",
       "[279919 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X = y_train\n",
    "df_X = df_X.merge (df_topics.rename(columns={'id': 'topic_id'}), on='topic_id', how='left')\n",
    "df_X = df_X.merge (df_content.rename(columns={'id': 'content_ids'}), on='content_ids', how='left')\n",
    "df_X.rename(columns={'title_x': 'title_topic', 'title_y': 'title_content', 'description_x': 'description_topic', 'description_y': 'description_content'}, inplace = True)\n",
    "df_X\n",
    "X_train = df_X.drop(columns = ['topic_id', 'content_ids'])\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0866f1f5",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38b43db",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c995e2",
   "metadata": {},
   "source": [
    "## Baseline Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81ddfac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = DummyClassifier (strategy = 'most_frequent')\n",
    "multi_target_dummy = MultiOutputClassifier(dummy, n_jobs=2)\n",
    "multi_target_dummy.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72ed0a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_target_dummy.predict (X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7cd4d95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the f2-score\n",
    "y_train = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]\n",
    "y_pred = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
    "F2 = fbeta_score(y_train, y_pred, beta=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4319e1d9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_topic</th>\n",
       "      <th>description_topic</th>\n",
       "      <th>channel</th>\n",
       "      <th>category</th>\n",
       "      <th>level</th>\n",
       "      <th>language_x</th>\n",
       "      <th>parent</th>\n",
       "      <th>has_content</th>\n",
       "      <th>title_content</th>\n",
       "      <th>description_content</th>\n",
       "      <th>kind</th>\n",
       "      <th>text</th>\n",
       "      <th>language_y</th>\n",
       "      <th>copyright_holder</th>\n",
       "      <th>license</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12. 20: Bird Reproduction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ebc86c</td>\n",
       "      <td>supplemental</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>t_c44ac9711007</td>\n",
       "      <td>True</td>\n",
       "      <td>12. 20: Bird Reproduction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>html5</td>\n",
       "      <td>Is this pair of birds actually a “couple”?\\n\\n...</td>\n",
       "      <td>en</td>\n",
       "      <td>CSU and Merlot</td>\n",
       "      <td>CC BY-NC-SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12. 20: Bird Reproduction</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ebc86c</td>\n",
       "      <td>supplemental</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>t_c44ac9711007</td>\n",
       "      <td>True</td>\n",
       "      <td>Astounding Mating Dance Birds of Paradise -- H...</td>\n",
       "      <td>The Birds of Paradise from BBC's outstanding P...</td>\n",
       "      <td>video</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.1.2 - Logarithms</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e77b55</td>\n",
       "      <td>aligned</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>t_b897d168db90</td>\n",
       "      <td>True</td>\n",
       "      <td>Proof of the logarithm change of base rule</td>\n",
       "      <td>Sal proves the logarithmic change of base rule...</td>\n",
       "      <td>video</td>\n",
       "      <td>What I want to do in this video is prove the c...</td>\n",
       "      <td>en</td>\n",
       "      <td>Khan Academy</td>\n",
       "      <td>CC BY-NC-SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.1.2 - Logarithms</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e77b55</td>\n",
       "      <td>aligned</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>t_b897d168db90</td>\n",
       "      <td>True</td>\n",
       "      <td>Intro to logarithm properties (2 of 2)</td>\n",
       "      <td>Sal introduces the logarithm identities for mu...</td>\n",
       "      <td>video</td>\n",
       "      <td>PROFESSOR: Welcome back. I'm going to show you...</td>\n",
       "      <td>en</td>\n",
       "      <td>Khan Academy</td>\n",
       "      <td>CC BY-NC-SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.1.2 - Logarithms</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e77b55</td>\n",
       "      <td>aligned</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>t_b897d168db90</td>\n",
       "      <td>True</td>\n",
       "      <td>Solve exponential equations using logarithms: ...</td>\n",
       "      <td>Solve exponential equations that have 2 or oth...</td>\n",
       "      <td>exercise</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  title_topic description_topic channel      category  level  \\\n",
       "17  12. 20: Bird Reproduction               NaN  ebc86c  supplemental      5   \n",
       "18  12. 20: Bird Reproduction               NaN  ebc86c  supplemental      5   \n",
       "19         2.1.2 - Logarithms               NaN  e77b55       aligned      5   \n",
       "20         2.1.2 - Logarithms               NaN  e77b55       aligned      5   \n",
       "21         2.1.2 - Logarithms               NaN  e77b55       aligned      5   \n",
       "\n",
       "   language_x          parent  has_content  \\\n",
       "17         en  t_c44ac9711007         True   \n",
       "18         en  t_c44ac9711007         True   \n",
       "19         en  t_b897d168db90         True   \n",
       "20         en  t_b897d168db90         True   \n",
       "21         en  t_b897d168db90         True   \n",
       "\n",
       "                                        title_content  \\\n",
       "17                          12. 20: Bird Reproduction   \n",
       "18  Astounding Mating Dance Birds of Paradise -- H...   \n",
       "19         Proof of the logarithm change of base rule   \n",
       "20             Intro to logarithm properties (2 of 2)   \n",
       "21  Solve exponential equations using logarithms: ...   \n",
       "\n",
       "                                  description_content      kind  \\\n",
       "17                                                NaN     html5   \n",
       "18  The Birds of Paradise from BBC's outstanding P...     video   \n",
       "19  Sal proves the logarithmic change of base rule...     video   \n",
       "20  Sal introduces the logarithm identities for mu...     video   \n",
       "21  Solve exponential equations that have 2 or oth...  exercise   \n",
       "\n",
       "                                                 text language_y  \\\n",
       "17  Is this pair of birds actually a “couple”?\\n\\n...         en   \n",
       "18                                                NaN         en   \n",
       "19  What I want to do in this video is prove the c...         en   \n",
       "20  PROFESSOR: Welcome back. I'm going to show you...         en   \n",
       "21                                                NaN         en   \n",
       "\n",
       "   copyright_holder      license  \n",
       "17   CSU and Merlot  CC BY-NC-SA  \n",
       "18              NaN          NaN  \n",
       "19     Khan Academy  CC BY-NC-SA  \n",
       "20     Khan Academy  CC BY-NC-SA  \n",
       "21              NaN          NaN  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ex = X_train[17:22]\n",
    "y_ex = y_train[17:22]\n",
    "X_ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2cec2688",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '12. 20: Bird Reproduction'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31m_RemoteTraceback\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 436, in _process_worker\n    r = call_item()\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py\", line 288, in __call__\n    return self.fn(*self.args, **self.kwargs)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/_parallel_backends.py\", line 595, in __call__\n    return self.func(*args, **kwargs)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/parallel.py\", line 263, in __call__\n    return [func(*args, **kwargs)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/parallel.py\", line 263, in <listcomp>\n    return [func(*args, **kwargs)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/utils/fixes.py\", line 117, in __call__\n    return self.function(*args, **kwargs)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/multioutput.py\", line 44, in _fit_estimator\n    estimator.fit(X, y, **fit_params)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/ensemble/_forest.py\", line 331, in fit\n    X, y = self._validate_data(\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/base.py\", line 596, in _validate_data\n    X, y = check_X_y(X, y, **check_params)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/utils/validation.py\", line 1074, in check_X_y\n    X = check_array(\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/utils/validation.py\", line 791, in check_array\n    array = array.astype(new_dtype)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/pandas/core/generic.py\", line 6240, in astype\n    new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/pandas/core/internals/managers.py\", line 450, in astype\n    return self.apply(\"astype\", dtype=dtype, copy=copy, errors=errors)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/pandas/core/internals/managers.py\", line 352, in apply\n    applied = getattr(b, f)(**kwargs)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/pandas/core/internals/blocks.py\", line 526, in astype\n    new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/pandas/core/dtypes/astype.py\", line 299, in astype_array_safe\n    new_values = astype_array(values, dtype, copy=copy)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/pandas/core/dtypes/astype.py\", line 230, in astype_array\n    values = astype_nansafe(values, dtype, copy=copy)\n  File \"/root/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/pandas/core/dtypes/astype.py\", line 170, in astype_nansafe\n    return arr.astype(dtype, copy=True)\nValueError: could not convert string to float: '12. 20: Bird Reproduction'\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [75], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m model \u001b[38;5;241m=\u001b[39m RandomForestClassifier(random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      5\u001b[0m multi_target_model \u001b[38;5;241m=\u001b[39m MultiOutputClassifier(model, n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m multi_target_model\u001b[38;5;241m.\u001b[39mfit(X_ex, y_ex)\n\u001b[1;32m      7\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m multi_target_model\u001b[38;5;241m.\u001b[39mpredict(X_ex)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/multioutput.py:435\u001b[0m, in \u001b[0;36mMultiOutputClassifier.fit\u001b[0;34m(self, X, Y, sample_weight, **fit_params)\u001b[0m\n\u001b[1;32m    409\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, Y, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params):\n\u001b[1;32m    410\u001b[0m     \u001b[38;5;124;03m\"\"\"Fit the model to data matrix X and targets Y.\u001b[39;00m\n\u001b[1;32m    411\u001b[0m \n\u001b[1;32m    412\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;124;03m        Returns a fitted instance.\u001b[39;00m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 435\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_ \u001b[38;5;241m=\u001b[39m [estimator\u001b[38;5;241m.\u001b[39mclasses_ \u001b[38;5;28;01mfor\u001b[39;00m estimator \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_]\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/sklearn/multioutput.py:202\u001b[0m, in \u001b[0;36m_MultiOutputEstimator.fit\u001b[0;34m(self, X, y, sample_weight, **fit_params)\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnderlying estimator does not support sample weights.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    200\u001b[0m fit_params_validated \u001b[38;5;241m=\u001b[39m _check_fit_params(X, fit_params)\n\u001b[0;32m--> 202\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_ \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params_validated\u001b[49m\n\u001b[1;32m    205\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    207\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_features_in_\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    210\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mn_features_in_\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/parallel.py:1061\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1058\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m   1060\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1061\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mretrieve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1062\u001b[0m \u001b[38;5;66;03m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m   1063\u001b[0m elapsed_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_start_time\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/parallel.py:938\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    936\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    937\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msupports_timeout\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 938\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output\u001b[38;5;241m.\u001b[39mextend(\u001b[43mjob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    939\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    940\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output\u001b[38;5;241m.\u001b[39mextend(job\u001b[38;5;241m.\u001b[39mget())\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/envs/lewagon/lib/python3.10/site-packages/joblib/_parallel_backends.py:542\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[38;5;124;03m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m    540\u001b[0m \u001b[38;5;124;03mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m    541\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 542\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m CfTimeoutError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    544\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/concurrent/futures/_base.py:458\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m    457\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 458\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    459\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    460\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.10.6/lib/python3.10/concurrent/futures/_base.py:403\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 403\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    404\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    405\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m    406\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '12. 20: Bird Reproduction'"
     ]
    }
   ],
   "source": [
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "model = RandomForestClassifier(random_state=1)\n",
    "multi_target_model = MultiOutputClassifier(model, n_jobs=-1)\n",
    "multi_target_model.fit(X_ex, y_ex)\n",
    "y_pred = multi_target_model.predict(X_ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c468147",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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