TabNet_Premier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
    "\n",
    "import os\n",
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = \"Data/Premier\"\n",
    "años = [\"19-20\", \"20-21\", \"21-22\", \"22-23\", \"23-24\"]\n",
    "torneos = []\n",
    "\n",
    "for año in años:\n",
    "    match_file = f\"stats_{año}.csv\"\n",
    "    path_matches = os.path.join(folder, match_file)\n",
    "    matches = pd.read_csv(path_matches)\n",
    "    torneos.append(matches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se borran los partidos de cada torneo en los que no hay registro de los equipos que jugaron. y el ELO que no se utiliza (Peores resultados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for torneo in torneos:\n",
    "    torneo.dropna(subset=['team_home', 'team_away'], inplace=True)\n",
    "    #drop columna ELO\n",
    "    torneo.drop(columns=['ELO_home', 'ELO_away'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora, se eliminan las columnas que tengan datos faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_with_null = []\n",
    "\n",
    "for torneo in torneos:\n",
    "    null_columns = torneo.columns[torneo.isna().any()].tolist()\n",
    "    columns_with_null.append(null_columns)\n",
    "\n",
    "columns_with_null\n",
    "\n",
    "unique_columns = list(set(itertools.chain.from_iterable(columns_with_null)))\n",
    "unique_columns\n",
    "\n",
    "#Borrar para cada torneo las columnas en unique_columns\n",
    "for torneo in torneos:\n",
    "    torneo.drop(columns=unique_columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "torneos2 = []\n",
    "\n",
    "for torneo in torneos:\n",
    "    torneo.sort_values(by='number_of_match', inplace=True)\n",
    "    torneo.rename(columns={'home_score': 'score_home', 'away_score': 'score_away'}, inplace=True)\n",
    "\n",
    "    ##OJOO arreglar tema de equipos\n",
    "    team_counts = pd.concat([torneo['team_home'], torneo['team_away']]).value_counts()\n",
    "    teams_to_remove = team_counts[team_counts < 5].index\n",
    "\n",
    "    torneo = torneo[~(torneo['team_home'].isin(teams_to_remove) | torneo['team_away'].isin(teams_to_remove))]\n",
    "    torneos2.append(torneo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(378, 49)\n",
      "(380, 49)\n",
      "(374, 49)\n",
      "(380, 49)\n",
      "(132, 49)\n"
     ]
    }
   ],
   "source": [
    "torneos = torneos2\n",
    "torneos[0].head()\n",
    "\n",
    "#Cantidad de datos en cada torneo\n",
    "for torneo in torneos:\n",
    "    print(torneo.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se generan las tablas para cada temporada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['score_prom', 'posession', 'total_shots', 'shots_on_target', 'shots_off_target', 'blocked_shots', 'corner_kicks', 'fouls', 'red_cards', 'free_kicks', 'goal_kicks', 'shots_inside_box', 'shots_outside_box', 'passes', 'accurate_passes', 'long_balls', 'crosses', 'dribbles', 'duels_won', 'aerials_won', 'tackles', 'interceptions', 'last_season_position']\n",
      "23\n"
     ]
    }
   ],
   "source": [
    "def remove_suffix(item):\n",
    "    return item.replace(\"_home\", \"\").replace(\"_away\", \"\")\n",
    "\n",
    "column = [remove_suffix(item) for item in matches.columns]\n",
    "columnas = []\n",
    "[columnas.append(x) for x in column if x not in columnas]\n",
    "columnas.remove(\"number_of_match\")\n",
    "columnas.remove(\"team\")\n",
    "columnas[0] = \"score_prom\"\n",
    "print(columnas)\n",
    "print(len(columnas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnas.append(\"wins\")\n",
    "columnas.append(\"ties\")\n",
    "columnas.append(\"losses\")\n",
    "columnas.append(\"total_goals\")\n",
    "columnas.append(\"total_goals_conceded\")\n",
    "columnas.append(\"points\")\n",
    "columnas.append(\"streak\")\n",
    "columnas.append(\"wins_in_last_5_matches\")\n",
    "columnas.append(\"ties_in_last_5_matches\")\n",
    "columnas.append(\"losses_in_last_5_matches\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 20\n",
      "['Liverpool' 'West Ham' 'Bournemouth' 'Burnley' 'Crystal Palace' 'Watford'\n",
      " 'Tottenham' 'Newcastle United' 'Manchester United' 'Arsenal'\n",
      " 'Aston Villa' 'Brighton' 'Everton' 'Norwich City' 'Southampton'\n",
      " 'Manchester City' 'Sheffield United' 'Chelsea' 'Wolverhampton'\n",
      " 'Leicester City']\n",
      "20 20\n",
      "['Fulham' 'Crystal Palace' 'Liverpool' 'West Ham' 'West Bromwich Albion'\n",
      " 'Tottenham' 'Sheffield United' 'Brighton' 'Burnley' 'Manchester City'\n",
      " 'Everton' 'Leeds United' 'Manchester United' 'Arsenal' 'Southampton'\n",
      " 'Newcastle United' 'Chelsea' 'Leicester City' 'Aston Villa'\n",
      " 'Wolverhampton']\n",
      "20 20\n",
      "['Brentford' 'Manchester United' 'Burnley' 'Chelsea' 'Everton'\n",
      " 'Leicester City' 'Watford' 'Norwich City' 'Newcastle United' 'Tottenham'\n",
      " 'Liverpool' 'Aston Villa' 'Crystal Palace' 'Leeds United'\n",
      " 'Manchester City' 'Brighton' 'Southampton' 'Wolverhampton' 'Arsenal'\n",
      " 'West Ham']\n",
      "20 20\n",
      "['Crystal Palace' 'Fulham' 'Bournemouth' 'Leeds United' 'Newcastle United'\n",
      " 'Tottenham' 'Everton' 'Leicester City' 'Manchester United' 'West Ham'\n",
      " 'Aston Villa' 'Arsenal' 'Brighton' 'Manchester City' 'Southampton'\n",
      " 'Wolverhampton' 'Brentford' 'Nottingham Forest' 'Chelsea' 'Liverpool']\n",
      "20 20\n",
      "['Burnley' 'Arsenal' 'Bournemouth' 'Brighton' 'Everton' 'Sheffield United'\n",
      " 'Newcastle United' 'Brentford' 'Chelsea' 'Manchester United'\n",
      " 'Nottingham Forest' 'Fulham' 'Liverpool' 'Wolverhampton' 'Tottenham'\n",
      " 'Manchester City' 'Aston Villa' 'West Ham' 'Crystal Palace' 'Luton Town']\n"
     ]
    }
   ],
   "source": [
    "tablas = []\n",
    "\n",
    "for torneo, i in zip(torneos, range(len(torneos))):\n",
    "\n",
    "    torneo.loc[:, \"team_home\"] = torneo[\"team_home\"].apply(lambda x: x.replace(\"AFC \", \"\"))\n",
    "    torneo.loc[:, \"team_away\"] = torneo[\"team_away\"].apply(lambda x: x.replace(\"AFC \", \"\"))\n",
    "\n",
    "    torneo.loc[:, \"team_home\"] = torneo[\"team_home\"].apply(lambda x: x.replace(\"West Ham United\", \"West Ham\"))\n",
    "    torneo.loc[:, \"team_away\"] = torneo[\"team_away\"].apply(lambda x: x.replace(\"West Ham United\", \"West Ham\"))\n",
    "\n",
    "    torneo.loc[:, \"team_home\"] = torneo[\"team_home\"].apply(lambda x: x.replace(\" Hotspur\", \"\"))\n",
    "    torneo.loc[:, \"team_away\"] = torneo[\"team_away\"].apply(lambda x: x.replace(\" Hotspur\", \"\"))\n",
    "\n",
    "    torneo.loc[:, \"team_home\"] = torneo[\"team_home\"].apply(lambda x: x.replace(\"Brighton & Hove Albion\", \"Brighton\"))\n",
    "    torneo.loc[:, \"team_away\"] = torneo[\"team_away\"].apply(lambda x: x.replace(\"Brighton & Hove Albion\", \"Brighton\"))\n",
    "\n",
    "    equipos = torneo[\"team_home\"].unique()\n",
    "    equipos2 = torneo[\"team_away\"].unique()\n",
    "    print(len(equipos), len(equipos2))\n",
    "    print(equipos)\n",
    "    stats = columnas\n",
    "\n",
    "    tabla = {}\n",
    "\n",
    "    for equipo in equipos:\n",
    "\n",
    "        tabla[equipo] = dict.fromkeys(stats, 0)\n",
    "        tabla[equipo][\"last_5_matches\"] = list()\n",
    "\n",
    "    tablas.append(tabla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_match</th>\n",
       "      <th>team_home</th>\n",
       "      <th>team_away</th>\n",
       "      <th>score_home</th>\n",
       "      <th>score_away</th>\n",
       "      <th>score_prom_home</th>\n",
       "      <th>score_prom_away</th>\n",
       "      <th>posession_home</th>\n",
       "      <th>posession_away</th>\n",
       "      <th>total_shots_home</th>\n",
       "      <th>...</th>\n",
       "      <th>points_home</th>\n",
       "      <th>points_away</th>\n",
       "      <th>streak_home</th>\n",
       "      <th>streak_away</th>\n",
       "      <th>wins_in_last_5_matches_home</th>\n",
       "      <th>wins_in_last_5_matches_away</th>\n",
       "      <th>ties_in_last_5_matches_home</th>\n",
       "      <th>ties_in_last_5_matches_away</th>\n",
       "      <th>losses_in_last_5_matches_home</th>\n",
       "      <th>losses_in_last_5_matches_away</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [number_of_match, team_home, team_away, score_home, score_away, score_prom_home, score_prom_away, posession_home, posession_away, total_shots_home, total_shots_away, shots_on_target_home, shots_on_target_away, shots_off_target_home, shots_off_target_away, blocked_shots_home, blocked_shots_away, corner_kicks_home, corner_kicks_away, fouls_home, fouls_away, red_cards_home, red_cards_away, free_kicks_home, free_kicks_away, goal_kicks_home, goal_kicks_away, shots_inside_box_home, shots_inside_box_away, shots_outside_box_home, shots_outside_box_away, passes_home, passes_away, accurate_passes_home, accurate_passes_away, long_balls_home, long_balls_away, crosses_home, crosses_away, dribbles_home, dribbles_away, duels_won_home, duels_won_away, aerials_won_home, aerials_won_away, tackles_home, tackles_away, interceptions_home, interceptions_away, last_season_position_home, last_season_position_away, wins_home, wins_away, ties_home, ties_away, losses_home, losses_away, total_goals_home, total_goals_away, total_goals_conceded_home, total_goals_conceded_away, points_home, points_away, streak_home, streak_away, wins_in_last_5_matches_home, wins_in_last_5_matches_away, ties_in_last_5_matches_home, ties_in_last_5_matches_away, losses_in_last_5_matches_home, losses_in_last_5_matches_away]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 71 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_stats = [\"wins\",\n",
    "\"ties\",\n",
    "\"losses\",\n",
    "\"total_goals\",\n",
    "\"total_goals_conceded\",\n",
    "\"points\",\n",
    "\"streak\",\n",
    "\"wins_in_last_5_matches\",\n",
    "\"ties_in_last_5_matches\",\n",
    "\"losses_in_last_5_matches\"]\n",
    "procesated_matches_columns = list(torneos[0].columns)\n",
    "procesated_matches_columns.insert(5, \"score_prom_away\")\n",
    "procesated_matches_columns.insert(5, \"score_prom_home\")\n",
    "\n",
    "for stat in new_stats:\n",
    "    procesated_matches_columns.append(f\"{stat}_home\")\n",
    "    procesated_matches_columns.append(f\"{stat}_away\")\n",
    "\n",
    "procesated_matches = pd.DataFrame(columns = procesated_matches_columns)\n",
    "\n",
    "procesated_matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for torneo, i in zip(torneos, range(len(torneos))):\n",
    "    tabla = tablas[i]\n",
    "    for index in torneo.index:\n",
    "    \n",
    "        \"\"\"Escribir info en el nuevo df de partidos con la información hasta el partido la cual esta almacenada en 'tabla'\"\"\"\n",
    "        match = torneo.loc[index]\n",
    "        team_home = match[\"team_home\"]\n",
    "        team_away = match[\"team_away\"]\n",
    "\n",
    "        stats_home = tabla[team_home]\n",
    "        stats_away = tabla[team_away]\n",
    "\n",
    "        fila = []\n",
    "        fila.append(match[\"number_of_match\"])\n",
    "        fila.append(team_home)\n",
    "        fila.append(team_away)\n",
    "        fila.append(match[\"score_home\"])\n",
    "        fila.append(match[\"score_away\"])\n",
    "\n",
    "        for stat in stats:\n",
    "            \n",
    "            fila.append(tabla[team_home][stat])\n",
    "            fila.append(tabla[team_away][stat])\n",
    "        \n",
    "        procesated_matches.loc[len(procesated_matches)] = fila\n",
    "        \n",
    "        \"\"\" Actualizar la tabla con lo que realmente ocurrió en ese partido\"\"\"\n",
    "\n",
    "        matches_played_home = tabla[team_home][\"wins\"] + tabla[team_home][\"ties\"] + tabla[team_home][\"losses\"]+1\n",
    "        matches_played_away = tabla[team_away][\"wins\"] + tabla[team_away][\"ties\"] + tabla[team_away][\"losses\"]+1\n",
    "        \n",
    "        ## stats promediadas:\n",
    "        for stat in stats[:stats.index(\"wins\")]:\n",
    "            if stat == \"score_prom\":\n",
    "                tabla[team_home][stat] = (tabla[team_home][stat]*(matches_played_home-1) + match[\"score_home\"])/matches_played_home\n",
    "                tabla[team_away][stat] = (tabla[team_away][stat]*(matches_played_away-1) + match[\"score_away\"])/matches_played_away\n",
    "            else:\n",
    "                tabla[team_home][stat] = (tabla[team_home][stat]*(matches_played_home-1) + match[f\"{stat}_home\"])/matches_played_home\n",
    "                tabla[team_away][stat] = (tabla[team_away][stat]*(matches_played_away-1) + match[f\"{stat}_away\"])/matches_played_away\n",
    "            \n",
    "        ## stats acumuladas\n",
    "        \n",
    "        empate = False\n",
    "        if match[\"score_home\"] > match[\"score_away\"]:\n",
    "\n",
    "            #victorias / derrotas\n",
    "            tabla[team_home][\"wins\"] += 1\n",
    "            tabla[team_away][\"losses\"] += 1\n",
    "\n",
    "            #goles a favor\n",
    "            tabla[team_home][\"total_goals\"] += match[\"score_home\"]\n",
    "            tabla[team_away][\"total_goals\"] += match[\"score_away\"]\n",
    "\n",
    "            #goles en contra\n",
    "            tabla[team_home][\"total_goals_conceded\"] += match[\"score_away\"]\n",
    "            tabla[team_away][\"total_goals_conceded\"] += match[\"score_home\"]\n",
    "\n",
    "            #puntos\n",
    "            tabla[team_home][\"points\"] += 3\n",
    "\n",
    "            # racha\n",
    "            tabla[team_home][\"streak\"] += 1\n",
    "            tabla[team_away][\"streak\"] = 0\n",
    "\n",
    "            # ultimos 5 partidos\n",
    "            if len(tabla[team_home][\"last_5_matches\"]) == 5:\n",
    "                tabla[team_home][\"last_5_matches\"].pop(0)\n",
    "            if len(tabla[team_away][\"last_5_matches\"]) == 5:\n",
    "                tabla[team_away][\"last_5_matches\"].pop(0)\n",
    "\n",
    "            tabla[team_home][\"last_5_matches\"].append(\"W\")\n",
    "            tabla[team_away][\"last_5_matches\"].append(\"L\")\n",
    "\n",
    "\n",
    "        elif match[\"score_home\"] < match[\"score_away\"]:\n",
    "\n",
    "            #victorias / derrotas\n",
    "            tabla[team_away][\"wins\"] += 1\n",
    "            tabla[team_home][\"losses\"] += 1\n",
    "\n",
    "            #goles a favor\n",
    "            tabla[team_away][\"total_goals\"] += match[\"score_away\"]\n",
    "            tabla[team_home][\"total_goals\"] += match[\"score_home\"]\n",
    "\n",
    "            #goles en contra\n",
    "            tabla[team_away][\"total_goals_conceded\"] += match[\"score_home\"]\n",
    "            tabla[team_home][\"total_goals_conceded\"] += match[\"score_away\"]\n",
    "\n",
    "            #puntos\n",
    "            tabla[team_away][\"points\"] += 3\n",
    "\n",
    "            # racha\n",
    "            tabla[team_away][\"streak\"] += 1\n",
    "            tabla[team_home][\"streak\"] = 0\n",
    "\n",
    "            # ultimos 5 partidos\n",
    "            if len(tabla[team_home][\"last_5_matches\"]) == 5:\n",
    "                tabla[team_home][\"last_5_matches\"].pop(0)\n",
    "            if len(tabla[team_away][\"last_5_matches\"]) == 5:\n",
    "                tabla[team_away][\"last_5_matches\"].pop(0)\n",
    "\n",
    "            tabla[team_away][\"last_5_matches\"].append(\"W\")\n",
    "            tabla[team_home][\"last_5_matches\"].append(\"L\")\n",
    "\n",
    "        else:\n",
    "            #empate\n",
    "            tabla[team_home][\"ties\"] += 1\n",
    "            tabla[team_away][\"ties\"] += 1\n",
    "\n",
    "            #goles a favor\n",
    "            tabla[team_home][\"total_goals\"] += match[\"score_home\"]\n",
    "            tabla[team_away][\"total_goals\"] += match[\"score_away\"]\n",
    "\n",
    "            #goles en contra\n",
    "            tabla[team_home][\"total_goals_conceded\"] += match[\"score_away\"]\n",
    "            tabla[team_away][\"total_goals_conceded\"] += match[\"score_home\"]\n",
    "\n",
    "            #puntos\n",
    "            tabla[team_home][\"points\"] += 1\n",
    "            tabla[team_away][\"points\"] += 1\n",
    "\n",
    "            # racha\n",
    "            tabla[team_away][\"streak\"] += 1\n",
    "            tabla[team_home][\"streak\"] += 1\n",
    "\n",
    "            # ultimos 5 partidos\n",
    "            if len(tabla[team_home][\"last_5_matches\"]) == 5:\n",
    "                tabla[team_home][\"last_5_matches\"].pop(0)\n",
    "            if len(tabla[team_away][\"last_5_matches\"]) == 5:\n",
    "                tabla[team_away][\"last_5_matches\"].pop(0)\n",
    "\n",
    "            tabla[team_away][\"last_5_matches\"].append(\"T\")\n",
    "            tabla[team_home][\"last_5_matches\"].append(\"T\")\n",
    "\n",
    "        tabla[team_home][\"wins_in_last_5_matches\"] = tabla[team_home][\"last_5_matches\"].count(\"W\")\n",
    "        tabla[team_home][\"ties_in_last_5_matches\"] = tabla[team_home][\"last_5_matches\"].count(\"T\")\n",
    "        tabla[team_home][\"losses_in_last_5_matches\"] = tabla[team_home][\"last_5_matches\"].count(\"L\")\n",
    "\n",
    "        tabla[team_away][\"wins_in_last_5_matches\"] = tabla[team_away][\"last_5_matches\"].count(\"W\")\n",
    "        tabla[team_away][\"ties_in_last_5_matches\"] = tabla[team_away][\"last_5_matches\"].count(\"T\")\n",
    "        tabla[team_away][\"losses_in_last_5_matches\"] = tabla[team_away][\"last_5_matches\"].count(\"L\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_match</th>\n",
       "      <th>team_home</th>\n",
       "      <th>team_away</th>\n",
       "      <th>score_home</th>\n",
       "      <th>score_away</th>\n",
       "      <th>score_prom_home</th>\n",
       "      <th>score_prom_away</th>\n",
       "      <th>posession_home</th>\n",
       "      <th>posession_away</th>\n",
       "      <th>total_shots_home</th>\n",
       "      <th>...</th>\n",
       "      <th>points_home</th>\n",
       "      <th>points_away</th>\n",
       "      <th>streak_home</th>\n",
       "      <th>streak_away</th>\n",
       "      <th>wins_in_last_5_matches_home</th>\n",
       "      <th>wins_in_last_5_matches_away</th>\n",
       "      <th>ties_in_last_5_matches_home</th>\n",
       "      <th>ties_in_last_5_matches_away</th>\n",
       "      <th>losses_in_last_5_matches_home</th>\n",
       "      <th>losses_in_last_5_matches_away</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>129</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.181818</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>41.636364</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>14.272727</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640</th>\n",
       "      <td>130</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>48.750000</td>\n",
       "      <td>47.833333</td>\n",
       "      <td>11.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>131</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.076923</td>\n",
       "      <td>1.384615</td>\n",
       "      <td>61.230769</td>\n",
       "      <td>47.230769</td>\n",
       "      <td>14.538462</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642</th>\n",
       "      <td>132</td>\n",
       "      <td>Brentford</td>\n",
       "      <td>Luton Town</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.461538</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>46.923077</td>\n",
       "      <td>35.769231</td>\n",
       "      <td>14.153846</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>147</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.384615</td>\n",
       "      <td>2.538462</td>\n",
       "      <td>52.538462</td>\n",
       "      <td>62.384615</td>\n",
       "      <td>14.923077</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      number_of_match    team_home          team_away  score_home  score_away  \\\n",
       "1639              129      Everton  Manchester United         0.0         3.0   \n",
       "1640              130       Fulham      Wolverhampton         3.0         2.0   \n",
       "1641              131      Arsenal      Wolverhampton         2.0         1.0   \n",
       "1642              132    Brentford         Luton Town         3.0         1.0   \n",
       "1643              147  Aston Villa    Manchester City         1.0         0.0   \n",
       "\n",
       "      score_prom_home  score_prom_away  posession_home  posession_away  \\\n",
       "1639         1.181818         1.083333       41.636364       54.250000   \n",
       "1640         0.833333         1.333333       48.750000       47.833333   \n",
       "1641         2.076923         1.384615       61.230769       47.230769   \n",
       "1642         1.461538         0.923077       46.923077       35.769231   \n",
       "1643         2.384615         2.538462       52.538462       62.384615   \n",
       "\n",
       "      total_shots_home  ...  points_home  points_away  streak_home  \\\n",
       "1639         14.272727  ...           13           21            2   \n",
       "1640         11.083333  ...           12           15            0   \n",
       "1641         14.538462  ...           30           15            2   \n",
       "1642         14.153846  ...           16            9            0   \n",
       "1643         14.923077  ...           28           29            2   \n",
       "\n",
       "      streak_away  wins_in_last_5_matches_home  wins_in_last_5_matches_away  \\\n",
       "1639            2                            3                            4   \n",
       "1640            1                            1                            2   \n",
       "1641            0                            3                            2   \n",
       "1642            1                            3                            1   \n",
       "1643            5                            4                            3   \n",
       "\n",
       "      ties_in_last_5_matches_home  ties_in_last_5_matches_away  \\\n",
       "1639                            0                            0   \n",
       "1640                            1                            2   \n",
       "1641                            1                            1   \n",
       "1642                            0                            2   \n",
       "1643                            0                            2   \n",
       "\n",
       "      losses_in_last_5_matches_home  losses_in_last_5_matches_away  \n",
       "1639                              2                              1  \n",
       "1640                              3                              1  \n",
       "1641                              1                              2  \n",
       "1642                              2                              2  \n",
       "1643                              1                              0  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "procesated_matches.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exportar CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#procesated_matches.to_csv(\"procesated_matches_PL_last_5_seasons.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using mps\n"
     ]
    }
   ],
   "source": [
    "#Usamos el GPU si esta disponible, mps porque es apple silicon.\n",
    "device = 'mps' if torch.backends.mps.is_available() else 'cpu'\n",
    "print(f\"using {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = procesated_matches\n",
    "\n",
    "#Aqui agrega la columna de resultados\n",
    "\n",
    "results = []\n",
    "\n",
    "for index in matches.index:\n",
    "\n",
    "    match = matches.loc[index]\n",
    "    if match[\"score_home\"] > match[\"score_away\"]:\n",
    "        results.append(1)\n",
    "    elif match[\"score_home\"] < match[\"score_away\"]:\n",
    "        results.append(2)\n",
    "    else:\n",
    "        results.append(0)\n",
    "\n",
    "matches[\"result\"] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_match</th>\n",
       "      <th>team_home</th>\n",
       "      <th>team_away</th>\n",
       "      <th>score_home</th>\n",
       "      <th>score_away</th>\n",
       "      <th>score_prom_home</th>\n",
       "      <th>score_prom_away</th>\n",
       "      <th>posession_home</th>\n",
       "      <th>posession_away</th>\n",
       "      <th>total_shots_home</th>\n",
       "      <th>...</th>\n",
       "      <th>points_away</th>\n",
       "      <th>streak_home</th>\n",
       "      <th>streak_away</th>\n",
       "      <th>wins_in_last_5_matches_home</th>\n",
       "      <th>wins_in_last_5_matches_away</th>\n",
       "      <th>ties_in_last_5_matches_home</th>\n",
       "      <th>ties_in_last_5_matches_away</th>\n",
       "      <th>losses_in_last_5_matches_home</th>\n",
       "      <th>losses_in_last_5_matches_away</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Norwich City</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Everton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_match       team_home         team_away  score_home  score_away  \\\n",
       "0                0       Liverpool      Norwich City         4.0         1.0   \n",
       "1                1        West Ham   Manchester City         0.0         5.0   \n",
       "2                2     Bournemouth  Sheffield United         1.0         1.0   \n",
       "3                3         Burnley       Southampton         3.0         0.0   \n",
       "4                4  Crystal Palace           Everton         0.0         0.0   \n",
       "\n",
       "   score_prom_home  score_prom_away  posession_home  posession_away  \\\n",
       "0              0.0              0.0             0.0             0.0   \n",
       "1              0.0              0.0             0.0             0.0   \n",
       "2              0.0              0.0             0.0             0.0   \n",
       "3              0.0              0.0             0.0             0.0   \n",
       "4              0.0              0.0             0.0             0.0   \n",
       "\n",
       "   total_shots_home  ...  points_away  streak_home  streak_away  \\\n",
       "0               0.0  ...            0            0            0   \n",
       "1               0.0  ...            0            0            0   \n",
       "2               0.0  ...            0            0            0   \n",
       "3               0.0  ...            0            0            0   \n",
       "4               0.0  ...            0            0            0   \n",
       "\n",
       "   wins_in_last_5_matches_home  wins_in_last_5_matches_away  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   ties_in_last_5_matches_home  ties_in_last_5_matches_away  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   losses_in_last_5_matches_home  losses_in_last_5_matches_away  result  \n",
       "0                              0                              0       1  \n",
       "1                              0                              0       2  \n",
       "2                              0                              0       0  \n",
       "3                              0                              0       1  \n",
       "4                              0                              0       0  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado 0, hay: 371 partidos\n",
      "Resultado 1, hay: 722 partidos\n",
      "Resultado 2, hay: 551 partidos\n",
      "azar: 0.4391727493917275\n"
     ]
    }
   ],
   "source": [
    "#Cantidad de partidos por resultado (local, empate, visita)\n",
    "\n",
    "for i in range(0,3):\n",
    "    cantidad = matches[matches[\"result\"]==i].shape[0]\n",
    "    print(f\"Resultado {i}, hay: {cantidad} partidos\")\n",
    "num = 722/(371+722+551)\n",
    "print(\"azar:\", num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1644"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "371+722+551"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(      score_prom_home  score_prom_away  posession_home  posession_away  \\\n",
       " 1507         0.891892         1.000000       42.702703       39.540541   \n",
       " 1508         1.270270         1.783784       46.810811       50.000000   \n",
       " 1509         1.324324         1.108108       47.648649       41.378378   \n",
       " 1510         1.513514         1.459459       53.810811       48.675676   \n",
       " 1511         0.864865         1.918919       44.837838       60.810811   \n",
       " \n",
       "       total_shots_home  total_shots_away  shots_on_target_home  \\\n",
       " 1507         11.243243          9.486486              3.945946   \n",
       " 1508         11.972973         13.702703              3.864865   \n",
       " 1509         10.945946         12.378378              4.000000   \n",
       " 1510         15.459459         11.324324              5.594595   \n",
       " 1511         10.864865         15.540541              3.567568   \n",
       " \n",
       "       shots_on_target_away  shots_off_target_home  shots_off_target_away  ...  \\\n",
       " 1507              3.540541               4.054054               3.405405  ...   \n",
       " 1508              5.189189               4.432432               4.594595  ...   \n",
       " 1509              3.837838               4.000000               4.810811  ...   \n",
       " 1510              3.945946               5.540541               4.270270  ...   \n",
       " 1511              5.513514               4.297297               5.864865  ...   \n",
       " \n",
       "       points_home  points_away  streak_home  streak_away  \\\n",
       " 1507           33           39            1            0   \n",
       " 1508           31           57            0            0   \n",
       " 1509           31           40            1            1   \n",
       " 1510           72           52            2            3   \n",
       " 1511           24           66            0            8   \n",
       " \n",
       "       wins_in_last_5_matches_home  wins_in_last_5_matches_away  \\\n",
       " 1507                            1                            2   \n",
       " 1508                            0                            1   \n",
       " 1509                            0                            2   \n",
       " 1510                            3                            2   \n",
       " 1511                            0                            4   \n",
       " \n",
       "       ties_in_last_5_matches_home  ties_in_last_5_matches_away  \\\n",
       " 1507                            2                            0   \n",
       " 1508                            2                            1   \n",
       " 1509                            3                            0   \n",
       " 1510                            1                            1   \n",
       " 1511                            0                            1   \n",
       " \n",
       "       losses_in_last_5_matches_home  losses_in_last_5_matches_away  \n",
       " 1507                              2                              3  \n",
       " 1508                              3                              3  \n",
       " 1509                              2                              3  \n",
       " 1510                              1                              2  \n",
       " 1511                              5                              0  \n",
       " \n",
       " [5 rows x 66 columns],\n",
       "       score_prom_home  score_prom_away  posession_home  posession_away  \\\n",
       " 1639         1.181818         1.083333       41.636364       54.250000   \n",
       " 1640         0.833333         1.333333       48.750000       47.833333   \n",
       " 1641         2.076923         1.384615       61.230769       47.230769   \n",
       " 1642         1.461538         0.923077       46.923077       35.769231   \n",
       " 1643         2.384615         2.538462       52.538462       62.384615   \n",
       " \n",
       "       total_shots_home  total_shots_away  shots_on_target_home  \\\n",
       " 1639         14.272727         14.833333              4.818182   \n",
       " 1640         11.083333         12.166667              3.583333   \n",
       " 1641         14.538462         12.000000              5.230769   \n",
       " 1642         14.153846         11.384615              4.538462   \n",
       " 1643         14.923077         16.461538              5.692308   \n",
       " \n",
       "       shots_on_target_away  shots_off_target_home  shots_off_target_away  ...  \\\n",
       " 1639              4.750000               6.181818               5.166667  ...   \n",
       " 1640              3.833333               4.500000               4.583333  ...   \n",
       " 1641              4.000000               4.538462               4.538462  ...   \n",
       " 1642              2.692308               5.846154               4.923077  ...   \n",
       " 1643              7.153846               5.461538               4.384615  ...   \n",
       " \n",
       "       points_home  points_away  streak_home  streak_away  \\\n",
       " 1639           13           21            2            2   \n",
       " 1640           12           15            0            1   \n",
       " 1641           30           15            2            0   \n",
       " 1642           16            9            0            1   \n",
       " 1643           28           29            2            5   \n",
       " \n",
       "       wins_in_last_5_matches_home  wins_in_last_5_matches_away  \\\n",
       " 1639                            3                            4   \n",
       " 1640                            1                            2   \n",
       " 1641                            3                            2   \n",
       " 1642                            3                            1   \n",
       " 1643                            4                            3   \n",
       " \n",
       "       ties_in_last_5_matches_home  ties_in_last_5_matches_away  \\\n",
       " 1639                            0                            0   \n",
       " 1640                            1                            2   \n",
       " 1641                            1                            1   \n",
       " 1642                            0                            2   \n",
       " 1643                            0                            2   \n",
       " \n",
       "       losses_in_last_5_matches_home  losses_in_last_5_matches_away  \n",
       " 1639                              2                              1  \n",
       " 1640                              3                              1  \n",
       " 1641                              1                              2  \n",
       " 1642                              2                              2  \n",
       " 1643                              1                              0  \n",
       " \n",
       " [5 rows x 66 columns])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ahora,vamos a separar el último torneo\n",
    "\n",
    "cant_partidos_ultima_temp = len(torneos[-1])\n",
    "\n",
    "matches_train = matches.iloc[:-cant_partidos_ultima_temp]\n",
    "matches_test = matches.iloc[-cant_partidos_ultima_temp:]\n",
    "\n",
    "result_train = matches_train[\"result\"]\n",
    "result_test = matches_test[\"result\"]\n",
    "matches_train = matches_train.drop(columns = [\"team_home\", \"team_away\", \"result\", \"score_home\", \"score_away\", \"number_of_match\"])\n",
    "matches_test = matches_test.drop(columns = [\"team_home\", \"team_away\", \"result\", \"score_home\", \"score_away\", \"number_of_match\"])\n",
    "\n",
    "matches_train.tail(), matches_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columnas categóricas:\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "result = matches[\"result\"]\n",
    "matches = matches.drop(columns = [\"team_home\", \"team_away\", \"result\", \"score_home\", \"score_away\"])\n",
    "matches.head()\n",
    "\n",
    "columnas_categoricas = matches.select_dtypes(include=['object']).columns.tolist()\n",
    "\n",
    "print(\"Columnas categóricas:\")\n",
    "print(columnas_categoricas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "X = matches.to_numpy()\n",
    "scaler = StandardScaler()\n",
    "X_escalados = scaler.fit_transform(X)\n",
    "y = result\n",
    "'''\n",
    "\n",
    "X = matches_train.to_numpy()\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X)\n",
    "y_train = result_train\n",
    "\n",
    "X_test = scaler.transform(matches_test.to_numpy())\n",
    "y_test = result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.09482186, -2.08239938, -4.35882136, ..., -1.13515216,\n",
       "        -1.44088954, -1.41002467],\n",
       "       [-2.09482186, -2.08239938, -4.35882136, ..., -1.13515216,\n",
       "        -1.44088954, -1.41002467],\n",
       "       [-2.09482186, -2.08239938, -4.35882136, ..., -1.13515216,\n",
       "        -1.44088954, -1.41002467],\n",
       "       ...,\n",
       "       [-0.07301738, -0.40634757, -0.09474347, ..., -1.13515216,\n",
       "         0.17230926,  1.01265408],\n",
       "       [ 0.21581183,  0.12508349,  0.45670846, ..., -0.06843366,\n",
       "        -0.63429014,  0.2050945 ],\n",
       "       [-0.77445975,  0.8200318 , -0.34628295, ..., -0.06843366,\n",
       "         2.59210747, -1.41002467]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gustavofernandez/Desktop/Ing Civil/2024-1/IIC2985/iPre-resultados-deportivos/env/lib/python3.8/site-packages/pytorch_tabnet/abstract_model.py:82: UserWarning: Device used : cpu\n",
      "  warnings.warn(f\"Device used : {self.device}\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0  | loss: 1.33562 | train_accuracy: 0.34325 | valid_accuracy: 0.40909 |  0:00:00s\n",
      "epoch 1  | loss: 1.23101 | train_accuracy: 0.36442 | valid_accuracy: 0.42424 |  0:00:01s\n",
      "epoch 2  | loss: 1.19564 | train_accuracy: 0.3955  | valid_accuracy: 0.45455 |  0:00:01s\n",
      "epoch 3  | loss: 1.14749 | train_accuracy: 0.4127  | valid_accuracy: 0.43939 |  0:00:02s\n",
      "epoch 4  | loss: 1.14287 | train_accuracy: 0.43651 | valid_accuracy: 0.45455 |  0:00:02s\n",
      "epoch 5  | loss: 1.1273  | train_accuracy: 0.45833 | valid_accuracy: 0.44697 |  0:00:03s\n",
      "epoch 6  | loss: 1.1087  | train_accuracy: 0.45767 | valid_accuracy: 0.43182 |  0:00:04s\n",
      "epoch 7  | loss: 1.09653 | train_accuracy: 0.45437 | valid_accuracy: 0.4697  |  0:00:04s\n",
      "epoch 8  | loss: 1.0861  | train_accuracy: 0.47024 | valid_accuracy: 0.43939 |  0:00:05s\n",
      "epoch 9  | loss: 1.07891 | train_accuracy: 0.47156 | valid_accuracy: 0.46212 |  0:00:06s\n",
      "epoch 10 | loss: 1.08303 | train_accuracy: 0.46892 | valid_accuracy: 0.44697 |  0:00:06s\n",
      "epoch 11 | loss: 1.08434 | train_accuracy: 0.47421 | valid_accuracy: 0.46212 |  0:00:07s\n",
      "epoch 12 | loss: 1.0813  | train_accuracy: 0.47024 | valid_accuracy: 0.46212 |  0:00:07s\n",
      "epoch 13 | loss: 1.08186 | train_accuracy: 0.46825 | valid_accuracy: 0.4697  |  0:00:08s\n",
      "epoch 14 | loss: 1.06913 | train_accuracy: 0.4709  | valid_accuracy: 0.45455 |  0:00:09s\n",
      "epoch 15 | loss: 1.0553  | train_accuracy: 0.47024 | valid_accuracy: 0.46212 |  0:00:09s\n",
      "epoch 16 | loss: 1.05399 | train_accuracy: 0.48413 | valid_accuracy: 0.47727 |  0:00:10s\n",
      "epoch 17 | loss: 1.06027 | train_accuracy: 0.48677 | valid_accuracy: 0.49242 |  0:00:10s\n",
      "epoch 18 | loss: 1.04577 | train_accuracy: 0.50331 | valid_accuracy: 0.51515 |  0:00:11s\n",
      "epoch 19 | loss: 1.04532 | train_accuracy: 0.50595 | valid_accuracy: 0.47727 |  0:00:12s\n",
      "epoch 20 | loss: 1.04663 | train_accuracy: 0.50265 | valid_accuracy: 0.5303  |  0:00:12s\n",
      "epoch 21 | loss: 1.05821 | train_accuracy: 0.50794 | valid_accuracy: 0.51515 |  0:00:13s\n",
      "epoch 22 | loss: 1.03428 | train_accuracy: 0.50794 | valid_accuracy: 0.52273 |  0:00:13s\n",
      "epoch 23 | loss: 1.04961 | train_accuracy: 0.50463 | valid_accuracy: 0.5303  |  0:00:14s\n",
      "epoch 24 | loss: 1.05052 | train_accuracy: 0.50265 | valid_accuracy: 0.50758 |  0:00:14s\n",
      "epoch 25 | loss: 1.04025 | train_accuracy: 0.51257 | valid_accuracy: 0.53788 |  0:00:15s\n",
      "epoch 26 | loss: 1.04494 | train_accuracy: 0.50926 | valid_accuracy: 0.58333 |  0:00:15s\n",
      "epoch 27 | loss: 1.03264 | train_accuracy: 0.51521 | valid_accuracy: 0.59848 |  0:00:16s\n",
      "epoch 28 | loss: 1.03189 | train_accuracy: 0.51786 | valid_accuracy: 0.56818 |  0:00:16s\n",
      "epoch 29 | loss: 1.03345 | train_accuracy: 0.52116 | valid_accuracy: 0.56061 |  0:00:17s\n",
      "epoch 30 | loss: 1.03105 | train_accuracy: 0.52447 | valid_accuracy: 0.56061 |  0:00:17s\n",
      "epoch 31 | loss: 1.02966 | train_accuracy: 0.50926 | valid_accuracy: 0.54545 |  0:00:18s\n",
      "epoch 32 | loss: 1.02754 | train_accuracy: 0.51852 | valid_accuracy: 0.53788 |  0:00:19s\n",
      "epoch 33 | loss: 1.02732 | train_accuracy: 0.52712 | valid_accuracy: 0.53788 |  0:00:19s\n",
      "epoch 34 | loss: 1.01408 | train_accuracy: 0.51984 | valid_accuracy: 0.55303 |  0:00:19s\n",
      "epoch 35 | loss: 1.03993 | train_accuracy: 0.52381 | valid_accuracy: 0.57576 |  0:00:20s\n",
      "epoch 36 | loss: 1.03098 | train_accuracy: 0.52315 | valid_accuracy: 0.56061 |  0:00:20s\n",
      "epoch 37 | loss: 1.01315 | train_accuracy: 0.53836 | valid_accuracy: 0.55303 |  0:00:21s\n",
      "epoch 38 | loss: 1.01057 | train_accuracy: 0.52447 | valid_accuracy: 0.53788 |  0:00:21s\n",
      "epoch 39 | loss: 1.02062 | train_accuracy: 0.53108 | valid_accuracy: 0.56818 |  0:00:22s\n",
      "epoch 40 | loss: 1.02259 | train_accuracy: 0.53042 | valid_accuracy: 0.59091 |  0:00:22s\n",
      "epoch 41 | loss: 1.0146  | train_accuracy: 0.53042 | valid_accuracy: 0.56061 |  0:00:23s\n",
      "epoch 42 | loss: 1.01287 | train_accuracy: 0.52447 | valid_accuracy: 0.56818 |  0:00:23s\n",
      "epoch 43 | loss: 1.00631 | train_accuracy: 0.52315 | valid_accuracy: 0.56061 |  0:00:24s\n",
      "epoch 44 | loss: 1.01122 | train_accuracy: 0.52447 | valid_accuracy: 0.57576 |  0:00:24s\n",
      "epoch 45 | loss: 1.00006 | train_accuracy: 0.52778 | valid_accuracy: 0.56818 |  0:00:25s\n",
      "epoch 46 | loss: 1.00493 | train_accuracy: 0.53241 | valid_accuracy: 0.56818 |  0:00:26s\n",
      "epoch 47 | loss: 1.01114 | train_accuracy: 0.53108 | valid_accuracy: 0.56061 |  0:00:26s\n",
      "epoch 48 | loss: 1.00487 | train_accuracy: 0.52513 | valid_accuracy: 0.57576 |  0:00:27s\n",
      "epoch 49 | loss: 1.00323 | train_accuracy: 0.54034 | valid_accuracy: 0.56818 |  0:00:27s\n",
      "epoch 50 | loss: 1.00148 | train_accuracy: 0.53968 | valid_accuracy: 0.59091 |  0:00:28s\n",
      "epoch 51 | loss: 0.99109 | train_accuracy: 0.5377  | valid_accuracy: 0.58333 |  0:00:28s\n",
      "epoch 52 | loss: 1.00106 | train_accuracy: 0.54828 | valid_accuracy: 0.60606 |  0:00:29s\n",
      "epoch 53 | loss: 0.99774 | train_accuracy: 0.54497 | valid_accuracy: 0.59848 |  0:00:30s\n",
      "epoch 54 | loss: 1.00221 | train_accuracy: 0.54497 | valid_accuracy: 0.59848 |  0:00:30s\n",
      "epoch 55 | loss: 1.00225 | train_accuracy: 0.5463  | valid_accuracy: 0.58333 |  0:00:31s\n",
      "epoch 56 | loss: 0.99746 | train_accuracy: 0.54762 | valid_accuracy: 0.59848 |  0:00:31s\n",
      "epoch 57 | loss: 0.99802 | train_accuracy: 0.54696 | valid_accuracy: 0.58333 |  0:00:32s\n",
      "epoch 58 | loss: 0.97949 | train_accuracy: 0.54431 | valid_accuracy: 0.59091 |  0:00:32s\n",
      "epoch 59 | loss: 0.99803 | train_accuracy: 0.54828 | valid_accuracy: 0.58333 |  0:00:33s\n",
      "epoch 60 | loss: 0.99044 | train_accuracy: 0.54563 | valid_accuracy: 0.60606 |  0:00:34s\n",
      "epoch 61 | loss: 0.98946 | train_accuracy: 0.55622 | valid_accuracy: 0.58333 |  0:00:34s\n",
      "epoch 62 | loss: 0.99035 | train_accuracy: 0.54894 | valid_accuracy: 0.58333 |  0:00:35s\n",
      "epoch 63 | loss: 0.99453 | train_accuracy: 0.54894 | valid_accuracy: 0.59091 |  0:00:35s\n",
      "epoch 64 | loss: 0.98494 | train_accuracy: 0.54894 | valid_accuracy: 0.57576 |  0:00:36s\n",
      "epoch 65 | loss: 0.98393 | train_accuracy: 0.55423 | valid_accuracy: 0.58333 |  0:00:36s\n",
      "epoch 66 | loss: 0.98194 | train_accuracy: 0.55093 | valid_accuracy: 0.59848 |  0:00:37s\n",
      "epoch 67 | loss: 0.98099 | train_accuracy: 0.55159 | valid_accuracy: 0.59091 |  0:00:37s\n",
      "epoch 68 | loss: 0.98155 | train_accuracy: 0.55556 | valid_accuracy: 0.59848 |  0:00:38s\n",
      "epoch 69 | loss: 0.98691 | train_accuracy: 0.5582  | valid_accuracy: 0.59091 |  0:00:38s\n",
      "epoch 70 | loss: 0.98044 | train_accuracy: 0.55225 | valid_accuracy: 0.59091 |  0:00:39s\n",
      "epoch 71 | loss: 0.97889 | train_accuracy: 0.55357 | valid_accuracy: 0.57576 |  0:00:39s\n",
      "epoch 72 | loss: 0.97906 | train_accuracy: 0.55423 | valid_accuracy: 0.59091 |  0:00:40s\n",
      "epoch 73 | loss: 0.98388 | train_accuracy: 0.55622 | valid_accuracy: 0.59848 |  0:00:40s\n",
      "epoch 74 | loss: 0.9785  | train_accuracy: 0.56085 | valid_accuracy: 0.61364 |  0:00:41s\n",
      "epoch 75 | loss: 0.97312 | train_accuracy: 0.56085 | valid_accuracy: 0.59091 |  0:00:41s\n",
      "epoch 76 | loss: 0.97699 | train_accuracy: 0.55489 | valid_accuracy: 0.59091 |  0:00:42s\n",
      "epoch 77 | loss: 0.97552 | train_accuracy: 0.55489 | valid_accuracy: 0.55303 |  0:00:42s\n",
      "epoch 78 | loss: 0.97744 | train_accuracy: 0.56085 | valid_accuracy: 0.55303 |  0:00:43s\n",
      "epoch 79 | loss: 0.96415 | train_accuracy: 0.5582  | valid_accuracy: 0.56061 |  0:00:43s\n",
      "epoch 80 | loss: 0.97388 | train_accuracy: 0.56085 | valid_accuracy: 0.56061 |  0:00:44s\n",
      "epoch 81 | loss: 0.96722 | train_accuracy: 0.56283 | valid_accuracy: 0.59091 |  0:00:44s\n",
      "epoch 82 | loss: 0.97639 | train_accuracy: 0.56481 | valid_accuracy: 0.59091 |  0:00:45s\n",
      "epoch 83 | loss: 0.96977 | train_accuracy: 0.57011 | valid_accuracy: 0.57576 |  0:00:45s\n",
      "epoch 84 | loss: 0.97643 | train_accuracy: 0.57011 | valid_accuracy: 0.56818 |  0:00:46s\n",
      "epoch 85 | loss: 0.96859 | train_accuracy: 0.56614 | valid_accuracy: 0.56061 |  0:00:47s\n",
      "epoch 86 | loss: 0.97635 | train_accuracy: 0.56151 | valid_accuracy: 0.58333 |  0:00:47s\n",
      "epoch 87 | loss: 0.97379 | train_accuracy: 0.5668  | valid_accuracy: 0.60606 |  0:00:48s\n",
      "epoch 88 | loss: 0.96151 | train_accuracy: 0.57011 | valid_accuracy: 0.59091 |  0:00:48s\n",
      "epoch 89 | loss: 0.96861 | train_accuracy: 0.56878 | valid_accuracy: 0.59091 |  0:00:49s\n",
      "epoch 90 | loss: 0.9573  | train_accuracy: 0.57143 | valid_accuracy: 0.57576 |  0:00:49s\n",
      "epoch 91 | loss: 0.96216 | train_accuracy: 0.57011 | valid_accuracy: 0.54545 |  0:00:50s\n",
      "epoch 92 | loss: 0.96907 | train_accuracy: 0.56415 | valid_accuracy: 0.55303 |  0:00:50s\n",
      "epoch 93 | loss: 0.95705 | train_accuracy: 0.56746 | valid_accuracy: 0.57576 |  0:00:51s\n",
      "epoch 94 | loss: 0.95493 | train_accuracy: 0.57143 | valid_accuracy: 0.58333 |  0:00:51s\n",
      "epoch 95 | loss: 0.96096 | train_accuracy: 0.57011 | valid_accuracy: 0.57576 |  0:00:52s\n",
      "epoch 96 | loss: 0.95613 | train_accuracy: 0.58201 | valid_accuracy: 0.58333 |  0:00:52s\n",
      "epoch 97 | loss: 0.95485 | train_accuracy: 0.57937 | valid_accuracy: 0.56818 |  0:00:53s\n",
      "epoch 98 | loss: 0.94252 | train_accuracy: 0.58333 | valid_accuracy: 0.54545 |  0:00:53s\n",
      "epoch 99 | loss: 0.94925 | train_accuracy: 0.58201 | valid_accuracy: 0.54545 |  0:00:54s\n",
      "epoch 100| loss: 0.95159 | train_accuracy: 0.58598 | valid_accuracy: 0.52273 |  0:00:55s\n",
      "epoch 101| loss: 0.95418 | train_accuracy: 0.5873  | valid_accuracy: 0.52273 |  0:00:55s\n",
      "epoch 102| loss: 0.94992 | train_accuracy: 0.58333 | valid_accuracy: 0.53788 |  0:00:56s\n",
      "epoch 103| loss: 0.95972 | train_accuracy: 0.58003 | valid_accuracy: 0.51515 |  0:00:56s\n",
      "epoch 104| loss: 0.96084 | train_accuracy: 0.58069 | valid_accuracy: 0.55303 |  0:00:57s\n",
      "epoch 105| loss: 0.95597 | train_accuracy: 0.58466 | valid_accuracy: 0.54545 |  0:00:57s\n",
      "epoch 106| loss: 0.94822 | train_accuracy: 0.58267 | valid_accuracy: 0.54545 |  0:00:58s\n",
      "epoch 107| loss: 0.95624 | train_accuracy: 0.58003 | valid_accuracy: 0.56061 |  0:00:59s\n",
      "epoch 108| loss: 0.94276 | train_accuracy: 0.58532 | valid_accuracy: 0.54545 |  0:00:59s\n",
      "epoch 109| loss: 0.94783 | train_accuracy: 0.58796 | valid_accuracy: 0.55303 |  0:01:00s\n",
      "epoch 110| loss: 0.95791 | train_accuracy: 0.59458 | valid_accuracy: 0.56061 |  0:01:00s\n",
      "epoch 111| loss: 0.95088 | train_accuracy: 0.58333 | valid_accuracy: 0.56818 |  0:01:00s\n",
      "epoch 112| loss: 0.94691 | train_accuracy: 0.58399 | valid_accuracy: 0.56818 |  0:01:01s\n",
      "epoch 113| loss: 0.93791 | train_accuracy: 0.58598 | valid_accuracy: 0.55303 |  0:01:01s\n",
      "epoch 114| loss: 0.94865 | train_accuracy: 0.58929 | valid_accuracy: 0.55303 |  0:01:02s\n",
      "epoch 115| loss: 0.94549 | train_accuracy: 0.59854 | valid_accuracy: 0.55303 |  0:01:02s\n",
      "epoch 116| loss: 0.93741 | train_accuracy: 0.59722 | valid_accuracy: 0.55303 |  0:01:03s\n",
      "epoch 117| loss: 0.94374 | train_accuracy: 0.60053 | valid_accuracy: 0.56818 |  0:01:03s\n",
      "epoch 118| loss: 0.93825 | train_accuracy: 0.60053 | valid_accuracy: 0.54545 |  0:01:04s\n",
      "epoch 119| loss: 0.94222 | train_accuracy: 0.59987 | valid_accuracy: 0.55303 |  0:01:04s\n",
      "epoch 120| loss: 0.94472 | train_accuracy: 0.59788 | valid_accuracy: 0.56818 |  0:01:05s\n",
      "epoch 121| loss: 0.93491 | train_accuracy: 0.5959  | valid_accuracy: 0.56818 |  0:01:05s\n",
      "epoch 122| loss: 0.94113 | train_accuracy: 0.59987 | valid_accuracy: 0.56061 |  0:01:06s\n",
      "epoch 123| loss: 0.95466 | train_accuracy: 0.60185 | valid_accuracy: 0.56818 |  0:01:06s\n",
      "epoch 124| loss: 0.9395  | train_accuracy: 0.59987 | valid_accuracy: 0.56818 |  0:01:07s\n",
      "epoch 125| loss: 0.93896 | train_accuracy: 0.60317 | valid_accuracy: 0.58333 |  0:01:07s\n",
      "epoch 126| loss: 0.93881 | train_accuracy: 0.60913 | valid_accuracy: 0.57576 |  0:01:08s\n",
      "epoch 127| loss: 0.92153 | train_accuracy: 0.6045  | valid_accuracy: 0.56061 |  0:01:08s\n",
      "epoch 128| loss: 0.92165 | train_accuracy: 0.60648 | valid_accuracy: 0.56061 |  0:01:09s\n",
      "epoch 129| loss: 0.95087 | train_accuracy: 0.60847 | valid_accuracy: 0.54545 |  0:01:09s\n",
      "epoch 130| loss: 0.94117 | train_accuracy: 0.60913 | valid_accuracy: 0.57576 |  0:01:10s\n",
      "epoch 131| loss: 0.92756 | train_accuracy: 0.60516 | valid_accuracy: 0.56061 |  0:01:10s\n",
      "epoch 132| loss: 0.93164 | train_accuracy: 0.60913 | valid_accuracy: 0.54545 |  0:01:11s\n",
      "epoch 133| loss: 0.90861 | train_accuracy: 0.60582 | valid_accuracy: 0.52273 |  0:01:11s\n",
      "epoch 134| loss: 0.92016 | train_accuracy: 0.60847 | valid_accuracy: 0.54545 |  0:01:12s\n",
      "epoch 135| loss: 0.91804 | train_accuracy: 0.60317 | valid_accuracy: 0.54545 |  0:01:13s\n",
      "epoch 136| loss: 0.92855 | train_accuracy: 0.6045  | valid_accuracy: 0.56061 |  0:01:13s\n",
      "epoch 137| loss: 0.93656 | train_accuracy: 0.60516 | valid_accuracy: 0.54545 |  0:01:14s\n",
      "epoch 138| loss: 0.9408  | train_accuracy: 0.61111 | valid_accuracy: 0.54545 |  0:01:14s\n",
      "epoch 139| loss: 0.91865 | train_accuracy: 0.60979 | valid_accuracy: 0.54545 |  0:01:15s\n",
      "epoch 140| loss: 0.9343  | train_accuracy: 0.6078  | valid_accuracy: 0.53788 |  0:01:16s\n",
      "epoch 141| loss: 0.91475 | train_accuracy: 0.60714 | valid_accuracy: 0.56061 |  0:01:16s\n",
      "epoch 142| loss: 0.90869 | train_accuracy: 0.6045  | valid_accuracy: 0.57576 |  0:01:17s\n",
      "epoch 143| loss: 0.92345 | train_accuracy: 0.60847 | valid_accuracy: 0.56061 |  0:01:17s\n",
      "epoch 144| loss: 0.91662 | train_accuracy: 0.6078  | valid_accuracy: 0.54545 |  0:01:18s\n",
      "epoch 145| loss: 0.91199 | train_accuracy: 0.60847 | valid_accuracy: 0.53788 |  0:01:18s\n",
      "epoch 146| loss: 0.90913 | train_accuracy: 0.6078  | valid_accuracy: 0.55303 |  0:01:19s\n",
      "epoch 147| loss: 0.93516 | train_accuracy: 0.61045 | valid_accuracy: 0.56061 |  0:01:19s\n",
      "epoch 148| loss: 0.91281 | train_accuracy: 0.61045 | valid_accuracy: 0.54545 |  0:01:20s\n",
      "epoch 149| loss: 0.91141 | train_accuracy: 0.60847 | valid_accuracy: 0.55303 |  0:01:20s\n",
      "epoch 150| loss: 0.90787 | train_accuracy: 0.61177 | valid_accuracy: 0.53788 |  0:01:21s\n",
      "epoch 151| loss: 0.91677 | train_accuracy: 0.60979 | valid_accuracy: 0.53788 |  0:01:21s\n",
      "epoch 152| loss: 0.91171 | train_accuracy: 0.60913 | valid_accuracy: 0.54545 |  0:01:22s\n",
      "epoch 153| loss: 0.90739 | train_accuracy: 0.61243 | valid_accuracy: 0.5303  |  0:01:22s\n",
      "epoch 154| loss: 0.91369 | train_accuracy: 0.6164  | valid_accuracy: 0.5     |  0:01:23s\n",
      "epoch 155| loss: 0.90093 | train_accuracy: 0.61905 | valid_accuracy: 0.52273 |  0:01:23s\n",
      "epoch 156| loss: 0.90286 | train_accuracy: 0.61839 | valid_accuracy: 0.52273 |  0:01:24s\n",
      "epoch 157| loss: 0.90623 | train_accuracy: 0.62103 | valid_accuracy: 0.49242 |  0:01:24s\n",
      "epoch 158| loss: 0.91369 | train_accuracy: 0.61574 | valid_accuracy: 0.52273 |  0:01:25s\n",
      "epoch 159| loss: 0.90968 | train_accuracy: 0.62368 | valid_accuracy: 0.48485 |  0:01:25s\n",
      "epoch 160| loss: 0.91558 | train_accuracy: 0.61971 | valid_accuracy: 0.50758 |  0:01:26s\n",
      "epoch 161| loss: 0.89391 | train_accuracy: 0.61706 | valid_accuracy: 0.51515 |  0:01:26s\n",
      "epoch 162| loss: 0.8818  | train_accuracy: 0.62434 | valid_accuracy: 0.53788 |  0:01:27s\n",
      "epoch 163| loss: 0.905   | train_accuracy: 0.62765 | valid_accuracy: 0.53788 |  0:01:27s\n",
      "epoch 164| loss: 0.90106 | train_accuracy: 0.62302 | valid_accuracy: 0.52273 |  0:01:28s\n",
      "epoch 165| loss: 0.90303 | train_accuracy: 0.62566 | valid_accuracy: 0.53788 |  0:01:28s\n",
      "epoch 166| loss: 0.89805 | train_accuracy: 0.62963 | valid_accuracy: 0.53788 |  0:01:29s\n",
      "epoch 167| loss: 0.89753 | train_accuracy: 0.625   | valid_accuracy: 0.56061 |  0:01:29s\n",
      "epoch 168| loss: 0.901   | train_accuracy: 0.63426 | valid_accuracy: 0.52273 |  0:01:30s\n",
      "epoch 169| loss: 0.90951 | train_accuracy: 0.63228 | valid_accuracy: 0.5303  |  0:01:31s\n",
      "epoch 170| loss: 0.89476 | train_accuracy: 0.63029 | valid_accuracy: 0.54545 |  0:01:31s\n",
      "epoch 171| loss: 0.90105 | train_accuracy: 0.62632 | valid_accuracy: 0.53788 |  0:01:32s\n",
      "epoch 172| loss: 0.89231 | train_accuracy: 0.63161 | valid_accuracy: 0.55303 |  0:01:32s\n",
      "epoch 173| loss: 0.90376 | train_accuracy: 0.62963 | valid_accuracy: 0.55303 |  0:01:33s\n",
      "epoch 174| loss: 0.89364 | train_accuracy: 0.62963 | valid_accuracy: 0.54545 |  0:01:33s\n",
      "\n",
      "Early stopping occurred at epoch 174 with best_epoch = 74 and best_valid_accuracy = 0.61364\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gustavofernandez/Desktop/Ing Civil/2024-1/IIC2985/iPre-resultados-deportivos/env/lib/python3.8/site-packages/pytorch_tabnet/callbacks.py:172: UserWarning: Best weights from best epoch are automatically used!\n",
      "  warnings.warn(wrn_msg)\n"
     ]
    }
   ],
   "source": [
    "#kfold = KFold(n_splits=5)\n",
    "\n",
    "#y_true_all = []\n",
    "#y_pred_all = []\n",
    "\n",
    "#for train_index, test_index in kfold.split(X_escalados):\n",
    "#X_train, X_test = X_escalados[train_index], X_escalados[test_index]\n",
    "#y_train, y_test = y[train_index], y[test_index]\n",
    "clf = TabNetClassifier(\n",
    "    optimizer_fn=torch.optim.Adam,\n",
    "    optimizer_params=dict(lr=1e-3),\n",
    "    scheduler_params={\"step_size\":50, # how to use learning rate scheduler\n",
    "                        \"gamma\":0.9},\n",
    "    scheduler_fn=torch.optim.lr_scheduler.StepLR,\n",
    "    mask_type='entmax', # \"sparsemax\" \"entmax\"\n",
    "    )\n",
    "    \n",
    "clf.fit(X_train, y_train,\n",
    "    eval_set=[(X_train, y_train), (X_test, y_test)], \n",
    "    eval_name=['train', 'valid'], \n",
    "    eval_metric=['accuracy'], \n",
    "    max_epochs=1000 , patience=100, \n",
    "    batch_size=28, drop_last=False)\n",
    "\n",
    "pred = clf.predict(X_test)\n",
    "# Almacenar las predicciones\n",
    "#y_true_all.extend(y_test)\n",
    "#y_pred_all.extend(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.04      0.07        25\n",
      "           1       0.61      0.87      0.72        61\n",
      "           2       0.63      0.59      0.61        46\n",
      "\n",
      "    accuracy                           0.61       132\n",
      "   macro avg       0.58      0.50      0.47       132\n",
      "weighted avg       0.60      0.61      0.56       132\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"classification report\")\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "cnf_matrix = confusion_matrix(y_test, pred)\n",
    "classes = np.unique(y_train)\n",
    "np.set_printoptions(precision=2)\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure(figsize=(5,5))\n",
    "\n",
    "plot_confusion_matrix(cnf_matrix, classes=classes, normalize=True, title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17c7f75b0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clf.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'last_season_position_home': 0.06209388862051942,\n",
       " 'score_prom_home': 0.048898420379555,\n",
       " 'total_shots_home': 0.046467202048021336,\n",
       " 'total_goals_home': 0.03689434995357039,\n",
       " 'total_shots_away': 0.034578259171510944,\n",
       " 'shots_outside_box_away': 0.030299166663668105,\n",
       " 'shots_inside_box_home': 0.030250367361262506,\n",
       " 'losses_home': 0.029123727000148602,\n",
       " 'losses_in_last_5_matches_home': 0.025652837238259563,\n",
       " 'score_prom_away': 0.025141552893100126,\n",
       " 'accurate_passes_home': 0.02491194247503243,\n",
       " 'shots_on_target_away': 0.02414234125986878,\n",
       " 'wins_away': 0.023523308399486135,\n",
       " 'losses_away': 0.023220380920402003,\n",
       " 'goal_kicks_home': 0.02195568727849502,\n",
       " 'corner_kicks_home': 0.02118667066387307,\n",
       " 'shots_on_target_home': 0.02011846871534145,\n",
       " 'streak_away': 0.01798572912140835,\n",
       " 'free_kicks_home': 0.017147597144463303,\n",
       " 'total_goals_conceded_home': 0.016865971830602323,\n",
       " 'passes_away': 0.016693560315492528,\n",
       " 'passes_home': 0.01628487761126554,\n",
       " 'corner_kicks_away': 0.01552894889858733,\n",
       " 'red_cards_away': 0.015083506205779253,\n",
       " 'tackles_away': 0.014107520720834753,\n",
       " 'wins_in_last_5_matches_away': 0.014054004414649312,\n",
       " 'dribbles_home': 0.013974677516942145,\n",
       " 'total_goals_conceded_away': 0.01317074624919533,\n",
       " 'shots_outside_box_home': 0.013070660371038465,\n",
       " 'losses_in_last_5_matches_away': 0.012614264353239311}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature importance\n",
    "clf.feature_importances_\n",
    "#diccionario con los nombres de las columnas y su importancia\n",
    "feature_importances = dict(zip(matches_test.columns, clf.feature_importances_))\n",
    "#ordenar de mayor a menor\n",
    "sorted_feature_importances = dict(sorted(feature_importances.items(), key=lambda item: item[1], reverse=True))\n",
    "#las 20 mas importantes\n",
    "top = dict(itertools.islice(sorted_feature_importances.items(), 30))\n",
    "top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19435386100166613"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#suma total de importancia 4 mas importante\n",
    "sum(list(top.values())[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total atributos\n",
    "len(matches_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#tablatura con las 30 mas importantes img\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(top.keys(), top.values())\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Relevance\")\n",
    "plt.xlabel(\"Attribute\")\n",
    "plt.title(\"30 most relevant attributes\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer, masks = clf.explain(X_train)\n",
    "\n",
    "# plot feature importance\n",
    "fig, axs = plt.subplots(1, 3, figsize=(20, 20))\n",
    "for i in range(3):\n",
    "    axs[i].imshow(masks[i][:50])\n",
    "    axs[i].set_title(f'Layer {i}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}