ADCC_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "\n",
    "## ADCC Model Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalize feature generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "Project: Predict match-level win/loss outcome for fighter match ups at ADCC. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "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>Gender</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Championship</th>\n",
       "      <th>Round</th>\n",
       "      <th>Fighter</th>\n",
       "      <th>Opponant</th>\n",
       "      <th>Points</th>\n",
       "      <th>Result</th>\n",
       "      <th>How</th>\n",
       "      <th>Time</th>\n",
       "      <th>...</th>\n",
       "      <th>MR_IBJJF_yr</th>\n",
       "      <th>IBJJF_belt</th>\n",
       "      <th>Num_IBJJF_medals</th>\n",
       "      <th>MR_IBJJF_place</th>\n",
       "      <th>MR_Other_yr</th>\n",
       "      <th>Other_belt</th>\n",
       "      <th>Num_Other_medals</th>\n",
       "      <th>MR_Other_place</th>\n",
       "      <th>Birthdate</th>\n",
       "      <th>Belt_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men -66kg</td>\n",
       "      <td>Championship</td>\n",
       "      <td>Semi-Finals</td>\n",
       "      <td>Paulo Miyao</td>\n",
       "      <td>Augusto \"Tanquinho\" Mendes</td>\n",
       "      <td>0</td>\n",
       "      <td>l</td>\n",
       "      <td>ref decision</td>\n",
       "      <td>15:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>black</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>black</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1991-05-11 00:00:00</td>\n",
       "      <td>2013-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men -77kg</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Garry Tonon</td>\n",
       "      <td>Dante Leon</td>\n",
       "      <td>0</td>\n",
       "      <td>w</td>\n",
       "      <td>submission</td>\n",
       "      <td>5:22</td>\n",
       "      <td>...</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>black</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>black</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1991-09-12 00:00:00</td>\n",
       "      <td>2013-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men -88kg</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Josh Hinger</td>\n",
       "      <td>Jon Blank</td>\n",
       "      <td>3</td>\n",
       "      <td>w</td>\n",
       "      <td>score</td>\n",
       "      <td>10:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>black</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>black</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1982-05-28 00:00:00</td>\n",
       "      <td>2013-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men -99kg</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Lucas \"Hulk\" Barbosa</td>\n",
       "      <td>Vinny Magalhaes</td>\n",
       "      <td>0</td>\n",
       "      <td>w</td>\n",
       "      <td>forfeit</td>\n",
       "      <td>0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>black</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>black</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1992-02-13 00:00:00</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men +99kg</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Marcus Buchecha Almeida</td>\n",
       "      <td>Roberto \"Cyborg\" Abreu</td>\n",
       "      <td>0</td>\n",
       "      <td>w</td>\n",
       "      <td>forfeit</td>\n",
       "      <td>0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>black</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>black</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1990-01-08 00:00:00</td>\n",
       "      <td>2010-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Weight  Championship        Round                  Fighter  \\\n",
       "0   Male  Men -66kg  Championship  Semi-Finals              Paulo Miyao   \n",
       "1   Male  Men -77kg   Consolation  Consolation              Garry Tonon   \n",
       "2   Male  Men -88kg   Consolation  Consolation              Josh Hinger   \n",
       "3   Male  Men -99kg   Consolation  Consolation     Lucas \"Hulk\" Barbosa   \n",
       "4   Male  Men +99kg   Consolation  Consolation  Marcus Buchecha Almeida   \n",
       "\n",
       "                     Opponant  Points Result           How   Time  ...  \\\n",
       "0  Augusto \"Tanquinho\" Mendes       0      l  ref decision  15:00  ...   \n",
       "1                  Dante Leon       0      w    submission   5:22  ...   \n",
       "2                   Jon Blank       3      w         score  10:00  ...   \n",
       "3             Vinny Magalhaes       0      w       forfeit   0:00  ...   \n",
       "4      Roberto \"Cyborg\" Abreu       0      w       forfeit   0:00  ...   \n",
       "\n",
       "  MR_IBJJF_yr  IBJJF_belt Num_IBJJF_medals MR_IBJJF_place MR_Other_yr  \\\n",
       "0      2019.0       black              6.0            1.0      2019.0   \n",
       "1      2014.0       black              1.0            3.0      2016.0   \n",
       "2      2018.0       black              4.0            1.0      2017.0   \n",
       "3      2019.0       black              9.0            1.0      2017.0   \n",
       "4      2012.0       black              6.0            1.0      2016.0   \n",
       "\n",
       "  Other_belt Num_Other_medals MR_Other_place            Birthdate  Belt_date  \n",
       "0      black              6.0            1.0  1991-05-11 00:00:00 2013-06-01  \n",
       "1      black              4.0            1.0  1991-09-12 00:00:00 2013-09-01  \n",
       "2      black              9.0            1.0  1982-05-28 00:00:00 2013-12-01  \n",
       "3      black              1.0            1.0  1992-02-13 00:00:00 2015-07-01  \n",
       "4      black              7.0            1.0  1990-01-08 00:00:00 2010-06-01  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('ADCC_fighter_history_FINAL.xlsx')\n",
    "df.drop(['Unnamed: 0'], axis=1, inplace=True)\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 61)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Row Number: 226 \n",
      "Total Col Number: 61\n"
     ]
    }
   ],
   "source": [
    "print(\"Total Row Number: {0} \\nTotal Col Number: {1}\".format(df.shape[0], df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 226 entries, 0 to 225\n",
      "Data columns (total 61 columns):\n",
      "Gender                  226 non-null object\n",
      "Weight                  226 non-null object\n",
      "Championship            226 non-null object\n",
      "Round                   226 non-null object\n",
      "Fighter                 226 non-null object\n",
      "Opponant                226 non-null object\n",
      "Points                  226 non-null int64\n",
      "Result                  226 non-null object\n",
      "How                     226 non-null object\n",
      "Time                    226 non-null object\n",
      "Day                     226 non-null object\n",
      "Match No                226 non-null int64\n",
      "Place                   226 non-null object\n",
      "Entrance                221 non-null object\n",
      "Gym                     226 non-null object\n",
      "Belt                    224 non-null object\n",
      "Medal_ADCC              79 non-null object\n",
      "ADCC_place              79 non-null object\n",
      "ADCC_Year               80 non-null object\n",
      "ADCC_years_competed     224 non-null object\n",
      "Num_ADCC_comp           224 non-null float64\n",
      "Trails_winner           93 non-null object\n",
      "Trials_Year             94 non-null object\n",
      "Trails_place            88 non-null object\n",
      "Highest_ADCC_place      61 non-null float64\n",
      "Max_ADCC_medals         61 non-null float64\n",
      "MR_ADCC_Medal_yr        224 non-null float64\n",
      "Highest_Trials_place    88 non-null float64\n",
      "Max_tr_medals           88 non-null float64\n",
      "MR_tr_Medal_yr          94 non-null float64\n",
      "age_float               226 non-null float64\n",
      "blackbelt_float         226 non-null float64\n",
      "Results                 226 non-null object\n",
      "RoundNo                 226 non-null int64\n",
      "Min                     226 non-null int64\n",
      "Sec                     226 non-null int64\n",
      "Seconds                 226 non-null float64\n",
      "KGWeight                226 non-null int64\n",
      "How_weighted            226 non-null int64\n",
      "How_weighted_2          226 non-null int64\n",
      "Result_score            226 non-null int64\n",
      "Final_WL_Score          226 non-null int64\n",
      "Place_score             226 non-null int64\n",
      "Place_num               226 non-null int64\n",
      "IBJJF_world             134 non-null object\n",
      "IBJJF_majors            177 non-null object\n",
      "Other_tours             151 non-null object\n",
      "MR_IBJJF_W_yr           132 non-null float64\n",
      "IBJJF_W_belt            133 non-null object\n",
      "Num_IBJJF_W_medals      132 non-null float64\n",
      "MR_IBJJF_W_place        132 non-null float64\n",
      "MR_IBJJF_yr             177 non-null float64\n",
      "IBJJF_belt              178 non-null object\n",
      "Num_IBJJF_medals        177 non-null float64\n",
      "MR_IBJJF_place          177 non-null float64\n",
      "MR_Other_yr             151 non-null float64\n",
      "Other_belt              151 non-null object\n",
      "Num_Other_medals        151 non-null float64\n",
      "MR_Other_place          151 non-null float64\n",
      "Birthdate               214 non-null object\n",
      "Belt_date               194 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(19), int64(12), object(29)\n",
      "memory usage: 107.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Highest_ADCC_place      165\n",
       "Max_ADCC_medals         165\n",
       "ADCC_place              147\n",
       "Medal_ADCC              147\n",
       "ADCC_Year               146\n",
       "Trails_place            138\n",
       "Highest_Trials_place    138\n",
       "Max_tr_medals           138\n",
       "Trails_winner           133\n",
       "Trials_Year             132\n",
       "MR_tr_Medal_yr          132\n",
       "MR_IBJJF_W_yr            94\n",
       "Num_IBJJF_W_medals       94\n",
       "MR_IBJJF_W_place         94\n",
       "IBJJF_W_belt             93\n",
       "IBJJF_world              92\n",
       "Other_tours              75\n",
       "MR_Other_yr              75\n",
       "Other_belt               75\n",
       "Num_Other_medals         75\n",
       "MR_Other_place           75\n",
       "IBJJF_majors             49\n",
       "MR_IBJJF_yr              49\n",
       "Num_IBJJF_medals         49\n",
       "MR_IBJJF_place           49\n",
       "IBJJF_belt               48\n",
       "Belt_date                32\n",
       "Birthdate                12\n",
       "Entrance                  5\n",
       "ADCC_years_competed       2\n",
       "Num_ADCC_comp             2\n",
       "MR_ADCC_Medal_yr          2\n",
       "Belt                      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nullvalues = df.loc[:, df.isnull().any()].isnull().sum().sort_values(ascending=False)\n",
    "nullvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "from datetime import time\n",
    "# df['Time'] = pd.to_datetime(df['Time'], format= '%M:%S')\n",
    "df['Birthdate_DT'] = pd.to_datetime(df['Birthdate'])\n",
    "df['Belt_date_DT'] = pd.to_datetime(df['Belt_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Birthdate_DT'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2019, 12, 10, 9, 2, 50, 581145)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['age_float'] = (datetime.datetime.now() - df['Birthdate_DT'])/np.timedelta64(1, 'Y')\n",
    "df['blackbelt_float'] = (datetime.datetime.now() - df['Belt_date_DT'])/np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age_float.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill in birthdate NaNs with the median age\n",
    "df['age_float'].fillna((df['age_float'].median()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age_float.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.blackbelt_float.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill missing black belt dates with 0 since the fighters aren't bb yet\n",
    "df['blackbelt_float'].fillna(value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.blackbelt_float.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Target Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loss    113\n",
       "Win     113\n",
       "Name: Results, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Results.value_counts() #result is win/loss,  binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Results.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Loss', 'Win'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(df.Results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Results\n",
       "Loss    113\n",
       "Win     113\n",
       "Name: Fighter, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Results')['Fighter'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "Second Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "score           108\n",
       "submission       70\n",
       "ref decision     42\n",
       "forfeit           6\n",
       "Name: How, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.How.value_counts() #how match was won - 4 categories, multiclass classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.How.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ref decision', 'submission', 'score', 'forfeit'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(df.How)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "How\n",
       "forfeit           6\n",
       "ref decision     42\n",
       "score           108\n",
       "submission       70\n",
       "Name: Fighter, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('How')['Fighter'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "\n",
    "## Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Gender</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Championship</th>\n",
       "      <th>Round</th>\n",
       "      <th>Fighter</th>\n",
       "      <th>Opponant</th>\n",
       "      <th>Points</th>\n",
       "      <th>Result</th>\n",
       "      <th>How</th>\n",
       "      <th>Time</th>\n",
       "      <th>...</th>\n",
       "      <th>Num_IBJJF_medals</th>\n",
       "      <th>MR_IBJJF_place</th>\n",
       "      <th>MR_Other_yr</th>\n",
       "      <th>Other_belt</th>\n",
       "      <th>Num_Other_medals</th>\n",
       "      <th>MR_Other_place</th>\n",
       "      <th>Birthdate</th>\n",
       "      <th>Belt_date</th>\n",
       "      <th>Birthdate_DT</th>\n",
       "      <th>Belt_date_DT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men -66kg</td>\n",
       "      <td>Championship</td>\n",
       "      <td>Semi-Finals</td>\n",
       "      <td>Paulo Miyao</td>\n",
       "      <td>Augusto \"Tanquinho\" Mendes</td>\n",
       "      <td>0</td>\n",
       "      <td>l</td>\n",
       "      <td>ref decision</td>\n",
       "      <td>15:00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>black</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1991-05-11 00:00:00</td>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>1991-05-11</td>\n",
       "      <td>2013-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men -77kg</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Garry Tonon</td>\n",
       "      <td>Dante Leon</td>\n",
       "      <td>0</td>\n",
       "      <td>w</td>\n",
       "      <td>submission</td>\n",
       "      <td>5:22</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>black</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1991-09-12 00:00:00</td>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>1991-09-12</td>\n",
       "      <td>2013-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>Men -88kg</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Consolation</td>\n",
       "      <td>Josh Hinger</td>\n",
       "      <td>Jon Blank</td>\n",
       "      <td>3</td>\n",
       "      <td>w</td>\n",
       "      <td>score</td>\n",
       "      <td>10:00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>black</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1982-05-28 00:00:00</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>1982-05-28</td>\n",
       "      <td>2013-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Weight  Championship        Round      Fighter  \\\n",
       "0   Male  Men -66kg  Championship  Semi-Finals  Paulo Miyao   \n",
       "1   Male  Men -77kg   Consolation  Consolation  Garry Tonon   \n",
       "2   Male  Men -88kg   Consolation  Consolation  Josh Hinger   \n",
       "\n",
       "                     Opponant  Points Result           How   Time  ...  \\\n",
       "0  Augusto \"Tanquinho\" Mendes       0      l  ref decision  15:00  ...   \n",
       "1                  Dante Leon       0      w    submission   5:22  ...   \n",
       "2                   Jon Blank       3      w         score  10:00  ...   \n",
       "\n",
       "  Num_IBJJF_medals  MR_IBJJF_place MR_Other_yr Other_belt Num_Other_medals  \\\n",
       "0              6.0             1.0      2019.0      black              6.0   \n",
       "1              1.0             3.0      2016.0      black              4.0   \n",
       "2              4.0             1.0      2017.0      black              9.0   \n",
       "\n",
       "  MR_Other_place            Birthdate  Belt_date Birthdate_DT Belt_date_DT  \n",
       "0            1.0  1991-05-11 00:00:00 2013-06-01   1991-05-11   2013-06-01  \n",
       "1            1.0  1991-09-12 00:00:00 2013-09-01   1991-09-12   2013-09-01  \n",
       "2            1.0  1982-05-28 00:00:00 2013-12-01   1982-05-28   2013-12-01  \n",
       "\n",
       "[3 rows x 63 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Weight', 'Championship', 'Round', 'Fighter', 'Opponant',\n",
       "       'Points', 'Result', 'How', 'Time', 'Day', 'Match No', 'Place',\n",
       "       'Entrance', 'Gym', 'Belt', 'Medal_ADCC', 'ADCC_place', 'ADCC_Year',\n",
       "       'ADCC_years_competed', 'Num_ADCC_comp', 'Trails_winner', 'Trials_Year',\n",
       "       'Trails_place', 'Highest_ADCC_place', 'Max_ADCC_medals',\n",
       "       'MR_ADCC_Medal_yr', 'Highest_Trials_place', 'Max_tr_medals',\n",
       "       'MR_tr_Medal_yr', 'age_float', 'blackbelt_float', 'Results', 'RoundNo',\n",
       "       'Min', 'Sec', 'Seconds', 'KGWeight', 'How_weighted', 'How_weighted_2',\n",
       "       'Result_score', 'Final_WL_Score', 'Place_score', 'Place_num',\n",
       "       'IBJJF_world', 'IBJJF_majors', 'Other_tours', 'MR_IBJJF_W_yr',\n",
       "       'IBJJF_W_belt', 'Num_IBJJF_W_medals', 'MR_IBJJF_W_place', 'MR_IBJJF_yr',\n",
       "       'IBJJF_belt', 'Num_IBJJF_medals', 'MR_IBJJF_place', 'MR_Other_yr',\n",
       "       'Other_belt', 'Num_Other_medals', 'MR_Other_place', 'Birthdate',\n",
       "       'Belt_date', 'Birthdate_DT', 'Belt_date_DT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original data from the ADCC tournament contained multiple features for how the matches concluded. For the prediction model, I dropped all match outcome data that would lead to target data leakage. We will keep only fighter history, ADCC entrance info, and results and how the match concluded (two targets) for the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[['Results', #'How', #target\n",
    "          'Gender','Weight','Fighter','Opponant','Entrance','Gym', #match entrace data\n",
    "          'Belt','age_float','blackbelt_float', #fighter info\n",
    "          'Num_ADCC_comp','Highest_ADCC_place','Max_ADCC_medals','MR_ADCC_Medal_yr','Highest_Trials_place',\n",
    "          'Max_tr_medals','MR_tr_Medal_yr',\n",
    "          #'IBJJF_world', 'IBJJF_majors','Other_tours', \n",
    "          'MR_IBJJF_W_yr', 'IBJJF_W_belt', 'Num_IBJJF_W_medals','MR_IBJJF_W_place', 'MR_IBJJF_yr', 'IBJJF_belt', \n",
    "          'Num_IBJJF_medals','MR_IBJJF_place', 'MR_Other_yr', 'Other_belt', 'Num_Other_medals','MR_Other_place'\n",
    "         ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 28)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we fill missing values with a zero because if the fighters do not have a medal in past tournaments, they get a score of zero\n",
    "df1.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df1.Num_ADCC_comp= df1.Num_ADCC_comp.astype(int)\n",
    "df1.Highest_ADCC_place= df1.Highest_ADCC_place.astype(int)\n",
    "df1.Max_ADCC_medals= df1.Max_ADCC_medals.astype(int)\n",
    "df1.MR_ADCC_Medal_yr= df1.MR_ADCC_Medal_yr.astype(int)\n",
    "df1.Highest_Trials_place= df1.Highest_Trials_place.astype(int)\n",
    "df1.Max_tr_medals= df1.Max_tr_medals.astype(int)\n",
    "df1.MR_tr_Medal_yr= df1.MR_tr_Medal_yr.astype(int)\n",
    "\n",
    "df1.MR_IBJJF_W_yr= df1.MR_IBJJF_W_yr.astype(int)\n",
    "df1.Num_IBJJF_W_medals= df1.Num_IBJJF_W_medals.astype(int)\n",
    "df1.MR_IBJJF_W_place= df1.MR_IBJJF_W_place.astype(int)\n",
    "\n",
    "df1.MR_IBJJF_yr= df1.MR_IBJJF_yr.astype(int)\n",
    "df1.Num_IBJJF_medals= df1.Num_IBJJF_medals.astype(int)\n",
    "df1.MR_IBJJF_place= df1.MR_IBJJF_place.astype(int)\n",
    "df1.MR_Other_yr= df1.MR_Other_yr.astype(int)\n",
    "df1.Num_Other_medals= df1.Num_Other_medals.astype(int)\n",
    "df1.MR_Other_place= df1.MR_Other_place.astype(int)\n",
    "\n",
    "df1.age_float= df1.age_float.astype(int)\n",
    "df1.blackbelt_float= df1.blackbelt_float.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.Gym= df1.Gym.astype(str)\n",
    "df1.Belt= df1.Belt.astype(str)\n",
    "df1.Entrance= df1.Entrance.astype(str)\n",
    "df1.IBJJF_W_belt= df1.IBJJF_W_belt.astype(str)\n",
    "df1.IBJJF_belt= df1.IBJJF_belt.astype(str)\n",
    "df1.Other_belt= df1.Other_belt.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Results\n",
       "0        0\n",
       "1        1\n",
       "2        1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label encode the target column\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "target = ['Results']\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# Apply the label encoder to each column\n",
    "encoded = df1[target].apply(encoder.fit_transform)\n",
    "encoded.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# segment categorical features\n",
    "\n",
    "cat_features = ['Gender','Weight','Fighter','Opponant',\n",
    "       'Entrance','Gym','Belt','IBJJF_W_belt', 'IBJJF_belt','Other_belt',\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#ohe with pandas get_dummies for categorical features, drop the target\n",
    "df1.drop(columns=['Results'], inplace=True)\n",
    "data = pd.get_dummies(df1,prefix_sep=\"__\",\n",
    "                              columns=cat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>age_float</th>\n",
       "      <th>blackbelt_float</th>\n",
       "      <th>Num_ADCC_comp</th>\n",
       "      <th>Highest_ADCC_place</th>\n",
       "      <th>Max_ADCC_medals</th>\n",
       "      <th>MR_ADCC_Medal_yr</th>\n",
       "      <th>Highest_Trials_place</th>\n",
       "      <th>Max_tr_medals</th>\n",
       "      <th>MR_tr_Medal_yr</th>\n",
       "      <th>MR_IBJJF_W_yr</th>\n",
       "      <th>...</th>\n",
       "      <th>IBJJF_W_belt__brown</th>\n",
       "      <th>IBJJF_W_belt__purple</th>\n",
       "      <th>IBJJF_belt__0</th>\n",
       "      <th>IBJJF_belt__black</th>\n",
       "      <th>IBJJF_belt__brown</th>\n",
       "      <th>IBJJF_belt__purple</th>\n",
       "      <th>Other_belt__0</th>\n",
       "      <th>Other_belt__black</th>\n",
       "      <th>Other_belt__brown</th>\n",
       "      <th>Other_belt__purple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 296 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_float  blackbelt_float  Num_ADCC_comp  Highest_ADCC_place  \\\n",
       "0         28                6              1                   3   \n",
       "1         28                6              4                   0   \n",
       "2         37                6              1                   0   \n",
       "3         27                4              1                   0   \n",
       "4         29                9              3                   1   \n",
       "\n",
       "   Max_ADCC_medals  MR_ADCC_Medal_yr  Highest_Trials_place  Max_tr_medals  \\\n",
       "0                1              2019                     0              0   \n",
       "1                0              2019                     0              0   \n",
       "2                0              2019                     1              1   \n",
       "3                0              2019                     0              0   \n",
       "4                4              2017                     0              0   \n",
       "\n",
       "   MR_tr_Medal_yr  MR_IBJJF_W_yr  ...  IBJJF_W_belt__brown  \\\n",
       "0               0           2019  ...                    0   \n",
       "1               0              0  ...                    0   \n",
       "2            2019              0  ...                    0   \n",
       "3               0           2018  ...                    0   \n",
       "4               0           2019  ...                    0   \n",
       "\n",
       "   IBJJF_W_belt__purple  IBJJF_belt__0  IBJJF_belt__black  IBJJF_belt__brown  \\\n",
       "0                     0              0                  1                  0   \n",
       "1                     0              0                  1                  0   \n",
       "2                     0              0                  1                  0   \n",
       "3                     0              0                  1                  0   \n",
       "4                     0              0                  1                  0   \n",
       "\n",
       "   IBJJF_belt__purple  Other_belt__0  Other_belt__black  Other_belt__brown  \\\n",
       "0                   0              0                  1                  0   \n",
       "1                   0              0                  1                  0   \n",
       "2                   0              0                  1                  0   \n",
       "3                   0              0                  1                  0   \n",
       "4                   0              0                  1                  0   \n",
       "\n",
       "   Other_belt__purple  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "\n",
       "[5 rows x 296 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age_float', 'blackbelt_float', 'Num_ADCC_comp', 'Highest_ADCC_place',\n",
       "       'Max_ADCC_medals', 'MR_ADCC_Medal_yr', 'Highest_Trials_place',\n",
       "       'Max_tr_medals', 'MR_tr_Medal_yr', 'MR_IBJJF_W_yr',\n",
       "       ...\n",
       "       'IBJJF_W_belt__brown', 'IBJJF_W_belt__purple', 'IBJJF_belt__0',\n",
       "       'IBJJF_belt__black', 'IBJJF_belt__brown', 'IBJJF_belt__purple',\n",
       "       'Other_belt__0', 'Other_belt__black', 'Other_belt__brown',\n",
       "       'Other_belt__purple'],\n",
       "      dtype='object', length=296)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from sklearn import preprocessing\n",
    "# le = preprocessing.LabelEncoder()\n",
    "\n",
    "# #encode all column features into array\n",
    "# for col in df1.columns:\n",
    "#     df1[col]=le.fit_transform(df1[col])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StandardScaler(copy=True, with_mean=True, with_std=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "print(scaler.fit(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(scaler.mean_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.03265331  0.31041175 -0.67379752 ...  0.74023321 -0.1159867\n",
      "  -0.09449112]\n",
      " [ 0.03265331  0.31041175  1.51201453 ...  0.74023321 -0.1159867\n",
      "  -0.09449112]\n",
      " [ 1.87756512  0.31041175 -0.67379752 ...  0.74023321 -0.1159867\n",
      "  -0.09449112]\n",
      " ...\n",
      " [-0.7873075  -0.86271627 -0.67379752 ...  0.74023321 -0.1159867\n",
      "  -0.09449112]\n",
      " [ 0.44263371  0.07578615 -0.67379752 ...  0.74023321 -0.1159867\n",
      "  -0.09449112]\n",
      " [ 0.03265331 -1.09734187 -1.40240154 ... -1.35092561 -0.1159867\n",
      "  -0.09449112]]\n"
     ]
    }
   ],
   "source": [
    "print(scaler.transform(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = encoded #data['Results']\n",
    "X = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 296)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(226, 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X.shape)\n",
    "display(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25,random_state=109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training X_train Shape: (169, 296)\n",
      "Training y_train Shape: (169, 1)\n",
      "Testing X_test Shape: (57, 296)\n",
      "Testing y_test Shape: (57, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Training X_train Shape:', X_train.shape)\n",
    "print('Training y_train Shape:', y_train.shape)\n",
    "print('Testing X_test Shape:', X_test.shape)\n",
    "print('Testing y_test Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "    \n",
    "### Target 1: Results (Win/Loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.56\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "neighbors_settings = range (1,11)\n",
    "\n",
    "for n_neighbors in neighbors_settings:\n",
    "    knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    knn.fit(X_train, y_train)\n",
    "    training_accuracy.append(knn.score(X_train, y_train))\n",
    "    test_accuracy.append(knn.score(X_test, y_test))\n",
    "\n",
    "plt.plot(neighbors_settings, training_accuracy, label='training_accuracy')\n",
    "plt.plot(neighbors_settings, test_accuracy, label='test_accuracy')\n",
    "plt.xlabel('n_neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "Best neighbors is 5 to 6 to avoid over and underfitting. The model's performance is 61% using KNN. However, with one-hot encoding, the dataset is has 296 features and a handful have 0 most of the time (sparse dataset). We use the KNN accuracy as the baseline, but continue to compare models below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.61\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=6)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "predicted= clf.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.543859649122807\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.46      0.50        28\n",
      "           1       0.55      0.62      0.58        29\n",
      "\n",
      "    accuracy                           0.54        57\n",
      "   macro avg       0.54      0.54      0.54        57\n",
      "weighted avg       0.54      0.54      0.54        57\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, 13],\n",
       "       [14, 16]], dtype=int64)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validating \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "y_pred_cv = cross_val_predict(clf, X_test, y_test, cv=5)\n",
    "\n",
    "confusion_matrix(y_pred_cv, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.66666667 0.83333333 0.5        0.5        0.66666667 0.66666667\n",
      " 0.5        0.33333333 0.33333333 0.5        0.5        0.83333333\n",
      " 0.5        0.33333333 0.66666667 0.66666667 0.5        0.5\n",
      " 0.5        1.         0.5        0.5        0.66666667 0.66666667\n",
      " 0.4        0.5        0.5        0.75       0.5        0.        ]\n",
      "Accuracy: 0.55 (+/- 0.36)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cv_score = cross_val_score(clf, X_train, y_train, cv = 30)\n",
    "print(cv_score)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (cv_score.mean(), cv_score.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.51 (+/- 0.27)\n"
     ]
    }
   ],
   "source": [
    "#Let's test the entire dataset again.\n",
    "scores = cross_val_score(clf, X, y, cv=10)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training X_train Shape: (169, 296)\n",
      "Training y_train Shape: (169, 1)\n",
      "Testing X_test Shape: (57, 296)\n",
      "Testing y_test Shape: (57, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25,random_state=109) \n",
    "# 75% training and 25% test\n",
    "\n",
    "print('Training X_train Shape:', X_train.shape)\n",
    "print('Training y_train Shape:', y_train.shape)\n",
    "print('Testing X_test Shape:', X_test.shape)\n",
    "print('Testing y_test Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "lr_predict =lr.predict(X_test)\n",
    "\n",
    "lr_predict_prob = lr.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15 13]\n",
      " [10 19]]\n",
      "0.5964912280701754\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "lr_conf_matrix = confusion_matrix(y_test, lr_predict)\n",
    "lr_accuracy = accuracy_score(y_test, lr_predict)\n",
    "print(lr_conf_matrix)\n",
    "print(lr_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.54      0.57        28\n",
      "           1       0.59      0.66      0.62        29\n",
      "\n",
      "    accuracy                           0.60        57\n",
      "   macro avg       0.60      0.60      0.59        57\n",
      "weighted avg       0.60      0.60      0.59        57\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test, lr_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 13],\n",
       "       [13, 16]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validation \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "y_pred_cv = cross_val_predict(lr, X_test, y_test, cv=5)\n",
    "\n",
    "confusion_matrix(y_pred_cv, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.66666667 0.66666667 0.66666667 0.5        0.66666667 0.66666667\n",
      " 0.5        0.33333333 0.5        0.66666667 0.5        0.5\n",
      " 0.5        0.33333333 0.66666667 0.83333333 0.5        0.66666667\n",
      " 0.5        0.83333333 0.33333333 0.66666667 0.66666667 0.66666667\n",
      " 0.4        0.75       0.5        1.         0.75       0.        ]\n",
      "Accuracy: 0.58 (+/- 0.37)\n"
     ]
    }
   ],
   "source": [
    "#cross validation score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cv_score = cross_val_score(lr, X_train, y_train, cv = 30)\n",
    "print(cv_score)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (cv_score.mean(), cv_score.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.50 (+/- 0.20)\n"
     ]
    }
   ],
   "source": [
    "# accuracy on entire dataset\n",
    "scores = cross_val_score(lr, X, y, cv=10)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5964912280701754\n",
      "0.59375\n",
      "0.6551724137931034\n",
      "0.6229508196721311\n"
     ]
    }
   ],
   "source": [
    "# accuracy, precision, recall, and f1 scoring for LR\n",
    "from sklearn import metrics\n",
    "print(metrics.accuracy_score(y_test, lr_predict))\n",
    "print(metrics.precision_score(y_test, lr_predict))\n",
    "print(metrics.recall_score(y_test, lr_predict))\n",
    "print(metrics.f1_score(y_test, lr_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Precision: tp / (tp + fp) - The number of correct positive results divided by the number of all positive results returned by the classifier. \n",
    "- Recall: tp / (tp + fn) - The number of correct positive results divided by the number of all samples that should have been identified as positive.\n",
    "- The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "Compare to LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy Score: 0.60\n",
      "Testing Accuracy Score: 0.63\n"
     ]
    }
   ],
   "source": [
    "# compare logistic regression to linear support vector machine classifiers\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "clf = LinearSVC(random_state=0, tol=1e-5)\n",
    "clf.fit(X_train, y_train)\n",
    "print(\"Training Accuracy Score: {:.2f}\".format(clf.score(X_train, y_train)))\n",
    "print(\"Testing Accuracy Score: {:.2f}\".format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "Cross Validation for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5666666666666667\n"
     ]
    }
   ],
   "source": [
    "# try k-fold cross-validator\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kfold = KFold(n_splits=3, random_state=7)\n",
    "\n",
    "result = cross_val_score(lr, X, y, cv=kfold, scoring='accuracy')\n",
    "print(result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "dual=[True,False]\n",
    "max_iter=[100,110,120,130,140]\n",
    "C = [0.1,1.0,1.5,2.0,4,5,10]\n",
    "param_grid = dict(dual=dual,max_iter=max_iter,C=C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.592920 using {'C': 1.5, 'dual': True, 'max_iter': 110}\n",
      "Execution time: 6.007554531097412 ms\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "lr = LogisticRegression(penalty='l2')\n",
    "grid = GridSearchCV(estimator=lr, param_grid=param_grid, cv = 3, n_jobs=-1)\n",
    "\n",
    "start_time = time.time()\n",
    "grid_result = grid.fit(X, y)\n",
    "# Summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "print(\"Execution time: \" + str((time.time() - start_time)) + ' ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.584071 using {'max_iter': 110, 'dual': True, 'C': 10}\n",
      "Execution time: 0.29690980911254883 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# lr = LogisticRegression(penalty='l2')\n",
    "random = RandomizedSearchCV(estimator=lr, param_distributions=param_grid, cv = 3, n_jobs=-1)\n",
    "\n",
    "start_time = time.time()\n",
    "random_result = random.fit(X, y)\n",
    "# Summarize results\n",
    "print(\"Best: %f using %s\" % (random_result.best_score_, random_result.best_params_))\n",
    "print(\"Execution time: \" + str((time.time() - start_time)) + ' ms')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "The above two cross validation searches aren't stable and continue to produce different accuracy scores. I switch to only searching for the regularization penalty and regularization hyperparameter C. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy Score: 0.99\n",
      "Testing Accuracy Score: 0.65\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(penalty='l2',C=5)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "print(\"Training Accuracy Score: {:.2f}\".format(lr.score(X_train, y_train)))\n",
    "print(\"Testing Accuracy Score: {:.2f}\".format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "# Create regularization penalty space\n",
    "penalty = ['l1', 'l2']\n",
    "\n",
    "# Create regularization hyperparameter space\n",
    "C = [0.1,1.0,1.5,2.0,4,5,10]\n",
    "\n",
    "hyperparameters = dict(C=C, penalty=penalty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Penalty: l1\n",
      "Best C: 0.1\n"
     ]
    }
   ],
   "source": [
    "grid_2 = GridSearchCV(lr, hyperparameters, cv=3, verbose=0)\n",
    "best_model = grid_2.fit(X, y)\n",
    "print('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])\n",
    "print('Best C:', best_model.best_estimator_.get_params()['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "    \n",
    "#### Final Model Selection - Logistic Regression with a l1 penalty and 0.1 C regularlization hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy Score: 0.66\n",
      "Testing Accuracy Score: 0.70\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(penalty='l1', C=0.1)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "print(\"Training Accuracy Score: {:.2f}\".format(lr.score(X_train, y_train)))\n",
    "print(\"Testing Accuracy Score: {:.2f}\".format(lr.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "Feature Coefficients for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = list(data)\n",
    "# list(df)\n",
    "# my_dataframe.columns.values.tolist()\n",
    "# my_dataframe.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.66191013e-01,  8.95827603e-02,  4.18353819e-01,\n",
       "         3.81597149e-01, -3.05797077e-01,  1.25417677e-04,\n",
       "         8.59844316e-01,  1.26131633e-01, -1.21329220e-03,\n",
       "         5.84034423e-04, -1.04561220e-01, -4.59490140e-01,\n",
       "         1.86145582e-03, -1.38578913e-01, -2.58370580e-01,\n",
       "         8.20695383e-04, -1.84097746e-03, -7.12963389e-01,\n",
       "         6.38540300e-03,  4.35933747e-01, -2.87768680e-01,\n",
       "         2.26877565e-01, -7.31374977e-02, -5.51944718e-01,\n",
       "         6.92596719e-01, -3.26910279e-02,  4.62001387e-01,\n",
       "        -1.37874720e-01,  1.44260123e-01,  7.08215626e-01,\n",
       "         8.11473173e-01, -6.73554713e-01,  0.00000000e+00,\n",
       "        -7.22140616e-02,  8.38999796e-01, -3.81745767e-01,\n",
       "         4.08815749e-01, -5.95867292e-01, -1.56833516e-01,\n",
       "         0.00000000e+00,  7.03846388e-01,  0.00000000e+00,\n",
       "         5.71235688e-01, -3.91402380e-01,  5.17298289e-01,\n",
       "         7.83318437e-01, -6.62607382e-02, -1.01245006e-01,\n",
       "         1.94386681e-01, -8.47453354e-01, -1.76593786e-01,\n",
       "        -2.23100226e-01,  5.91740456e-01, -3.76998410e-01,\n",
       "         0.00000000e+00,  6.64780531e-01,  9.36147646e-01,\n",
       "        -7.53666529e-01, -1.50747869e-01, -7.33895525e-01,\n",
       "         0.00000000e+00, -7.44365393e-01,  5.55560657e-01,\n",
       "        -1.39817145e-01,  6.21222224e-01, -3.85971941e-01,\n",
       "         6.60045414e-01, -7.89323064e-01, -4.44798746e-01,\n",
       "        -9.84131133e-01, -4.39024261e-01, -2.03032966e-01,\n",
       "         3.11001794e-02, -2.22872346e-01,  3.91301850e-01,\n",
       "        -1.36776337e-01, -6.35650376e-02, -2.42242560e-01,\n",
       "        -6.82935037e-02, -1.79969960e-01,  4.38003407e-01,\n",
       "        -1.76510410e-01,  5.08421562e-01,  0.00000000e+00,\n",
       "         1.24691597e-01, -1.96910702e-01, -9.99664392e-02,\n",
       "        -4.67122749e-02, -1.79099927e-01,  9.76622326e-01,\n",
       "         6.53639636e-01,  0.00000000e+00, -1.31767715e+00,\n",
       "        -1.40021364e-01, -6.69917530e-02, -6.09180927e-01,\n",
       "         9.18186877e-01,  1.05681402e-01, -2.18299525e-01,\n",
       "         9.54419138e-01, -8.00642723e-01,  4.59138869e-01,\n",
       "        -5.74509320e-01, -2.49539549e-01,  1.77217076e-01,\n",
       "        -2.95572419e-02, -2.03580685e-01, -7.25999948e-02,\n",
       "         1.44854899e-01, -2.07528423e-01, -4.36415532e-01,\n",
       "         0.00000000e+00, -3.98726373e-02, -3.79080672e-01,\n",
       "        -1.26427167e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "         2.02680682e-02, -6.59270717e-02,  3.56091439e-01,\n",
       "         3.99290067e-01,  3.61261026e-01, -6.90347934e-01,\n",
       "         5.55180503e-01,  1.27048818e+00, -4.94359534e-01,\n",
       "         1.96896822e-01,  7.12971736e-01, -6.13686691e-01,\n",
       "         9.51712322e-01,  8.68678099e-01,  7.23201767e-01,\n",
       "        -3.76998410e-01,  3.47454555e-01, -3.27849522e+00,\n",
       "         1.25375243e+00,  2.18063307e-01,  0.00000000e+00,\n",
       "        -1.03236928e+00,  0.00000000e+00, -1.43427232e+00,\n",
       "         0.00000000e+00,  4.53471525e-01,  1.75567434e-02,\n",
       "        -2.72589322e-01,  0.00000000e+00, -2.22872346e-01,\n",
       "         8.95733183e-01,  2.43420389e-01,  1.96896822e-01,\n",
       "        -1.21185204e+00,  8.38999796e-01,  0.00000000e+00,\n",
       "        -2.07528423e-01, -2.26156021e+00,  0.00000000e+00,\n",
       "        -6.73554713e-01, -1.34514711e+00,  1.00989361e+00,\n",
       "         1.10518114e+00, -3.51658123e+00,  0.00000000e+00,\n",
       "        -2.64581262e+00,  3.59584494e-01,  2.55412083e-01,\n",
       "         1.89012694e+00,  9.28428759e-01,  8.24788457e-01,\n",
       "         7.60369086e-01,  9.83823621e-01,  1.19699924e+00,\n",
       "         8.65541132e-01, -1.08639629e+00, -2.78129922e+00,\n",
       "         4.76686221e-01,  1.81132493e+00, -1.59457172e+00,\n",
       "         3.83497333e-01, -2.73063704e+00,  0.00000000e+00,\n",
       "        -1.37548088e+00,  8.65541132e-01,  1.05950125e+00,\n",
       "        -8.90592398e-01, -2.82145441e-01,  0.00000000e+00,\n",
       "         1.44175400e+00, -1.76363784e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  8.11473173e-01,  9.33591635e-01,\n",
       "         1.12003095e+00,  7.57029926e-01, -1.66811329e+00,\n",
       "         3.83497333e-01,  1.06557561e+00,  7.63899025e-01,\n",
       "         1.16588747e+00,  5.49305983e-01,  5.39404538e-01,\n",
       "         7.23813128e-01, -1.11427228e+00,  3.36436869e-02,\n",
       "         5.64759624e-01,  4.54756144e-01, -2.17429988e-01,\n",
       "         4.13828681e-01,  9.17296287e-02,  1.26478564e-01,\n",
       "         3.59925885e-01,  1.25927872e+00,  0.00000000e+00,\n",
       "         1.21064784e+00,  2.33516881e-01,  1.50742680e-01,\n",
       "         1.05021997e+00,  7.53550559e-01, -5.37118874e-01,\n",
       "        -1.33481403e+00,  0.00000000e+00,  6.04149311e-02,\n",
       "        -2.25247060e+00, -4.13998572e-01,  6.12488909e-02,\n",
       "         6.95978857e-01,  1.14005095e+00, -1.32641178e+00,\n",
       "         8.38999796e-01, -9.06298671e-01,  5.59275598e-01,\n",
       "         1.22132184e+00,  9.63965925e-01,  7.03846388e-01,\n",
       "        -5.74509320e-01, -4.67122749e-02,  5.04139286e-01,\n",
       "        -2.03032966e-01,  0.00000000e+00,  1.96896822e-01,\n",
       "        -2.62838742e-01, -9.99664392e-02,  6.64780531e-01,\n",
       "        -6.62124487e-01, -1.19269054e+00,  5.71235688e-01,\n",
       "         5.81311489e-01, -3.81745767e-01, -1.56089604e+00,\n",
       "        -3.80620624e-01, -2.42242560e-01, -2.03580685e-01,\n",
       "        -1.76593786e-01, -3.79080672e-01,  0.00000000e+00,\n",
       "        -1.76510410e-01, -1.40021364e-01, -1.79099927e-01,\n",
       "         1.44854899e-01,  1.77217076e-01, -2.07528423e-01,\n",
       "         6.27233884e-01, -2.34142561e-01, -6.09180927e-01,\n",
       "         0.00000000e+00, -1.56833516e-01,  5.17298289e-01,\n",
       "         4.08815749e-01,  4.14522298e-01, -4.94359534e-01,\n",
       "        -1.79969960e-01, -6.69917530e-02,  7.08215626e-01,\n",
       "         3.61261026e-01, -7.25999948e-02,  4.62001387e-01,\n",
       "        -4.67078572e-01,  1.74366648e-01,  9.18186877e-01,\n",
       "         3.99290067e-01, -4.51260881e-01, -2.14105703e-01,\n",
       "        -3.79080672e-01,  5.10797624e-01,  3.39946413e-01,\n",
       "         9.54419138e-01,  3.22906099e-01, -1.68575012e+00,\n",
       "         5.10363844e-01,  2.86797839e-01, -4.64606257e-01,\n",
       "         1.09763724e-01,  4.42984659e-01,  1.80318353e-01,\n",
       "        -2.86665263e-01,  1.05681402e-01]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array([cols[1:-1]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "feature_importance = abs(lr.coef_[0])\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "\n",
    "featfig = plt.figure(figsize=(10,25))\n",
    "featax = featfig.add_subplot(1, 1, 1)\n",
    "featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "featax.set_yticks(pos)\n",
    "featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=8)\n",
    "featax.set_xlabel('Relative Feature Importance')\n",
    "\n",
    "plt.tight_layout()   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15 13]\n",
      " [15 14]]\n",
      "0.5087719298245614\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(max_depth=10)\n",
    "\n",
    "dt.fit(X_train,y_train)\n",
    "dt_predict = dt.predict(X_test)\n",
    "dt_predict_prob = dt.predict_proba(X_test)\n",
    "\n",
    "dt_conf_matrix = confusion_matrix(y_test, dt_predict)\n",
    "dt_accuracy_score = accuracy_score(y_test, dt_predict)\n",
    "\n",
    "print(dt_conf_matrix)\n",
    "print(dt_accuracy_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12 16]\n",
      " [ 9 20]]\n",
      "0.5614035087719298\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "gnb_predict = gnb.predict(X_test)\n",
    "gnb_predict_prob = gnb.predict_proba(X_test)\n",
    "\n",
    "gnb_conf_matrix = confusion_matrix(y_test, gnb_predict)\n",
    "gnb_accuracy_score = accuracy_score(y_test, gnb_predict)\n",
    "\n",
    "print(gnb_conf_matrix)\n",
    "print(gnb_accuracy_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### N-Estimators for Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False, random_state=0,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30, 50, 100]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='f1', verbose=0)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "{'n_estimators': [3, 10, 30, 50, 100], 'max_features': [2, 4, 6, 8]},\n",
    "]\n",
    "\n",
    "grid_search = GridSearchCV(clf, param_grid, cv=10, scoring='f1')\n",
    "\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 3}"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_score=[]\n",
    "for name, score in zip(X_train.columns, grid_search.best_estimator_.feature_importances_):\n",
    "    feat_score.append([name, score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age_float', 0.04737508422287202]\n",
      "['blackbelt_float', 0.045013300884638195]\n",
      "['Num_IBJJF_W_medals', 0.03626682168530549]\n",
      "['Opponant__Gordon Ryan', 0.03381717727234492]\n",
      "['MR_IBJJF_yr', 0.030625324235638435]\n",
      "['Max_tr_medals', 0.02632078177360674]\n",
      "['Weight__Absolute', 0.022353448670871]\n",
      "['MR_ADCC_Medal_yr', 0.02211895453042463]\n",
      "['Gender__Male', 0.02122705031781838]\n",
      "['Opponant__Gabi Garcia', 0.018808570570304346]\n",
      "['Max_ADCC_medals', 0.018604667122464055]\n",
      "['Num_ADCC_comp', 0.01834375038066462]\n",
      "['MR_tr_Medal_yr', 0.017877301723368443]\n",
      "['Opponant__Augusto \"Tanquinho\" Mendes', 0.017090105237957915]\n",
      "['Opponant__Garry Tonon', 0.016121195838724272]\n",
      "['Belt__brown', 0.015644381625934626]\n",
      "['MR_Other_place', 0.015254115323263527]\n",
      "['Opponant__Matheus Diniz', 0.014080985366998588]\n",
      "['Opponant__Aaron \"Tex\" Johnson', 0.014022668716193515]\n",
      "['IBJJF_belt__black', 0.013840460957451372]\n",
      "['MR_IBJJF_W_yr', 0.013217782621060282]\n",
      "['MR_Other_yr', 0.011919763266032392]\n",
      "['Fighter__Edwin Najmi', 0.011765327247769512]\n",
      "['Opponant__Mahamed Aly', 0.011316864408208643]\n",
      "['Opponant__Kennedy Maciel', 0.011307890206972778]\n",
      "['Opponant__Carina Santi', 0.01111925990501273]\n",
      "['IBJJF_belt__brown', 0.011069189262581103]\n",
      "['Gym__GF Team', 0.010908496895046077]\n",
      "['MR_IBJJF_place', 0.010645524549431214]\n",
      "['Entrance__Trials Medalists', 0.010193422516421097]\n",
      "['Opponant__John Hansen', 0.009836934760560382]\n",
      "['Gym__CTA', 0.009409458259345242]\n",
      "['Other_belt__black', 0.009268598758028374]\n",
      "['Num_Other_medals', 0.009160689847363483]\n",
      "['Opponant__Josh Hinger', 0.008901136543737262]\n",
      "['Opponant__Bruno Frazatto', 0.008750654861544388]\n",
      "['Opponant__JT Torres', 0.008501585767080683]\n",
      "['Num_IBJJF_medals', 0.008438724035054312]\n",
      "['Opponant__Jena Bishop', 0.008364699270163969]\n",
      "['Opponant__Catherine Perret', 0.008280606600604723]\n",
      "['Weight__Men -77kg', 0.008268561280128893]\n",
      "['Fighter__Andre Galvao', 0.008124220826432419]\n",
      "['Opponant__Ana Carolina Vieira', 0.007622663391843025]\n",
      "['Fighter__Fellipe Andrew', 0.007468763107992396]\n",
      "['Gym__Team Lloyd Irvin', 0.007374459396962518]\n",
      "['Opponant__Lucas \"Hulk\" Barbosa', 0.0069732353205295625]\n",
      "['MR_IBJJF_W_place', 0.006955408116601221]\n",
      "['Opponant__Mateusz Juskowiak', 0.006888848080179019]\n",
      "['Gym__Alliance', 0.006662543291944291]\n",
      "['Opponant__Jon Blank', 0.006389713591248639]\n",
      "['Opponant__Bianca Basilio', 0.0062446557859401905]\n",
      "['Opponant__Dante Leon', 0.00623527942590124]\n",
      "['Opponant__Paulo Miyao', 0.006210831101890851]\n",
      "['Opponant__Murilo Santana', 0.00610955130440921]\n",
      "['Opponant__Amanda Leve', 0.0059043302760893495]\n",
      "['Opponant__Alec Baulding', 0.005901553727640683]\n",
      "['Opponant__Patrick Gaudio', 0.005768689574034609]\n",
      "['IBJJF_belt__0', 0.005656920294652614]\n",
      "['Entrance__Invited', 0.005567000674034314]\n",
      "['Opponant__John Combs', 0.005530100654229278]\n",
      "['Opponant__Nicky Ryan', 0.0054083920680401505]\n",
      "['Fighter__Nick Rodriguez', 0.00518000583087017]\n",
      "['Highest_ADCC_place', 0.005105312351689167]\n",
      "['Fighter__Thomas Halpin', 0.005063234421032587]\n",
      "['Opponant__Jared Dopp', 0.004944787492792679]\n",
      "['Fighter__Mason Fowler', 0.004725685459630413]\n",
      "['Opponant__Max Gimenis', 0.004725685459630413]\n",
      "['Fighter__Dante Leon', 0.004486797697041846]\n",
      "['Opponant__Eliot Marshall', 0.004430330118403513]\n",
      "['Entrance__Super Fight Defending Champ', 0.004430330118403513]\n",
      "['Opponant__Nick Rodriguez', 0.004426165295730513]\n",
      "['Opponant__Perttu Tepponen', 0.004426165295730513]\n",
      "['IBJJF_W_belt__purple', 0.004419032373110701]\n",
      "['Opponant__Kaynan Duarte', 0.004189790420481241]\n",
      "['Gym__Renzo Gracie Academy', 0.004141333871382747]\n",
      "['Fighter__Lachlan Giles', 0.004097590552759043]\n",
      "['Belt__blue', 0.00393807121635868]\n",
      "['Opponant__Fellipe Andrew', 0.003938071216358678]\n",
      "['Opponant__Paulo Jose Barauna', 0.003938071216358678]\n",
      "['Opponant__Roberto \"Cyborg\" Abreu', 0.003938071216358678]\n",
      "['Gym__East Coast BJJ', 0.0039143136352509916]\n",
      "['Weight__Women -60kg', 0.003783047261308131]\n",
      "['Weight__Men +99kg', 0.0037826815044451774]\n",
      "['Opponant__Mateusz Gamrot', 0.003445349556239066]\n",
      "['IBJJF_W_belt__black', 0.003347360533904878]\n",
      "['Opponant__Ruan Alvarenga', 0.003329986690303288]\n",
      "['Opponant__Rustam Chsiev', 0.0032535820975026705]\n",
      "['Opponant__Marcus Buchecha Almeida', 0.003215844689053038]\n",
      "['Fighter__Kaynan Duarte', 0.003065663667944488]\n",
      "['IBJJF_W_belt__0', 0.0030296746409278364]\n",
      "['Opponant__Beatriz Mesquita', 0.003016411384092988]\n",
      "['Fighter__JT Torres', 0.0030147913618056676]\n",
      "['Opponant__Ben Dyson', 0.0029535534122690087]\n",
      "['Opponant__Dan Martinez', 0.0029535534122690087]\n",
      "['Opponant__Edwin Najmi', 0.0029535534122690087]\n",
      "['Opponant__Vinicius Gazola', 0.0029535534122690087]\n",
      "['IBJJF_W_belt__brown', 0.0029535534122690087]\n",
      "['Opponant__Valdir Araujo', 0.0029507768638203417]\n",
      "['Opponant__Victor Hugo', 0.0029507768638203417]\n",
      "['Weight__Men -99kg', 0.0027878640148961843]\n",
      "['Opponant__Mike Perez', 0.0027074239612465897]\n",
      "['Opponant__Elvira Karppinen', 0.002658198071042108]\n",
      "['Gym__Gracie Humaita', 0.0026002264275804873]\n",
      "['Weight__Men -88kg', 0.0025272230120876207]\n",
      "['Gym__Gracie Barra', 0.0023628427298152065]\n",
      "['Opponant__Pablo Mantovani', 0.0022784554894646637]\n",
      "['Entrance__0', 0.002248210943863116]\n",
      "['Highest_Trials_place', 0.0019671845758802275]\n",
      "['Weight__Men -66kg', 0.001889830040481896]\n",
      "['Opponant__Craig Jones', 0.0017721320473614054]\n",
      "['Fighter__Rikako Yuasa', 0.0017580675073029777]\n",
      "['Gym__Team Quest', 0.0017034075902577304]\n",
      "['Fighter__John Hansen', 0.0016620951898830398]\n",
      "['Opponant__Orlando Sanchez', 0.001559094248282472]\n",
      "['IBJJF_belt__purple', 0.001512577353555946]\n",
      "['Gender__Female', 0.0014767767061345043]\n",
      "['Fighter__Ffion Davies', 0.001327700378904025]\n",
      "['Fighter__Ruan Alvarenga', 0.001147678588621726]\n",
      "['Opponant__Ffion Davies', 0.0010491651071361204]\n",
      "['Opponant__Kenta Iwamoto', 0.0009845178040896695]\n",
      "['Opponant__Richard Alarcon', 0.0009845178040896695]\n",
      "['Opponant__James Puopolo', 0.0009835922879401137]\n",
      "['Gym__Absolute MMA', 0.000969371376334445]\n",
      "['Opponant__Renato Canuto', 0.0009276646691884026]\n",
      "['Fighter__Jena Bishop', 0.0007876142432717355]\n",
      "['Opponant__Vinny Magalhaes', 0.0005697440995889419]\n",
      "['Fighter__Perttu Tepponen', 0.0005625816023369521]\n",
      "['Fighter__Eldar Rafigaev', 0.00056205273596578]\n",
      "['Fighter__Jackson Sousa', 0.000492258902044835]\n",
      "['Fighter__Shane Jamil Hill-Taylor', 0.0004828543958978726]\n",
      "['Fighter__Felipe Pena', 0.00042193620175271537]\n",
      "['Fighter__Celso Vinicius', 0.00039380712163586645]\n",
      "['Opponant__Antonio Braga Neto', 0.00035442640947228396]\n",
      "['Weight__Women +60kg', 0.0003375489614021745]\n",
      "['Belt__black', 0.00032817260136322273]\n",
      "['Fighter__Eliot Marshall', 0.00024589807198502827]\n",
      "['Fighter__Pedro Marinho', 0.00024589807198502827]\n",
      "['Gym__Leve Jiu Jitsu', 0.00019671845758802405]\n",
      "['Fighter__Carina Santi', 0.0001695820141015721]\n",
      "['Fighter__Ross Nicholls', 7.032270029212031e-05]\n",
      "['Fighter__Ben Dyson', 4.683772799714877e-05]\n",
      "['Fighter__Yuri Simoes', 2.3440900097367756e-05]\n",
      "['Fighter__Craig Jones', 1.2626345159566368e-06]\n",
      "['Fighter__Matheus Gabriel', 1.8745578909233362e-07]\n",
      "['Weight__Super Fight', 0.0]\n",
      "['Fighter__Aaron \"Tex\" Johnson', 0.0]\n",
      "['Fighter__Adam Wardzinski', 0.0]\n",
      "['Fighter__Alec Baulding', 0.0]\n",
      "['Fighter__Amanda Leve', 0.0]\n",
      "['Fighter__Ana Carolina Vieira', 0.0]\n",
      "['Fighter__Antonio Braga Neto', 0.0]\n",
      "['Fighter__Augusto \"Tanquinho\" Mendes', 0.0]\n",
      "['Fighter__Beatriz Mesquita', 0.0]\n",
      "['Fighter__Ben Hodgkinson', 0.0]\n",
      "['Fighter__Bianca Basilio', 0.0]\n",
      "['Fighter__Bruno Frazatto', 0.0]\n",
      "['Fighter__Catherine Perret', 0.0]\n",
      "['Fighter__DJ Jackson', 0.0]\n",
      "['Fighter__Dan Martinez', 0.0]\n",
      "['Fighter__Elvira Karppinen', 0.0]\n",
      "['Fighter__Gabi Garcia', 0.0]\n",
      "['Fighter__Gabriel Almeida', 0.0]\n",
      "['Fighter__Gabriel Arges', 0.0]\n",
      "['Fighter__Garry Tonon', 0.0]\n",
      "['Fighter__Geo Martinez', 0.0]\n",
      "['Fighter__Gianni Grippo', 0.0]\n",
      "['Fighter__Gordon Ryan', 0.0]\n",
      "['Fighter__Hudson Taylor', 0.0]\n",
      "['Fighter__James Puopolo', 0.0]\n",
      "['Fighter__Jared Dopp', 0.0]\n",
      "['Fighter__Jessica Flowers', 0.0]\n",
      "['Fighter__John Combs', 0.0]\n",
      "['Fighter__Jon Blank', 0.0]\n",
      "['Fighter__Jon Satava', 0.0]\n",
      "['Fighter__Josh Hinger', 0.0]\n",
      "['Fighter__Keith Krikorian', 0.0]\n",
      "['Fighter__Keller Locke-Sodhi', 0.0]\n",
      "['Fighter__Kennedy Maciel', 0.0]\n",
      "['Fighter__Kenta Iwamoto', 0.0]\n",
      "['Fighter__Livia Giles', 0.0]\n",
      "['Fighter__Lucas \"Hulk\" Barbosa', 0.0]\n",
      "['Fighter__Lucas Lepri', 0.0]\n",
      "['Fighter__Mahamed Aly', 0.0]\n",
      "['Fighter__Marcus Buchecha Almeida', 0.0]\n",
      "['Fighter__Mateusz Gamrot', 0.0]\n",
      "['Fighter__Mateusz Juskowiak', 0.0]\n",
      "['Fighter__Matheus Diniz', 0.0]\n",
      "['Fighter__Max Gimenis', 0.0]\n",
      "['Fighter__Mike Perez', 0.0]\n",
      "['Fighter__Mraz Avdoyan', 0.0]\n",
      "['Fighter__Murilo Santana', 0.0]\n",
      "['Fighter__Nathiely de Jesus', 0.0]\n",
      "['Fighter__Nicky Ryan', 0.0]\n",
      "['Fighter__Oliver Taza', 0.0]\n",
      "['Fighter__Orlando Sanchez', 0.0]\n",
      "['Fighter__Pablo Mantovani', 0.0]\n",
      "['Fighter__Patrick Gaudio', 0.0]\n",
      "['Fighter__Paul Ardila-Ibarra', 0.0]\n",
      "['Fighter__Paulo Jose Barauna', 0.0]\n",
      "['Fighter__Paulo Miyao', 0.0]\n",
      "['Fighter__Renato Canuto', 0.0]\n",
      "['Fighter__Richard Alarcon', 0.0]\n",
      "['Fighter__Roberto \"Cyborg\" Abreu', 0.0]\n",
      "['Fighter__Rustam Chsiev', 0.0]\n",
      "['Fighter__Servio Tulio', 0.0]\n",
      "['Fighter__Stephanie Egger', 0.0]\n",
      "['Fighter__Tayane Porfirio', 0.0]\n",
      "['Fighter__Tim Spriggs', 0.0]\n",
      "['Fighter__Tye Ruotolo', 0.0]\n",
      "['Fighter__Vagner Rocha', 0.0]\n",
      "['Fighter__Valdir Araujo', 0.0]\n",
      "['Fighter__Victor Hugo', 0.0]\n",
      "['Fighter__Vinicius Gazola', 0.0]\n",
      "['Fighter__Vinny Magalhaes', 0.0]\n",
      "['Opponant__Adam Wardzinski', 0.0]\n",
      "['Opponant__Andre Galvao', 0.0]\n",
      "['Opponant__Ben Hodgkinson', 0.0]\n",
      "['Opponant__Celso Vinicius', 0.0]\n",
      "['Opponant__DJ Jackson', 0.0]\n",
      "['Opponant__Eldar Rafigaev', 0.0]\n",
      "['Opponant__Felipe Pena', 0.0]\n",
      "['Opponant__Gabriel Almeida', 0.0]\n",
      "['Opponant__Gabriel Arges', 0.0]\n",
      "['Opponant__Geo Martinez', 0.0]\n",
      "['Opponant__Gianni Grippo', 0.0]\n",
      "['Opponant__Hudson Taylor', 0.0]\n",
      "['Opponant__Jackson Sousa', 0.0]\n",
      "['Opponant__Jessica Flowers', 0.0]\n",
      "['Opponant__Jon Satava', 0.0]\n",
      "['Opponant__Keith Krikorian', 0.0]\n",
      "['Opponant__Keller Locke-Sodhi', 0.0]\n",
      "['Opponant__Lachlan Giles', 0.0]\n",
      "['Opponant__Livia Giles', 0.0]\n",
      "['Opponant__Lucas Lepri', 0.0]\n",
      "['Opponant__Mason Fowler', 0.0]\n",
      "['Opponant__Matheus Gabriel', 0.0]\n",
      "['Opponant__Mraz Avdoyan', 0.0]\n",
      "['Opponant__Nathiely de Jesus', 0.0]\n",
      "['Opponant__Oliver Taza', 0.0]\n",
      "['Opponant__Paul Ardila-Ibarra', 0.0]\n",
      "['Opponant__Pedro Marinho', 0.0]\n",
      "['Opponant__Rikako Yuasa', 0.0]\n",
      "['Opponant__Ross Nicholls', 0.0]\n",
      "['Opponant__Servio Tulio', 0.0]\n",
      "['Opponant__Shane Jamil Hill-Taylor', 0.0]\n",
      "['Opponant__Stephanie Egger', 0.0]\n",
      "['Opponant__Tayane Porfirio', 0.0]\n",
      "['Opponant__Thomas Halpin', 0.0]\n",
      "['Opponant__Tim Spriggs', 0.0]\n",
      "['Opponant__Tye Ruotolo', 0.0]\n",
      "['Opponant__Vagner Rocha', 0.0]\n",
      "['Opponant__Yuri Simoes', 0.0]\n",
      "['Entrance__2017 Champion', 0.0]\n",
      "['Gym__10th Planet', 0.0]\n",
      "['Gym__Almeida JJ', 0.0]\n",
      "['Gym__American Top Team (ATT)', 0.0]\n",
      "['Gym__Ankos MMA Poznan', 0.0]\n",
      "['Gym__Atos', 0.0]\n",
      "['Gym__Axios BJJ', 0.0]\n",
      "['Gym__Buddy Gym', 0.0]\n",
      "['Gym__Checkmat', 0.0]\n",
      "['Gym__Dethrone Mixed Martial Arts', 0.0]\n",
      "['Gym__Easton Training Centre', 0.0]\n",
      "['Gym__Fight Sports', 0.0]\n",
      "['Gym__G13', 0.0]\n",
      "['Gym__Gordo JJ', 0.0]\n",
      "['Gym__Grappling Education', 0.0]\n",
      "['Gym__Hilti BJJ', 0.0]\n",
      "['Gym__Jungle BJJ', 0.0]\n",
      "['Gym__K-Dojo', 0.0]\n",
      "['Gym__Maromba', 0.0]\n",
      "['Gym__Matrix Jiu Jitsu team', 0.0]\n",
      "['Gym__Murowana Goslina Team', 0.0]\n",
      "['Gym__One Jiu-Jitsu Academy', 0.0]\n",
      "['Gym__PSLPB Cicero Costha', 0.0]\n",
      "['Gym__Paraestra', 0.0]\n",
      "['Gym__Ribeiro JJ', 0.0]\n",
      "['Gym__Rodrigo Pinheiro', 0.0]\n",
      "['Gym__Roger Gracie Academy', 0.0]\n",
      "['Gym__Roy Dean BJJ', 0.0]\n",
      "['Gym__Ryan Gracie', 0.0]\n",
      "['Gym__Soul Fighters', 0.0]\n",
      "['Gym__Tri-Force Jiu Jitsu Academy', 0.0]\n",
      "['Gym__Unity', 0.0]\n",
      "['Gym__Unity JJ', 0.0]\n",
      "['Gym__VRMMA', 0.0]\n",
      "['Gym__Zenith JJ', 0.0]\n",
      "['Belt__0', 0.0]\n",
      "['Belt__MMA fighter', 0.0]\n",
      "['Belt__blue juvinile', 0.0]\n",
      "['Belt__purple', 0.0]\n",
      "['Belt__wrestler', 0.0]\n",
      "['IBJJF_W_belt__blue', 0.0]\n",
      "['Other_belt__0', 0.0]\n",
      "['Other_belt__brown', 0.0]\n",
      "['Other_belt__purple', 0.0]\n"
     ]
    }
   ],
   "source": [
    "feat_score.sort(reverse=True, key= lambda x:x[1])\n",
    "for char in feat_score:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change f1 scoring to roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False, random_state=0,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30, 50, 100]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "{'n_estimators': [3, 10, 30, 50, 100], 'max_features': [2, 4, 6, 8]},\n",
    "]\n",
    "\n",
    "grid_search = GridSearchCV(clf, param_grid, cv=10, scoring='roc_auc')\n",
    "\n",
    "grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 3}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X, y)\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_score=[]\n",
    "for name, score in zip(X_train.columns, grid_search.best_estimator_.feature_importances_):\n",
    "    feat_score.append([name, score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age_float', 0.04737508422287202]\n",
      "['blackbelt_float', 0.045013300884638195]\n",
      "['Num_IBJJF_W_medals', 0.03626682168530549]\n",
      "['Opponant__Gordon Ryan', 0.03381717727234492]\n",
      "['MR_IBJJF_yr', 0.030625324235638435]\n",
      "['Max_tr_medals', 0.02632078177360674]\n",
      "['Weight__Absolute', 0.022353448670871]\n",
      "['MR_ADCC_Medal_yr', 0.02211895453042463]\n",
      "['Gender__Male', 0.02122705031781838]\n",
      "['Opponant__Gabi Garcia', 0.018808570570304346]\n",
      "['Max_ADCC_medals', 0.018604667122464055]\n",
      "['Num_ADCC_comp', 0.01834375038066462]\n",
      "['MR_tr_Medal_yr', 0.017877301723368443]\n",
      "['Opponant__Augusto \"Tanquinho\" Mendes', 0.017090105237957915]\n",
      "['Opponant__Garry Tonon', 0.016121195838724272]\n",
      "['Belt__brown', 0.015644381625934626]\n",
      "['MR_Other_place', 0.015254115323263527]\n",
      "['Opponant__Matheus Diniz', 0.014080985366998588]\n",
      "['Opponant__Aaron \"Tex\" Johnson', 0.014022668716193515]\n",
      "['IBJJF_belt__black', 0.013840460957451372]\n",
      "['MR_IBJJF_W_yr', 0.013217782621060282]\n",
      "['MR_Other_yr', 0.011919763266032392]\n",
      "['Fighter__Edwin Najmi', 0.011765327247769512]\n",
      "['Opponant__Mahamed Aly', 0.011316864408208643]\n",
      "['Opponant__Kennedy Maciel', 0.011307890206972778]\n",
      "['Opponant__Carina Santi', 0.01111925990501273]\n",
      "['IBJJF_belt__brown', 0.011069189262581103]\n",
      "['Gym__GF Team', 0.010908496895046077]\n",
      "['MR_IBJJF_place', 0.010645524549431214]\n",
      "['Entrance__Trials Medalists', 0.010193422516421097]\n",
      "['Opponant__John Hansen', 0.009836934760560382]\n",
      "['Gym__CTA', 0.009409458259345242]\n",
      "['Other_belt__black', 0.009268598758028374]\n",
      "['Num_Other_medals', 0.009160689847363483]\n",
      "['Opponant__Josh Hinger', 0.008901136543737262]\n",
      "['Opponant__Bruno Frazatto', 0.008750654861544388]\n",
      "['Opponant__JT Torres', 0.008501585767080683]\n",
      "['Num_IBJJF_medals', 0.008438724035054312]\n",
      "['Opponant__Jena Bishop', 0.008364699270163969]\n",
      "['Opponant__Catherine Perret', 0.008280606600604723]\n",
      "['Weight__Men -77kg', 0.008268561280128893]\n",
      "['Fighter__Andre Galvao', 0.008124220826432419]\n",
      "['Opponant__Ana Carolina Vieira', 0.007622663391843025]\n",
      "['Fighter__Fellipe Andrew', 0.007468763107992396]\n",
      "['Gym__Team Lloyd Irvin', 0.007374459396962518]\n",
      "['Opponant__Lucas \"Hulk\" Barbosa', 0.0069732353205295625]\n",
      "['MR_IBJJF_W_place', 0.006955408116601221]\n",
      "['Opponant__Mateusz Juskowiak', 0.006888848080179019]\n",
      "['Gym__Alliance', 0.006662543291944291]\n",
      "['Opponant__Jon Blank', 0.006389713591248639]\n",
      "['Opponant__Bianca Basilio', 0.0062446557859401905]\n",
      "['Opponant__Dante Leon', 0.00623527942590124]\n",
      "['Opponant__Paulo Miyao', 0.006210831101890851]\n",
      "['Opponant__Murilo Santana', 0.00610955130440921]\n",
      "['Opponant__Amanda Leve', 0.0059043302760893495]\n",
      "['Opponant__Alec Baulding', 0.005901553727640683]\n",
      "['Opponant__Patrick Gaudio', 0.005768689574034609]\n",
      "['IBJJF_belt__0', 0.005656920294652614]\n",
      "['Entrance__Invited', 0.005567000674034314]\n",
      "['Opponant__John Combs', 0.005530100654229278]\n",
      "['Opponant__Nicky Ryan', 0.0054083920680401505]\n",
      "['Fighter__Nick Rodriguez', 0.00518000583087017]\n",
      "['Highest_ADCC_place', 0.005105312351689167]\n",
      "['Fighter__Thomas Halpin', 0.005063234421032587]\n",
      "['Opponant__Jared Dopp', 0.004944787492792679]\n",
      "['Fighter__Mason Fowler', 0.004725685459630413]\n",
      "['Opponant__Max Gimenis', 0.004725685459630413]\n",
      "['Fighter__Dante Leon', 0.004486797697041846]\n",
      "['Opponant__Eliot Marshall', 0.004430330118403513]\n",
      "['Entrance__Super Fight Defending Champ', 0.004430330118403513]\n",
      "['Opponant__Nick Rodriguez', 0.004426165295730513]\n",
      "['Opponant__Perttu Tepponen', 0.004426165295730513]\n",
      "['IBJJF_W_belt__purple', 0.004419032373110701]\n",
      "['Opponant__Kaynan Duarte', 0.004189790420481241]\n",
      "['Gym__Renzo Gracie Academy', 0.004141333871382747]\n",
      "['Fighter__Lachlan Giles', 0.004097590552759043]\n",
      "['Belt__blue', 0.00393807121635868]\n",
      "['Opponant__Fellipe Andrew', 0.003938071216358678]\n",
      "['Opponant__Paulo Jose Barauna', 0.003938071216358678]\n",
      "['Opponant__Roberto \"Cyborg\" Abreu', 0.003938071216358678]\n",
      "['Gym__East Coast BJJ', 0.0039143136352509916]\n",
      "['Weight__Women -60kg', 0.003783047261308131]\n",
      "['Weight__Men +99kg', 0.0037826815044451774]\n",
      "['Opponant__Mateusz Gamrot', 0.003445349556239066]\n",
      "['IBJJF_W_belt__black', 0.003347360533904878]\n",
      "['Opponant__Ruan Alvarenga', 0.003329986690303288]\n",
      "['Opponant__Rustam Chsiev', 0.0032535820975026705]\n",
      "['Opponant__Marcus Buchecha Almeida', 0.003215844689053038]\n",
      "['Fighter__Kaynan Duarte', 0.003065663667944488]\n",
      "['IBJJF_W_belt__0', 0.0030296746409278364]\n",
      "['Opponant__Beatriz Mesquita', 0.003016411384092988]\n",
      "['Fighter__JT Torres', 0.0030147913618056676]\n",
      "['Opponant__Ben Dyson', 0.0029535534122690087]\n",
      "['Opponant__Dan Martinez', 0.0029535534122690087]\n",
      "['Opponant__Edwin Najmi', 0.0029535534122690087]\n",
      "['Opponant__Vinicius Gazola', 0.0029535534122690087]\n",
      "['IBJJF_W_belt__brown', 0.0029535534122690087]\n",
      "['Opponant__Valdir Araujo', 0.0029507768638203417]\n",
      "['Opponant__Victor Hugo', 0.0029507768638203417]\n",
      "['Weight__Men -99kg', 0.0027878640148961843]\n",
      "['Opponant__Mike Perez', 0.0027074239612465897]\n",
      "['Opponant__Elvira Karppinen', 0.002658198071042108]\n",
      "['Gym__Gracie Humaita', 0.0026002264275804873]\n",
      "['Weight__Men -88kg', 0.0025272230120876207]\n",
      "['Gym__Gracie Barra', 0.0023628427298152065]\n",
      "['Opponant__Pablo Mantovani', 0.0022784554894646637]\n",
      "['Entrance__0', 0.002248210943863116]\n",
      "['Highest_Trials_place', 0.0019671845758802275]\n",
      "['Weight__Men -66kg', 0.001889830040481896]\n",
      "['Opponant__Craig Jones', 0.0017721320473614054]\n",
      "['Fighter__Rikako Yuasa', 0.0017580675073029777]\n",
      "['Gym__Team Quest', 0.0017034075902577304]\n",
      "['Fighter__John Hansen', 0.0016620951898830398]\n",
      "['Opponant__Orlando Sanchez', 0.001559094248282472]\n",
      "['IBJJF_belt__purple', 0.001512577353555946]\n",
      "['Gender__Female', 0.0014767767061345043]\n",
      "['Fighter__Ffion Davies', 0.001327700378904025]\n",
      "['Fighter__Ruan Alvarenga', 0.001147678588621726]\n",
      "['Opponant__Ffion Davies', 0.0010491651071361204]\n",
      "['Opponant__Kenta Iwamoto', 0.0009845178040896695]\n",
      "['Opponant__Richard Alarcon', 0.0009845178040896695]\n",
      "['Opponant__James Puopolo', 0.0009835922879401137]\n",
      "['Gym__Absolute MMA', 0.000969371376334445]\n",
      "['Opponant__Renato Canuto', 0.0009276646691884026]\n",
      "['Fighter__Jena Bishop', 0.0007876142432717355]\n",
      "['Opponant__Vinny Magalhaes', 0.0005697440995889419]\n",
      "['Fighter__Perttu Tepponen', 0.0005625816023369521]\n",
      "['Fighter__Eldar Rafigaev', 0.00056205273596578]\n",
      "['Fighter__Jackson Sousa', 0.000492258902044835]\n",
      "['Fighter__Shane Jamil Hill-Taylor', 0.0004828543958978726]\n",
      "['Fighter__Felipe Pena', 0.00042193620175271537]\n",
      "['Fighter__Celso Vinicius', 0.00039380712163586645]\n",
      "['Opponant__Antonio Braga Neto', 0.00035442640947228396]\n",
      "['Weight__Women +60kg', 0.0003375489614021745]\n",
      "['Belt__black', 0.00032817260136322273]\n",
      "['Fighter__Eliot Marshall', 0.00024589807198502827]\n",
      "['Fighter__Pedro Marinho', 0.00024589807198502827]\n",
      "['Gym__Leve Jiu Jitsu', 0.00019671845758802405]\n",
      "['Fighter__Carina Santi', 0.0001695820141015721]\n",
      "['Fighter__Ross Nicholls', 7.032270029212031e-05]\n",
      "['Fighter__Ben Dyson', 4.683772799714877e-05]\n",
      "['Fighter__Yuri Simoes', 2.3440900097367756e-05]\n",
      "['Fighter__Craig Jones', 1.2626345159566368e-06]\n",
      "['Fighter__Matheus Gabriel', 1.8745578909233362e-07]\n",
      "['Weight__Super Fight', 0.0]\n",
      "['Fighter__Aaron \"Tex\" Johnson', 0.0]\n",
      "['Fighter__Adam Wardzinski', 0.0]\n",
      "['Fighter__Alec Baulding', 0.0]\n",
      "['Fighter__Amanda Leve', 0.0]\n",
      "['Fighter__Ana Carolina Vieira', 0.0]\n",
      "['Fighter__Antonio Braga Neto', 0.0]\n",
      "['Fighter__Augusto \"Tanquinho\" Mendes', 0.0]\n",
      "['Fighter__Beatriz Mesquita', 0.0]\n",
      "['Fighter__Ben Hodgkinson', 0.0]\n",
      "['Fighter__Bianca Basilio', 0.0]\n",
      "['Fighter__Bruno Frazatto', 0.0]\n",
      "['Fighter__Catherine Perret', 0.0]\n",
      "['Fighter__DJ Jackson', 0.0]\n",
      "['Fighter__Dan Martinez', 0.0]\n",
      "['Fighter__Elvira Karppinen', 0.0]\n",
      "['Fighter__Gabi Garcia', 0.0]\n",
      "['Fighter__Gabriel Almeida', 0.0]\n",
      "['Fighter__Gabriel Arges', 0.0]\n",
      "['Fighter__Garry Tonon', 0.0]\n",
      "['Fighter__Geo Martinez', 0.0]\n",
      "['Fighter__Gianni Grippo', 0.0]\n",
      "['Fighter__Gordon Ryan', 0.0]\n",
      "['Fighter__Hudson Taylor', 0.0]\n",
      "['Fighter__James Puopolo', 0.0]\n",
      "['Fighter__Jared Dopp', 0.0]\n",
      "['Fighter__Jessica Flowers', 0.0]\n",
      "['Fighter__John Combs', 0.0]\n",
      "['Fighter__Jon Blank', 0.0]\n",
      "['Fighter__Jon Satava', 0.0]\n",
      "['Fighter__Josh Hinger', 0.0]\n",
      "['Fighter__Keith Krikorian', 0.0]\n",
      "['Fighter__Keller Locke-Sodhi', 0.0]\n",
      "['Fighter__Kennedy Maciel', 0.0]\n",
      "['Fighter__Kenta Iwamoto', 0.0]\n",
      "['Fighter__Livia Giles', 0.0]\n",
      "['Fighter__Lucas \"Hulk\" Barbosa', 0.0]\n",
      "['Fighter__Lucas Lepri', 0.0]\n",
      "['Fighter__Mahamed Aly', 0.0]\n",
      "['Fighter__Marcus Buchecha Almeida', 0.0]\n",
      "['Fighter__Mateusz Gamrot', 0.0]\n",
      "['Fighter__Mateusz Juskowiak', 0.0]\n",
      "['Fighter__Matheus Diniz', 0.0]\n",
      "['Fighter__Max Gimenis', 0.0]\n",
      "['Fighter__Mike Perez', 0.0]\n",
      "['Fighter__Mraz Avdoyan', 0.0]\n",
      "['Fighter__Murilo Santana', 0.0]\n",
      "['Fighter__Nathiely de Jesus', 0.0]\n",
      "['Fighter__Nicky Ryan', 0.0]\n",
      "['Fighter__Oliver Taza', 0.0]\n",
      "['Fighter__Orlando Sanchez', 0.0]\n",
      "['Fighter__Pablo Mantovani', 0.0]\n",
      "['Fighter__Patrick Gaudio', 0.0]\n",
      "['Fighter__Paul Ardila-Ibarra', 0.0]\n",
      "['Fighter__Paulo Jose Barauna', 0.0]\n",
      "['Fighter__Paulo Miyao', 0.0]\n",
      "['Fighter__Renato Canuto', 0.0]\n",
      "['Fighter__Richard Alarcon', 0.0]\n",
      "['Fighter__Roberto \"Cyborg\" Abreu', 0.0]\n",
      "['Fighter__Rustam Chsiev', 0.0]\n",
      "['Fighter__Servio Tulio', 0.0]\n",
      "['Fighter__Stephanie Egger', 0.0]\n",
      "['Fighter__Tayane Porfirio', 0.0]\n",
      "['Fighter__Tim Spriggs', 0.0]\n",
      "['Fighter__Tye Ruotolo', 0.0]\n",
      "['Fighter__Vagner Rocha', 0.0]\n",
      "['Fighter__Valdir Araujo', 0.0]\n",
      "['Fighter__Victor Hugo', 0.0]\n",
      "['Fighter__Vinicius Gazola', 0.0]\n",
      "['Fighter__Vinny Magalhaes', 0.0]\n",
      "['Opponant__Adam Wardzinski', 0.0]\n",
      "['Opponant__Andre Galvao', 0.0]\n",
      "['Opponant__Ben Hodgkinson', 0.0]\n",
      "['Opponant__Celso Vinicius', 0.0]\n",
      "['Opponant__DJ Jackson', 0.0]\n",
      "['Opponant__Eldar Rafigaev', 0.0]\n",
      "['Opponant__Felipe Pena', 0.0]\n",
      "['Opponant__Gabriel Almeida', 0.0]\n",
      "['Opponant__Gabriel Arges', 0.0]\n",
      "['Opponant__Geo Martinez', 0.0]\n",
      "['Opponant__Gianni Grippo', 0.0]\n",
      "['Opponant__Hudson Taylor', 0.0]\n",
      "['Opponant__Jackson Sousa', 0.0]\n",
      "['Opponant__Jessica Flowers', 0.0]\n",
      "['Opponant__Jon Satava', 0.0]\n",
      "['Opponant__Keith Krikorian', 0.0]\n",
      "['Opponant__Keller Locke-Sodhi', 0.0]\n",
      "['Opponant__Lachlan Giles', 0.0]\n",
      "['Opponant__Livia Giles', 0.0]\n",
      "['Opponant__Lucas Lepri', 0.0]\n",
      "['Opponant__Mason Fowler', 0.0]\n",
      "['Opponant__Matheus Gabriel', 0.0]\n",
      "['Opponant__Mraz Avdoyan', 0.0]\n",
      "['Opponant__Nathiely de Jesus', 0.0]\n",
      "['Opponant__Oliver Taza', 0.0]\n",
      "['Opponant__Paul Ardila-Ibarra', 0.0]\n",
      "['Opponant__Pedro Marinho', 0.0]\n",
      "['Opponant__Rikako Yuasa', 0.0]\n",
      "['Opponant__Ross Nicholls', 0.0]\n",
      "['Opponant__Servio Tulio', 0.0]\n",
      "['Opponant__Shane Jamil Hill-Taylor', 0.0]\n",
      "['Opponant__Stephanie Egger', 0.0]\n",
      "['Opponant__Tayane Porfirio', 0.0]\n",
      "['Opponant__Thomas Halpin', 0.0]\n",
      "['Opponant__Tim Spriggs', 0.0]\n",
      "['Opponant__Tye Ruotolo', 0.0]\n",
      "['Opponant__Vagner Rocha', 0.0]\n",
      "['Opponant__Yuri Simoes', 0.0]\n",
      "['Entrance__2017 Champion', 0.0]\n",
      "['Gym__10th Planet', 0.0]\n",
      "['Gym__Almeida JJ', 0.0]\n",
      "['Gym__American Top Team (ATT)', 0.0]\n",
      "['Gym__Ankos MMA Poznan', 0.0]\n",
      "['Gym__Atos', 0.0]\n",
      "['Gym__Axios BJJ', 0.0]\n",
      "['Gym__Buddy Gym', 0.0]\n",
      "['Gym__Checkmat', 0.0]\n",
      "['Gym__Dethrone Mixed Martial Arts', 0.0]\n",
      "['Gym__Easton Training Centre', 0.0]\n",
      "['Gym__Fight Sports', 0.0]\n",
      "['Gym__G13', 0.0]\n",
      "['Gym__Gordo JJ', 0.0]\n",
      "['Gym__Grappling Education', 0.0]\n",
      "['Gym__Hilti BJJ', 0.0]\n",
      "['Gym__Jungle BJJ', 0.0]\n",
      "['Gym__K-Dojo', 0.0]\n",
      "['Gym__Maromba', 0.0]\n",
      "['Gym__Matrix Jiu Jitsu team', 0.0]\n",
      "['Gym__Murowana Goslina Team', 0.0]\n",
      "['Gym__One Jiu-Jitsu Academy', 0.0]\n",
      "['Gym__PSLPB Cicero Costha', 0.0]\n",
      "['Gym__Paraestra', 0.0]\n",
      "['Gym__Ribeiro JJ', 0.0]\n",
      "['Gym__Rodrigo Pinheiro', 0.0]\n",
      "['Gym__Roger Gracie Academy', 0.0]\n",
      "['Gym__Roy Dean BJJ', 0.0]\n",
      "['Gym__Ryan Gracie', 0.0]\n",
      "['Gym__Soul Fighters', 0.0]\n",
      "['Gym__Tri-Force Jiu Jitsu Academy', 0.0]\n",
      "['Gym__Unity', 0.0]\n",
      "['Gym__Unity JJ', 0.0]\n",
      "['Gym__VRMMA', 0.0]\n",
      "['Gym__Zenith JJ', 0.0]\n",
      "['Belt__0', 0.0]\n",
      "['Belt__MMA fighter', 0.0]\n",
      "['Belt__blue juvinile', 0.0]\n",
      "['Belt__purple', 0.0]\n",
      "['Belt__wrestler', 0.0]\n",
      "['IBJJF_W_belt__blue', 0.0]\n",
      "['Other_belt__0', 0.0]\n",
      "['Other_belt__brown', 0.0]\n",
      "['Other_belt__purple', 0.0]\n"
     ]
    }
   ],
   "source": [
    "feat_score.sort(reverse=True, key= lambda x:x[1])\n",
    "for char in feat_score:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "\n",
    "#### Target 2: How Matches Concluded (Submission/Score/Referee Decision/Forfeit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "score           108\n",
       "submission       70\n",
       "ref decision     42\n",
       "forfeit           6\n",
       "Name: How, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.How.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>How</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   How\n",
       "0    1\n",
       "1    3\n",
       "2    2"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label encode the target column\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "target = ['How']\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# Apply the label encoder to each column\n",
    "encoded_2 = df[target].apply(encoder.fit_transform)\n",
    "encoded_2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_2 = encoded_2\n",
    "X_2 = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 296)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(226, 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X_2.shape)\n",
    "display(y_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_2_train, X_2_test, y_2_train, y_2_test = train_test_split(X_2,y_2, test_size=0.25,random_state=109)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training X_train Shape: (169, 296)\n",
      "Training y_train Shape: (169, 1)\n",
      "Testing X_test Shape: (57, 296)\n",
      "Testing y_test Shape: (57, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Training X_train Shape:', X_2_train.shape)\n",
    "print('Training y_train Shape:', y_2_train.shape)\n",
    "print('Testing X_test Shape:', X_2_test.shape)\n",
    "print('Testing y_test Shape:', y_2_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "\n",
    "clf.fit(X_2_train, y_2_train)\n",
    "\n",
    "predicted= clf.predict(X_2_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.3684210526315789\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_2_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         2\n",
      "           1       0.12      0.09      0.11        11\n",
      "           2       0.35      0.57      0.44        21\n",
      "           3       0.53      0.35      0.42        23\n",
      "\n",
      "    accuracy                           0.37        57\n",
      "   macro avg       0.25      0.25      0.24        57\n",
      "weighted avg       0.37      0.37      0.35        57\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_2_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0],\n",
       "       [ 0,  0,  1,  1],\n",
       "       [ 2,  6, 12,  3],\n",
       "       [ 0,  5,  8, 19]], dtype=int64)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross validating \n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "y_2_pred_cv = cross_val_predict(clf, X_2_test, y_2_test, cv=5)\n",
    "\n",
    "confusion_matrix(y_2_pred_cv, y_2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.375      0.28571429 0.28571429 0.57142857 0.66666667 0.33333333\n",
      " 0.5        0.33333333 0.5        0.16666667 0.66666667 0.5\n",
      " 1.         0.33333333 0.83333333 0.33333333 0.5        0.8\n",
      " 0.2        0.4        0.8        0.6        0.4        1.\n",
      " 0.4        0.4        0.6        0.5        0.5        0.75      ]\n",
      "Accuracy: 0.52 (+/- 0.43)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cv_score = cross_val_score(clf, X_2_train, y_2_train, cv = 30)\n",
    "print(cv_score)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (cv_score.mean(), cv_score.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.49 (+/- 0.30)\n"
     ]
    }
   ],
   "source": [
    "#Let's test the entire dataset again.\n",
    "scores = cross_val_score(clf, X_2, y_2, cv=10)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training X_train Shape: (169, 296)\n",
      "Training y_train Shape: (169, 1)\n",
      "Testing X_test Shape: (57, 296)\n",
      "Testing y_test Shape: (57, 1)\n"
     ]
    }
   ],
   "source": [
    "X_2_train, X_2_test, y_2_train, y_2_test = train_test_split(X_2,y_2, test_size=0.25,random_state=109) # 60% training and 40% test\n",
    "\n",
    "print('Training X_train Shape:', X_2_train.shape)\n",
    "print('Training y_train Shape:', y_2_train.shape)\n",
    "print('Testing X_test Shape:', X_2_test.shape)\n",
    "print('Testing y_test Shape:', y_2_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_2_train, y_2_train)\n",
    "lr_predict =lr.predict(X_2_test)\n",
    "\n",
    "lr_predict_prob = lr.predict_proba(X_2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  1  0]\n",
      " [ 0  0 10  1]\n",
      " [ 0  4 12  5]\n",
      " [ 0  2 11 10]]\n",
      "0.38596491228070173\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.38596491228070173"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "lr_conf_matrix = confusion_matrix(y_2_test, lr_predict)\n",
    "lr_accuracy = accuracy_score(y_2_test, lr_predict)\n",
    "\n",
    "print(lr_conf_matrix)\n",
    "print(lr_accuracy)\n",
    "\n",
    "metrics.f1_score(y_2_test, lr_predict, average='micro') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  1  0]\n",
      " [ 0  1  9  1]\n",
      " [ 0  9  8  4]\n",
      " [ 0  3 14  6]]\n",
      "0.2631578947368421\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(max_depth=10)\n",
    "\n",
    "dt.fit(X_2_train,y_2_train)\n",
    "dt_predict = dt.predict(X_2_test)\n",
    "dt_predict_prob = dt.predict_proba(X_2_test)\n",
    "\n",
    "dt_conf_matrix = confusion_matrix(y_2_test, dt_predict)\n",
    "dt_accuracy_score = accuracy_score(y_2_test, dt_predict)\n",
    "\n",
    "print(dt_conf_matrix)\n",
    "print(dt_accuracy_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  1  0]\n",
      " [ 0  2  6  3]\n",
      " [ 2  7  2 10]\n",
      " [ 1  3 10  9]]\n",
      "0.22807017543859648\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_2_train, y_2_train)\n",
    "gnb_predict = gnb.predict(X_2_test)\n",
    "gnb_predict_prob = gnb.predict_proba(X_2_test)\n",
    "\n",
    "gnb_conf_matrix = confusion_matrix(y_2_test, gnb_predict)\n",
    "gnb_accuracy_score = accuracy_score(y_2_test, gnb_predict)\n",
    "\n",
    "print(gnb_conf_matrix)\n",
    "print(gnb_accuracy_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[['Results', #for prediction\n",
    "          'Gender','Weight','Fighter','Opponant','Entrance','Gym', #match entrace data\n",
    "          'Belt','age_float','blackbelt_float', #fighter info\n",
    "          'Num_ADCC_comp','Highest_ADCC_place','Max_ADCC_medals','MR_ADCC_Medal_yr','Highest_Trials_place',\n",
    "          'Max_tr_medals','MR_tr_Medal_yr',\n",
    "          #'IBJJF_world', 'IBJJF_majors','Other_tours', \n",
    "          'MR_IBJJF_W_yr', 'IBJJF_W_belt', 'Num_IBJJF_W_medals','MR_IBJJF_W_place', 'MR_IBJJF_yr', 'IBJJF_belt', \n",
    "          'Num_IBJJF_medals','MR_IBJJF_place', 'MR_Other_yr', 'Other_belt', 'Num_Other_medals','MR_Other_place'\n",
    "         ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.Num_ADCC_comp= df2.Num_ADCC_comp.astype(int)\n",
    "df2.Highest_ADCC_place= df2.Highest_ADCC_place.astype(int)\n",
    "df2.Max_ADCC_medals= df2.Max_ADCC_medals.astype(int)\n",
    "df2.MR_ADCC_Medal_yr= df2.MR_ADCC_Medal_yr.astype(int)\n",
    "df2.Highest_Trials_place= df2.Highest_Trials_place.astype(int)\n",
    "df2.Max_tr_medals= df2.Max_tr_medals.astype(int)\n",
    "df2.MR_tr_Medal_yr= df2.MR_tr_Medal_yr.astype(int)\n",
    "\n",
    "df2.MR_IBJJF_W_yr= df2.MR_IBJJF_W_yr.astype(int)\n",
    "df2.Num_IBJJF_W_medals= df2.Num_IBJJF_W_medals.astype(int)\n",
    "df2.MR_IBJJF_W_place= df2.MR_IBJJF_W_place.astype(int)\n",
    "\n",
    "df2.MR_IBJJF_yr= df2.MR_IBJJF_yr.astype(int)\n",
    "df2.Num_IBJJF_medals= df2.Num_IBJJF_medals.astype(int)\n",
    "df2.MR_IBJJF_place= df2.MR_IBJJF_place.astype(int)\n",
    "df2.MR_Other_yr= df2.MR_Other_yr.astype(int)\n",
    "df2.Num_Other_medals= df2.Num_Other_medals.astype(int)\n",
    "df2.MR_Other_place= df2.MR_Other_place.astype(int)\n",
    "\n",
    "df2.age_float= df2.age_float.astype(int)\n",
    "df2.blackbelt_float= df2.blackbelt_float.astype(int)\n",
    "\n",
    "df2.Gym= df2.Gym.astype(str)\n",
    "df2.Belt= df2.Belt.astype(str)\n",
    "df2.Entrance= df2.Entrance.astype(str)\n",
    "df2.IBJJF_W_belt= df2.IBJJF_W_belt.astype(str)\n",
    "df2.IBJJF_belt= df2.IBJJF_belt.astype(str)\n",
    "df2.Other_belt= df2.Other_belt.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Results\n",
       "0        0\n",
       "1        1\n",
       "2        1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label encode the target column\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "target = ['Results']\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# Apply the label encoder to each column\n",
    "encoded = df2[target].apply(encoder.fit_transform)\n",
    "encoded.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# segment categorical features\n",
    "\n",
    "cat_features = ['Gender','Weight','Fighter','Opponant',\n",
    "       'Entrance','Gym','Belt','IBJJF_W_belt', 'IBJJF_belt','Other_belt',\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>age_float</th>\n",
       "      <th>blackbelt_float</th>\n",
       "      <th>Num_ADCC_comp</th>\n",
       "      <th>Highest_ADCC_place</th>\n",
       "      <th>Max_ADCC_medals</th>\n",
       "      <th>MR_ADCC_Medal_yr</th>\n",
       "      <th>Highest_Trials_place</th>\n",
       "      <th>Max_tr_medals</th>\n",
       "      <th>MR_tr_Medal_yr</th>\n",
       "      <th>MR_IBJJF_W_yr</th>\n",
       "      <th>...</th>\n",
       "      <th>IBJJF_W_belt__brown</th>\n",
       "      <th>IBJJF_W_belt__purple</th>\n",
       "      <th>IBJJF_belt__0</th>\n",
       "      <th>IBJJF_belt__black</th>\n",
       "      <th>IBJJF_belt__brown</th>\n",
       "      <th>IBJJF_belt__purple</th>\n",
       "      <th>Other_belt__0</th>\n",
       "      <th>Other_belt__black</th>\n",
       "      <th>Other_belt__brown</th>\n",
       "      <th>Other_belt__purple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 296 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_float  blackbelt_float  Num_ADCC_comp  Highest_ADCC_place  \\\n",
       "0         28                6              1                   3   \n",
       "1         28                6              4                   0   \n",
       "2         37                6              1                   0   \n",
       "3         27                4              1                   0   \n",
       "4         29                9              3                   1   \n",
       "\n",
       "   Max_ADCC_medals  MR_ADCC_Medal_yr  Highest_Trials_place  Max_tr_medals  \\\n",
       "0                1              2019                     0              0   \n",
       "1                0              2019                     0              0   \n",
       "2                0              2019                     1              1   \n",
       "3                0              2019                     0              0   \n",
       "4                4              2017                     0              0   \n",
       "\n",
       "   MR_tr_Medal_yr  MR_IBJJF_W_yr  ...  IBJJF_W_belt__brown  \\\n",
       "0               0           2019  ...                    0   \n",
       "1               0              0  ...                    0   \n",
       "2            2019              0  ...                    0   \n",
       "3               0           2018  ...                    0   \n",
       "4               0           2019  ...                    0   \n",
       "\n",
       "   IBJJF_W_belt__purple  IBJJF_belt__0  IBJJF_belt__black  IBJJF_belt__brown  \\\n",
       "0                     0              0                  1                  0   \n",
       "1                     0              0                  1                  0   \n",
       "2                     0              0                  1                  0   \n",
       "3                     0              0                  1                  0   \n",
       "4                     0              0                  1                  0   \n",
       "\n",
       "   IBJJF_belt__purple  Other_belt__0  Other_belt__black  Other_belt__brown  \\\n",
       "0                   0              0                  1                  0   \n",
       "1                   0              0                  1                  0   \n",
       "2                   0              0                  1                  0   \n",
       "3                   0              0                  1                  0   \n",
       "4                   0              0                  1                  0   \n",
       "\n",
       "   Other_belt__purple  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "\n",
       "[5 rows x 296 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ohe with pandas get_dummies for categorical features, drop the target\n",
    "df2.drop(columns=['Results'], inplace=True)\n",
    "data = pd.get_dummies(df2,prefix_sep=\"__\",\n",
    "                              columns=cat_features)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "Train / Test Split with GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = encoded \n",
    "X = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226, 296)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(226, 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X.shape)\n",
    "display(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training X_train Shape: (169, 296)\n",
      "Training y_train Shape: (169, 1)\n",
      "Testing X_test Shape: (57, 296)\n",
      "Testing y_test Shape: (57, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25,random_state=109)\n",
    "\n",
    "print('Training X_train Shape:', X_train.shape)\n",
    "print('Training y_train Shape:', y_train.shape)\n",
    "print('Testing X_test Shape:', X_test.shape)\n",
    "print('Testing y_test Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.metrics import auc, accuracy_score, roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "estimator = lgb.LGBMClassifier(learning_rate = 0.125, metric = 'l1', \n",
    "                        n_estimators = 20, num_leaves = 38)\n",
    "\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [x for x in range(24,40,2)],\n",
    "    'learning_rate': [0.10, 0.125, 0.15, 0.175, 0.2]}\n",
    "gridsearch = GridSearchCV(estimator, param_grid)\n",
    "\n",
    "gridsearch.fit(X_train, y_train,\n",
    "        eval_set = [(X_test, y_test)],\n",
    "        eval_metric = ['auc', 'binary_logloss'],\n",
    "        early_stopping_rounds = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's auc: 0.583128\tvalid_0's binary_logloss: 0.687776\tvalid_0's l1: 0.496311\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's auc: 0.553571\tvalid_0's binary_logloss: 0.688686\tvalid_0's l1: 0.494523\n",
      "[3]\tvalid_0's auc: 0.561576\tvalid_0's binary_logloss: 0.688714\tvalid_0's l1: 0.491568\n",
      "[4]\tvalid_0's auc: 0.571429\tvalid_0's binary_logloss: 0.686527\tvalid_0's l1: 0.489243\n",
      "[5]\tvalid_0's auc: 0.578818\tvalid_0's binary_logloss: 0.684953\tvalid_0's l1: 0.486661\n",
      "[6]\tvalid_0's auc: 0.568966\tvalid_0's binary_logloss: 0.689883\tvalid_0's l1: 0.486121\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's auc: 0.583128\tvalid_0's binary_logloss: 0.687776\tvalid_0's l1: 0.496311\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "               importance_type='split', learning_rate=0.15, max_depth=-1,\n",
       "               metric='l1', min_child_samples=20, min_child_weight=0.001,\n",
       "               min_split_gain=0.0, n_estimators=24, n_jobs=-1, num_leaves=31,\n",
       "               objective=None, random_state=None, reg_alpha=0.0, reg_lambda=0.0,\n",
       "               silent=True, subsample=1.0, subsample_for_bin=200000,\n",
       "               subsample_freq=0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm = lgb.LGBMClassifier(learning_rate = 0.15, metric = 'l1', \n",
    "                        n_estimators = 24)\n",
    "\n",
    "\n",
    "gbm.fit(X_train, y_train,\n",
    "        eval_set=[(X_test, y_test)],\n",
    "        eval_metric=['auc', 'binary_logloss'],\n",
    "early_stopping_rounds=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ax = lgb.plot_importance(gbm, height = 0.4, \n",
    "#                          max_num_features = 25, \n",
    "                         xlim = (0,20), \n",
    "#                          ylim = (0,23), \n",
    "                         figsize = (6,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of prediction is: 0.543859649122807\n",
      "The roc_auc_score of prediction is: 0.5437192118226601\n"
     ]
    }
   ],
   "source": [
    "y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)\n",
    "print('The accuracy of prediction is:', accuracy_score(y_test, y_pred))\n",
    "print('The roc_auc_score of prediction is:', roc_auc_score(y_test, y_pred))\n",
    "# print('The null acccuracy is:', max(y_test.mean(), 1 - y_test.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_prob = gbm.predict_proba(X_test)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.rcParams['font.size'] = 12\n",
    "plt.title('ROC curve for ADCC win/loss classifier')\n",
    "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[15 13]\n",
      " [13 16]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "Train / Test / Validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add target encoded to all data ohe\n",
    "data_1 = data.join(encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_float</th>\n",
       "      <th>blackbelt_float</th>\n",
       "      <th>Num_ADCC_comp</th>\n",
       "      <th>Highest_ADCC_place</th>\n",
       "      <th>Max_ADCC_medals</th>\n",
       "      <th>MR_ADCC_Medal_yr</th>\n",
       "      <th>Highest_Trials_place</th>\n",
       "      <th>Max_tr_medals</th>\n",
       "      <th>MR_tr_Medal_yr</th>\n",
       "      <th>MR_IBJJF_W_yr</th>\n",
       "      <th>...</th>\n",
       "      <th>IBJJF_W_belt__purple</th>\n",
       "      <th>IBJJF_belt__0</th>\n",
       "      <th>IBJJF_belt__black</th>\n",
       "      <th>IBJJF_belt__brown</th>\n",
       "      <th>IBJJF_belt__purple</th>\n",
       "      <th>Other_belt__0</th>\n",
       "      <th>Other_belt__black</th>\n",
       "      <th>Other_belt__brown</th>\n",
       "      <th>Other_belt__purple</th>\n",
       "      <th>Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 297 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_float  blackbelt_float  Num_ADCC_comp  Highest_ADCC_place  \\\n",
       "0         28                6              1                   3   \n",
       "1         28                6              4                   0   \n",
       "2         37                6              1                   0   \n",
       "\n",
       "   Max_ADCC_medals  MR_ADCC_Medal_yr  Highest_Trials_place  Max_tr_medals  \\\n",
       "0                1              2019                     0              0   \n",
       "1                0              2019                     0              0   \n",
       "2                0              2019                     1              1   \n",
       "\n",
       "   MR_tr_Medal_yr  MR_IBJJF_W_yr  ...  IBJJF_W_belt__purple  IBJJF_belt__0  \\\n",
       "0               0           2019  ...                     0              0   \n",
       "1               0              0  ...                     0              0   \n",
       "2            2019              0  ...                     0              0   \n",
       "\n",
       "   IBJJF_belt__black  IBJJF_belt__brown  IBJJF_belt__purple  Other_belt__0  \\\n",
       "0                  1                  0                   0              0   \n",
       "1                  1                  0                   0              0   \n",
       "2                  1                  0                   0              0   \n",
       "\n",
       "   Other_belt__black  Other_belt__brown  Other_belt__purple  Results  \n",
       "0                  1                  0                   0        0  \n",
       "1                  1                  0                   0        1  \n",
       "2                  1                  0                   0        1  \n",
       "\n",
       "[3 rows x 297 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# baseline model\n",
    "valid_fraction = 0.1\n",
    "valid_size = int(len(data_1) * valid_fraction)\n",
    "\n",
    "train = data_1[:-2 * valid_size]\n",
    "valid = data_1[-2 * valid_size:-valid_size]\n",
    "test = data_1[-valid_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result fraction = 0.5330\n",
      "Result fraction = 0.4545\n",
      "Result fraction = 0.2727\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "for each in [train, valid, test]:\n",
    "    print(f\"Result fraction = {each.Results.mean():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "feature_cols = train.columns.drop('Results')\n",
    "\n",
    "dtrain = lgb.Dataset(train[feature_cols], label=train['Results'])\n",
    "dvalid = lgb.Dataset(valid[feature_cols], label=valid['Results'])\n",
    "\n",
    "param = {'num_leaves': 64, 'objective': 'binary'}\n",
    "param['metric'] = 'auc'\n",
    "num_round = 1000\n",
    "bst = lgb.train(param, dtrain, num_round, valid_sets=[dvalid], early_stopping_rounds=10, verbose_eval=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test AUC score: 0.40625\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "ypred = bst.predict(test[feature_cols])\n",
    "score = metrics.roc_auc_score(test['Results'], ypred)\n",
    "\n",
    "print(f\"Test AUC score: {score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Categorical Encodings "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the model will learn on the validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn import metrics\n",
    "\n",
    "def get_data_splits(dataframe, valid_fraction=0.1):\n",
    "    valid_fraction = 0.1\n",
    "    valid_size = int(len(dataframe) * valid_fraction)\n",
    "\n",
    "    train = dataframe[:-valid_size * 2]\n",
    "    # valid size == test size, last two sections of the data\n",
    "    valid = dataframe[-valid_size * 2:-valid_size]\n",
    "    test = dataframe[-valid_size:]\n",
    "    \n",
    "    return train, valid, test\n",
    "\n",
    "def train_model(train, valid):\n",
    "    feature_cols = train.columns.drop('Results')\n",
    "\n",
    "    dtrain = lgb.Dataset(train[feature_cols], label=train['Results'])\n",
    "    dvalid = lgb.Dataset(valid[feature_cols], label=valid['Results'])\n",
    "\n",
    "    param = {'num_leaves': 64, 'objective': 'binary', \n",
    "             'metric': 'auc', }\n",
    "    print(\"Training model!\")\n",
    "    bst = lgb.train(param, dtrain, num_boost_round=1000, valid_sets=[dvalid], \n",
    "                    early_stopping_rounds=10, verbose_eval=False)\n",
    "\n",
    "    valid_pred = bst.predict(valid[feature_cols])\n",
    "    valid_score = metrics.roc_auc_score(valid['Results'], valid_pred)\n",
    "    print(f\"Validation AUC score: {valid_score:.4f}\") \n",
    "    return bst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model!\n",
      "Validation AUC score: 0.8167\n"
     ]
    }
   ],
   "source": [
    "# Training a model on the baseline data\n",
    "train, valid, _ = get_data_splits(data_1)\n",
    "\n",
    "bst = train_model(train, valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "Tune Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn import metrics\n",
    "\n",
    "def get_data_splits(dataframe, valid_fraction=0.1):\n",
    "    valid_fraction = 0.1\n",
    "    valid_size = int(len(dataframe) * valid_fraction)\n",
    "\n",
    "    train = dataframe[:-valid_size * 2]\n",
    "    # valid size == test size, last two sections of the data\n",
    "    valid = dataframe[-valid_size * 2:-valid_size]\n",
    "    test = dataframe[-valid_size:]\n",
    "    \n",
    "    return train, valid, test\n",
    "\n",
    "def train_model(train, valid):\n",
    "    feature_cols = train.columns.drop('Results')\n",
    "\n",
    "    dtrain = lgb.Dataset(train[feature_cols], label=train['Results'])\n",
    "    dvalid = lgb.Dataset(valid[feature_cols], label=valid['Results'])\n",
    "\n",
    "    param = {'num_leaves': 10, 'objective': 'binary', 'learning_rate': 2, 'max_depth': 10, 'feature_fraction':1,\n",
    "             'metric': 'auc', 'seed': 7, 'min_data':20 } #'boosting_type': 'gbdt', 'sub_features': 0.5,\n",
    "            # lowering num leaves loweres the score\n",
    "            # learning rate 2 had the max and raised score from 81 to 85\n",
    "            # changing seed didn't change the score\n",
    "            # added/removing boosting type and sub features didn't change anything\n",
    "    print(\"Training model!\")\n",
    "    bst = lgb.train(param, dtrain, num_boost_round=1000, valid_sets=[dvalid], \n",
    "                    early_stopping_rounds=10, verbose_eval=False)\n",
    "\n",
    "    valid_pred = bst.predict(valid[feature_cols])\n",
    "    valid_score = metrics.roc_auc_score(valid['Results'], valid_pred)\n",
    "    print(f\"Validation AUC score: {valid_score:.4f}\")\n",
    "    return bst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model!\n",
      "Validation AUC score: 0.8500\n"
     ]
    }
   ],
   "source": [
    "# Training a model on the baseline data\n",
    "train, valid, _ = get_data_splits(data_1)\n",
    "\n",
    "bst = train_model(train, valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = lgb.plot_importance(bst, figsize=(8,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "Count Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count encoding replaces each categorical value with the number of times it appears in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Results\n",
       "0        0\n",
       "1        1\n",
       "2        1"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#label encode the target column\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "target = ['Results']\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# Apply the label encoder to each column\n",
    "encoded = df2[target].apply(encoder.fit_transform)\n",
    "encoded.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# segment categorical features\n",
    "\n",
    "cat_features = ['Gender','Weight','Fighter','Opponant',\n",
    "       'Entrance','Gym','Belt','IBJJF_W_belt', 'IBJJF_belt','Other_belt',\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "import category_encoders as ce\n",
    "count_enc = ce.CountEncoder()\n",
    "count_encoded = count_enc.fit_transform(df2[cat_features])\n",
    "\n",
    "data_cols = ['age_float', 'blackbelt_float',\n",
    "       'Num_ADCC_comp', 'Highest_ADCC_place', 'Max_ADCC_medals',\n",
    "       'MR_ADCC_Medal_yr', 'Highest_Trials_place', 'Max_tr_medals',\n",
    "       'MR_tr_Medal_yr', 'MR_IBJJF_W_yr', 'Num_IBJJF_W_medals',\n",
    "       'MR_IBJJF_W_place', 'MR_IBJJF_yr', 'Num_IBJJF_medals',\n",
    "       'MR_IBJJF_place', 'MR_Other_yr', 'Num_Other_medals',\n",
    "       'MR_Other_place']\n",
    "\n",
    "data_2 = df2[data_cols].join(count_encoded.add_suffix(\"_count\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add target encoded to all data ohe\n",
    "data_2 = data_2.join(encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>age_float</th>\n",
       "      <th>blackbelt_float</th>\n",
       "      <th>Num_ADCC_comp</th>\n",
       "      <th>Highest_ADCC_place</th>\n",
       "      <th>Max_ADCC_medals</th>\n",
       "      <th>MR_ADCC_Medal_yr</th>\n",
       "      <th>Highest_Trials_place</th>\n",
       "      <th>Max_tr_medals</th>\n",
       "      <th>MR_tr_Medal_yr</th>\n",
       "      <th>MR_IBJJF_W_yr</th>\n",
       "      <th>...</th>\n",
       "      <th>Weight_count</th>\n",
       "      <th>Fighter_count</th>\n",
       "      <th>Opponant_count</th>\n",
       "      <th>Entrance_count</th>\n",
       "      <th>Gym_count</th>\n",
       "      <th>Belt_count</th>\n",
       "      <th>IBJJF_W_belt_count</th>\n",
       "      <th>IBJJF_belt_count</th>\n",
       "      <th>Other_belt_count</th>\n",
       "      <th>Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>139</td>\n",
       "      <td>4</td>\n",
       "      <td>197</td>\n",
       "      <td>89</td>\n",
       "      <td>151</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>139</td>\n",
       "      <td>21</td>\n",
       "      <td>197</td>\n",
       "      <td>93</td>\n",
       "      <td>151</td>\n",
       "      <td>146</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>27</td>\n",
       "      <td>197</td>\n",
       "      <td>93</td>\n",
       "      <td>151</td>\n",
       "      <td>146</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_float  blackbelt_float  Num_ADCC_comp  Highest_ADCC_place  \\\n",
       "0         28                6              1                   3   \n",
       "1         28                6              4                   0   \n",
       "2         37                5              1                   0   \n",
       "\n",
       "   Max_ADCC_medals  MR_ADCC_Medal_yr  Highest_Trials_place  Max_tr_medals  \\\n",
       "0                1              2019                     0              0   \n",
       "1                0              2019                     0              0   \n",
       "2                0              2019                     1              1   \n",
       "\n",
       "   MR_tr_Medal_yr  MR_IBJJF_W_yr  ...  Weight_count  Fighter_count  \\\n",
       "0               0           2019  ...            32              4   \n",
       "1               0              0  ...            32              6   \n",
       "2            2019              0  ...            32              4   \n",
       "\n",
       "   Opponant_count  Entrance_count  Gym_count  Belt_count  IBJJF_W_belt_count  \\\n",
       "0               4             139          4         197                  89   \n",
       "1               4             139         21         197                  93   \n",
       "2               5              52         27         197                  93   \n",
       "\n",
       "   IBJJF_belt_count  Other_belt_count  Results  \n",
       "0               151               146        0  \n",
       "1               151               146        1  \n",
       "2               151               146        1  \n",
       "\n",
       "[3 rows x 29 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model!\n",
      "Validation AUC score: 0.8167\n"
     ]
    }
   ],
   "source": [
    "# Training a model on the baseline data\n",
    "train, valid, test = get_data_splits(data_2)\n",
    "bst = train_model(train, valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "Target Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target encoding replaces a categorical value with the average value of the target for that value of the feature. This is often blended with the target probability over the entire dataset to reduce the variance of values with few occurences.\n",
    "\n",
    "This technique uses the targets to create new features. So including the validation or test data in the target encodings would be a form of target leakage. Instead, you should learn the target encodings from the training dataset only and apply it to the other datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model!\n",
      "Validation AUC score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "cat_features = ['Results', 'Gender', 'Weight', 'Fighter', 'Opponant',\n",
    "       'Entrance', 'Gym', 'Belt','IBJJF_W_belt', 'IBJJF_belt','Other_belt',\n",
    "               ]\n",
    "\n",
    "# Create the encoder itself\n",
    "target_enc = ce.TargetEncoder(cols=cat_features)\n",
    "\n",
    "train, valid, _ = get_data_splits(data_2)\n",
    "\n",
    "# Fit the encoder using the categorical features and target\n",
    "target_enc.fit(train[cat_features], train['Results'])\n",
    "\n",
    "# Transform the features, rename the columns with _target suffix, and join to dataframe\n",
    "train = train.join(target_enc.transform(train[cat_features]).add_suffix('_target'))\n",
    "valid = valid.join(target_enc.transform(valid[cat_features]).add_suffix('_target'))\n",
    "\n",
    "train.head()\n",
    "bst = train_model(train, valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "CatBoost Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is similar to target encoding in that it's based on the target probablity for a given value. However with CatBoost, for each row, the target probability is calculated only from the rows before it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model!\n",
      "Validation AUC score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "cat_features = ['Results', 'Gender', 'Weight', 'Fighter', 'Opponant',\n",
    "       'Entrance', 'Gym', 'Belt','IBJJF_W_belt', 'IBJJF_belt','Other_belt',\n",
    "               ]\n",
    "target_enc = ce.CatBoostEncoder(cols=cat_features)\n",
    "\n",
    "train, valid, _ = get_data_splits(data_2)\n",
    "target_enc.fit(train[cat_features], train['Results'])\n",
    "\n",
    "train = train.join(target_enc.transform(train[cat_features]).add_suffix('_cb'))\n",
    "valid = valid.join(target_enc.transform(valid[cat_features]).add_suffix('_cb'))\n",
    "\n",
    "bst = train_model(train, valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count, Target, and CatBoost encoding work too well on the small dataset and the perfect score of 1.0 is misleading. Data leakage has occurred and we should expand the dataset with the player data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Data Leakage!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stick with one-hot encoding for categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color= #28b463>\n",
    "    \n",
    "### Feature Engeineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=  #3498db >\n",
    "\n",
    "#### Feature Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df[['Results',#'Points','How', #for prediction\n",
    "          'Gender','Weight','Fighter','Opponant','Entrance','Gym', #match entrace data\n",
    "          'Belt','age_float','blackbelt_float', #fighter info\n",
    "          'Num_ADCC_comp','Highest_ADCC_place','Max_ADCC_medals','MR_ADCC_Medal_yr','Highest_Trials_place',\n",
    "          'Max_tr_medals','MR_tr_Medal_yr',\n",
    "          #'IBJJF_world', 'IBJJF_majors','Other_tours', \n",
    "          'MR_IBJJF_W_yr', 'IBJJF_W_belt', 'Num_IBJJF_W_medals','MR_IBJJF_W_place', 'MR_IBJJF_yr', 'IBJJF_belt', \n",
    "          'Num_IBJJF_medals','MR_IBJJF_place', 'MR_Other_yr', 'Other_belt', 'Num_Other_medals','MR_Other_place'\n",
    "         ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.Num_ADCC_comp= df4.Num_ADCC_comp.astype(int)\n",
    "df4.Highest_ADCC_place= df4.Highest_ADCC_place.astype(int)\n",
    "df4.Max_ADCC_medals= df4.Max_ADCC_medals.astype(int)\n",
    "df4.MR_ADCC_Medal_yr= df4.MR_ADCC_Medal_yr.astype(int)\n",
    "df4.Highest_Trials_place= df4.Highest_Trials_place.astype(int)\n",
    "df4.Max_tr_medals= df4.Max_tr_medals.astype(int)\n",
    "df4.MR_tr_Medal_yr= df4.MR_tr_Medal_yr.astype(int)\n",
    "\n",
    "df4.MR_IBJJF_W_yr= df4.MR_IBJJF_W_yr.astype(int)\n",
    "df4.Num_IBJJF_W_medals= df4.Num_IBJJF_W_medals.astype(int)\n",
    "df4.MR_IBJJF_W_place= df4.MR_IBJJF_W_place.astype(int)\n",
    "\n",
    "df4.MR_IBJJF_yr= df4.MR_IBJJF_yr.astype(int)\n",
    "df4.Num_IBJJF_medals= df4.Num_IBJJF_medals.astype(int)\n",
    "df4.MR_IBJJF_place= df4.MR_IBJJF_place.astype(int)\n",
    "df4.MR_Other_yr= df4.MR_Other_yr.astype(int)\n",
    "df4.Num_Other_medals= df4.Num_Other_medals.astype(int)\n",
    "df4.MR_Other_place= df4.MR_Other_place.astype(int)\n",
    "\n",
    "df4.age_float= df4.age_float.astype(int)\n",
    "df4.blackbelt_float= df4.blackbelt_float.astype(int)\n",
    "\n",
    "df4.Gym= df4.Gym.astype(str)\n",
    "df4.Belt= df4.Belt.astype(str)\n",
    "df4.Entrance= df4.Entrance.astype(str)\n",
    "df4.IBJJF_W_belt= df4.IBJJF_W_belt.astype(str)\n",
    "df4.IBJJF_belt= df4.IBJJF_belt.astype(str)\n",
    "df4.Other_belt= df4.Other_belt.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Generration:\n",
    "Create interactions by combining features into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df4['Entrance_Gym'] = df4['Entrance'] + \"_\" + df4['Gym']\n",
    "df4['Gender_Weight'] = df4['Gender'] + \"_\" + df4['Weight']\n",
    "df4['Fighter_Belt'] = df4['Fighter'] + \"_\" + df4['Belt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform numerical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Experience as Black Belt')"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(df.blackbelt_float, bins=30);\n",
    "plt.title('Experience as Black Belt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Square root - Experience as Black Belt')"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.hist(np.sqrt(df.blackbelt_float), bins=30);\n",
    "plt.title('Square root - Experience as Black Belt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(np.log(df.blackbelt_float), bins=30);\n",
    "plt.title('Natural Log - Experience as Black Belt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection: Drop unrealted features from dataset to reduce dimentionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.Num_ADCC_comp= df4.Num_ADCC_comp.astype(int)\n",
    "df4.Highest_ADCC_place= df4.Highest_ADCC_place.astype(int)\n",
    "df4.Max_ADCC_medals= df4.Max_ADCC_medals.astype(int)\n",
    "df4.MR_ADCC_Medal_yr= df4.MR_ADCC_Medal_yr.astype(int)\n",
    "df4.Highest_Trials_place= df4.Highest_Trials_place.astype(int)\n",
    "df4.Max_tr_medals= df4.Max_tr_medals.astype(int)\n",
    "df4.MR_tr_Medal_yr= df4.MR_tr_Medal_yr.astype(int)\n",
    "\n",
    "df4.MR_IBJJF_W_yr= df4.MR_IBJJF_W_yr.astype(int)\n",
    "df4.Num_IBJJF_W_medals= df4.Num_IBJJF_W_medals.astype(int)\n",
    "df4.MR_IBJJF_W_place= df4.MR_IBJJF_W_place.astype(int)\n",
    "\n",
    "df4.MR_IBJJF_yr= df4.MR_IBJJF_yr.astype(int)\n",
    "df4.Num_IBJJF_medals= df4.Num_IBJJF_medals.astype(int)\n",
    "df4.MR_IBJJF_place= df4.MR_IBJJF_place.astype(int)\n",
    "df4.MR_Other_yr= df4.MR_Other_yr.astype(int)\n",
    "df4.Num_Other_medals= df4.Num_Other_medals.astype(int)\n",
    "df4.MR_Other_place= df4.MR_Other_place.astype(int)\n",
    "\n",
    "df4.age_float= df4.age_float.astype(int)\n",
    "df4.blackbelt_float= df4.blackbelt_float.astype(int)\n",
    "\n",
    "df4.Gym= df4.Gym.astype(str)\n",
    "df4.Belt= df4.Belt.astype(str)\n",
    "df4.Entrance= df4.Entrance.astype(str)\n",
    "df4.IBJJF_W_belt= df4.IBJJF_W_belt.astype(str)\n",
    "df4.IBJJF_belt= df4.IBJJF_belt.astype(str)\n",
    "df4.Other_belt= df4.Other_belt.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "#encode all column features into array\n",
    "for col in df4.columns:\n",
    "    df4[col]=le.fit_transform(df4[col])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StandardScaler(copy=True, with_mean=True, with_std=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "print(scaler.fit(df4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "f,ax = plt.subplots(figsize=(15,15))\n",
    "ax = sns.heatmap(df4.corr(),annot=True, linewidths=.1, fmt='.1f', ax=ax,cmap=\"YlGnBu\")\n",
    "\n",
    "# fix for mpl bug that cuts off top/bottom of seaborn viz\n",
    "b, t = plt.ylim() # discover the values for bottom and top\n",
    "b += 0.5 # Add 0.5 to the bottom\n",
    "t -= 0.5 # Subtract 0.5 from the top\n",
    "plt.ylim(b, t) # update the ylim(bottom, top) values\n",
    "# plt.show() \n",
    "\n",
    "plt.show()\n",
    "# f.savefig('Numerical Corr Heat Map 2.png',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Results                 1.000000e+00\n",
       "Points                  4.482282e-01\n",
       "How                     0.000000e+00\n",
       "Gender                 -2.818152e-18\n",
       "Weight                  8.292176e-18\n",
       "Fighter                -3.509881e-02\n",
       "Opponant                3.509881e-02\n",
       "Entrance               -2.324823e-01\n",
       "Gym                    -3.921955e-02\n",
       "Belt                   -1.297882e-01\n",
       "age_float              -6.097065e-02\n",
       "blackbelt_float         1.285570e-01\n",
       "Num_ADCC_comp           1.644195e-01\n",
       "Highest_ADCC_place      2.002459e-01\n",
       "Max_ADCC_medals         1.657761e-01\n",
       "MR_ADCC_Medal_yr        2.733262e-01\n",
       "Highest_Trials_place   -1.492479e-01\n",
       "Max_tr_medals          -9.363830e-02\n",
       "MR_tr_Medal_yr         -2.167449e-01\n",
       "MR_IBJJF_W_yr           9.279679e-02\n",
       "IBJJF_W_belt           -1.977211e-02\n",
       "Num_IBJJF_W_medals      8.570438e-02\n",
       "MR_IBJJF_W_place        1.843886e-02\n",
       "MR_IBJJF_yr             2.020309e-01\n",
       "IBJJF_belt              1.129002e-01\n",
       "Num_IBJJF_medals        1.082086e-01\n",
       "MR_IBJJF_place          9.187022e-02\n",
       "MR_Other_yr             1.207758e-01\n",
       "Other_belt              9.859943e-02\n",
       "Num_Other_medals        1.860092e-01\n",
       "MR_Other_place          7.626702e-02\n",
       "Entrance_Gym           -2.178396e-01\n",
       "Gender_Weight           0.000000e+00\n",
       "Fighter_Belt           -3.509881e-02\n",
       "Name: Results, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[df4.columns[0:]].corr(method ='pearson')['Results'][:]"
   ]
  }
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