MLB_Runline_Training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "gTKL-2tRlNIT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mysql-connector-python\r\n",
      "  Obtaining dependency information for mysql-connector-python from https://files.pythonhosted.org/packages/99/72/9bcbc7d6fe122fcbb4f948fd79c18387e84c4df8762aa55eb230acf1193b/mysql_connector_python-8.1.0-py2.py3-none-any.whl.metadata\r\n",
      "  Downloading mysql_connector_python-8.1.0-py2.py3-none-any.whl.metadata (2.0 kB)\r\n",
      "Collecting protobuf<=4.21.12,>=4.21.1 (from mysql-connector-python)\r\n",
      "  Using cached protobuf-4.21.12-cp37-abi3-macosx_10_9_universal2.whl (486 kB)\r\n",
      "Downloading mysql_connector_python-8.1.0-py2.py3-none-any.whl (581 kB)\r\n",
      "\u001B[2K   \u001B[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m581.1/581.1 kB\u001B[0m \u001B[31m6.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m00:01\u001B[0m00:01\u001B[0m\r\n",
      "\u001B[?25hInstalling collected packages: protobuf, mysql-connector-python\r\n",
      "Successfully installed mysql-connector-python-8.1.0 protobuf-4.21.12\r\n",
      "Collecting sqlalchemy==1.4.32\r\n",
      "  Using cached SQLAlchemy-1.4.32.tar.gz (8.1 MB)\r\n",
      "  Preparing metadata (setup.py) ... \u001B[?25ldone\r\n",
      "\u001B[?25hBuilding wheels for collected packages: sqlalchemy\r\n",
      "  Building wheel for sqlalchemy (setup.py) ... \u001B[?25ldone\r\n",
      "\u001B[?25h  Created wheel for sqlalchemy: filename=SQLAlchemy-1.4.32-cp38-cp38-macosx_11_0_arm64.whl size=1535159 sha256=d152dac4f0825b5ccde4b649951cb7c8138ef7a639bfc7fe8f85c647c74949b5\r\n",
      "  Stored in directory: /Users/martinmfejka/Library/Caches/pip/wheels/f1/89/a6/de94839bd1eae1e97da3e026f85938bc8afca6739bda1df206\r\n",
      "Successfully built sqlalchemy\r\n",
      "Installing collected packages: sqlalchemy\r\n",
      "Successfully installed sqlalchemy-1.4.32\r\n"
     ]
    }
   ],
   "source": [
    "#!pip install pycaret\n",
    "!pip install mysql-connector-python\n",
    "!pip install sqlalchemy==1.4.32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import sklearn as sk\n",
    "import pycaret\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sqlalchemy\n",
    "import mysql.connector\n",
    "from constants import API_KEY, database_name, sql_hostname\n",
    "\n",
    "from pycaret.classification import *\n",
    "\n",
    "from datetime import datetime\n",
    "#from google.colab import drive\n",
    "# from google.colab.data_table import DataTable\n",
    "# from google.colab import files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "dNNe7YwMlpgP"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768\n"
     ]
    }
   ],
   "source": [
    "engine = sqlalchemy.create_engine(f'{sql_hostname}/{database_name}')\n",
    "\n",
    "Raw_Dataset = pd.read_sql(sql = \"SELECT * FROM baseball_spread\", con = engine).set_index(\"game_datetime\").reset_index()[[\"game_datetime\", \"team_1\",\t\"team_1_spread_odds\",\t\"team_2\",\t\"team_2_spread_odds\",\t\"venue_name\",\t\"spread\"]]\n",
    "print(len(Raw_Dataset))\n",
    "#Raw_Dataset['game_datetime'] = pd.to_numeric(pd.to_datetime(Raw_Dataset['game_datetime']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_datetime</th>\n",
       "      <th>team_1</th>\n",
       "      <th>team_1_spread_odds</th>\n",
       "      <th>team_2</th>\n",
       "      <th>team_2_spread_odds</th>\n",
       "      <th>venue_name</th>\n",
       "      <th>spread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-04-04 23:05:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>135</td>\n",
       "      <td>Washington Nationals</td>\n",
       "      <td>-165</td>\n",
       "      <td>Nationals Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-04-04 23:40:00</td>\n",
       "      <td>Toronto Blue Jays</td>\n",
       "      <td>-195</td>\n",
       "      <td>Kansas City Royals</td>\n",
       "      <td>160</td>\n",
       "      <td>Kauffman Stadium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-04-04 23:40:00</td>\n",
       "      <td>New York Mets</td>\n",
       "      <td>155</td>\n",
       "      <td>Milwaukee Brewers</td>\n",
       "      <td>-190</td>\n",
       "      <td>American Family Field</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-04-05 17:05:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>110</td>\n",
       "      <td>Washington Nationals</td>\n",
       "      <td>-140</td>\n",
       "      <td>Nationals Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-04-05 19:37:00</td>\n",
       "      <td>Cleveland Guardians</td>\n",
       "      <td>160</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>-195</td>\n",
       "      <td>Oakland Coliseum</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>2023-08-10 22:40:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>105</td>\n",
       "      <td>St. Louis Cardinals</td>\n",
       "      <td>-125</td>\n",
       "      <td>Tropicana Field</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2023-08-10 22:40:00</td>\n",
       "      <td>Philadelphia Phillies</td>\n",
       "      <td>-122</td>\n",
       "      <td>Washington Nationals</td>\n",
       "      <td>102</td>\n",
       "      <td>Citizens Bank Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>2023-08-10 23:10:00</td>\n",
       "      <td>Boston Red Sox</td>\n",
       "      <td>-125</td>\n",
       "      <td>Kansas City Royals</td>\n",
       "      <td>105</td>\n",
       "      <td>Fenway Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>2023-08-11 02:10:00</td>\n",
       "      <td>Los Angeles Dodgers</td>\n",
       "      <td>-166</td>\n",
       "      <td>Colorado Rockies</td>\n",
       "      <td>140</td>\n",
       "      <td>Dodger Stadium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>2023-08-11 22:40:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>120</td>\n",
       "      <td>Cleveland Guardians</td>\n",
       "      <td>-142</td>\n",
       "      <td>Tropicana Field</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>768 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          game_datetime                 team_1  team_1_spread_odds  \\\n",
       "0   2023-04-04 23:05:00         Tampa Bay Rays                 135   \n",
       "1   2023-04-04 23:40:00      Toronto Blue Jays                -195   \n",
       "2   2023-04-04 23:40:00          New York Mets                 155   \n",
       "3   2023-04-05 17:05:00         Tampa Bay Rays                 110   \n",
       "4   2023-04-05 19:37:00    Cleveland Guardians                 160   \n",
       "..                  ...                    ...                 ...   \n",
       "763 2023-08-10 22:40:00         Tampa Bay Rays                 105   \n",
       "764 2023-08-10 22:40:00  Philadelphia Phillies                -122   \n",
       "765 2023-08-10 23:10:00         Boston Red Sox                -125   \n",
       "766 2023-08-11 02:10:00    Los Angeles Dodgers                -166   \n",
       "767 2023-08-11 22:40:00         Tampa Bay Rays                 120   \n",
       "\n",
       "                   team_2  team_2_spread_odds             venue_name  spread  \n",
       "0    Washington Nationals                -165         Nationals Park       1  \n",
       "1      Kansas City Royals                 160       Kauffman Stadium       1  \n",
       "2       Milwaukee Brewers                -190  American Family Field       0  \n",
       "3    Washington Nationals                -140         Nationals Park       1  \n",
       "4       Oakland Athletics                -195       Oakland Coliseum       1  \n",
       "..                    ...                 ...                    ...     ...  \n",
       "763   St. Louis Cardinals                -125        Tropicana Field       0  \n",
       "764  Washington Nationals                 102     Citizens Bank Park       1  \n",
       "765    Kansas City Royals                 105            Fenway Park       1  \n",
       "766      Colorado Rockies                 140         Dodger Stadium       0  \n",
       "767   Cleveland Guardians                -142        Tropicana Field       0  \n",
       "\n",
       "[768 rows x 7 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Raw_Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_datetime</th>\n",
       "      <th>team_1</th>\n",
       "      <th>team_1_spread_odds</th>\n",
       "      <th>team_2</th>\n",
       "      <th>team_2_spread_odds</th>\n",
       "      <th>venue_name</th>\n",
       "      <th>spread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>2023-08-11 22:40:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>120</td>\n",
       "      <td>Cleveland Guardians</td>\n",
       "      <td>-142</td>\n",
       "      <td>Tropicana Field</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>2023-08-11 02:10:00</td>\n",
       "      <td>Los Angeles Dodgers</td>\n",
       "      <td>-166</td>\n",
       "      <td>Colorado Rockies</td>\n",
       "      <td>140</td>\n",
       "      <td>Dodger Stadium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>2023-08-10 23:10:00</td>\n",
       "      <td>Boston Red Sox</td>\n",
       "      <td>-125</td>\n",
       "      <td>Kansas City Royals</td>\n",
       "      <td>105</td>\n",
       "      <td>Fenway Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2023-08-10 22:40:00</td>\n",
       "      <td>Philadelphia Phillies</td>\n",
       "      <td>-122</td>\n",
       "      <td>Washington Nationals</td>\n",
       "      <td>102</td>\n",
       "      <td>Citizens Bank Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>2023-08-10 22:40:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>105</td>\n",
       "      <td>St. Louis Cardinals</td>\n",
       "      <td>-125</td>\n",
       "      <td>Tropicana Field</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-04-04 23:40:00</td>\n",
       "      <td>Toronto Blue Jays</td>\n",
       "      <td>-195</td>\n",
       "      <td>Kansas City Royals</td>\n",
       "      <td>160</td>\n",
       "      <td>Kauffman Stadium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2023-04-04 23:10:00</td>\n",
       "      <td>Boston Red Sox</td>\n",
       "      <td>140</td>\n",
       "      <td>Pittsburgh Pirates</td>\n",
       "      <td>-175</td>\n",
       "      <td>Fenway Park</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2023-04-04 23:05:00</td>\n",
       "      <td>New York Yankees</td>\n",
       "      <td>110</td>\n",
       "      <td>Philadelphia Phillies</td>\n",
       "      <td>-140</td>\n",
       "      <td>Yankee Stadium</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-04-04 23:05:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>135</td>\n",
       "      <td>Washington Nationals</td>\n",
       "      <td>-165</td>\n",
       "      <td>Nationals Park</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>2023-04-04 20:10:00</td>\n",
       "      <td>San Diego Padres</td>\n",
       "      <td>140</td>\n",
       "      <td>Arizona Diamondbacks</td>\n",
       "      <td>-165</td>\n",
       "      <td>Petco Park</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          game_datetime                 team_1  team_1_spread_odds  \\\n",
       "767 2023-08-11 22:40:00         Tampa Bay Rays                 120   \n",
       "766 2023-08-11 02:10:00    Los Angeles Dodgers                -166   \n",
       "765 2023-08-10 23:10:00         Boston Red Sox                -125   \n",
       "764 2023-08-10 22:40:00  Philadelphia Phillies                -122   \n",
       "763 2023-08-10 22:40:00         Tampa Bay Rays                 105   \n",
       "..                  ...                    ...                 ...   \n",
       "1   2023-04-04 23:40:00      Toronto Blue Jays                -195   \n",
       "284 2023-04-04 23:10:00         Boston Red Sox                 140   \n",
       "283 2023-04-04 23:05:00       New York Yankees                 110   \n",
       "0   2023-04-04 23:05:00         Tampa Bay Rays                 135   \n",
       "282 2023-04-04 20:10:00       San Diego Padres                 140   \n",
       "\n",
       "                    team_2  team_2_spread_odds          venue_name  spread  \n",
       "767    Cleveland Guardians                -142     Tropicana Field       0  \n",
       "766       Colorado Rockies                 140      Dodger Stadium       0  \n",
       "765     Kansas City Royals                 105         Fenway Park       1  \n",
       "764   Washington Nationals                 102  Citizens Bank Park       1  \n",
       "763    St. Louis Cardinals                -125     Tropicana Field       0  \n",
       "..                     ...                 ...                 ...     ...  \n",
       "1       Kansas City Royals                 160    Kauffman Stadium       1  \n",
       "284     Pittsburgh Pirates                -175         Fenway Park       0  \n",
       "283  Philadelphia Phillies                -140      Yankee Stadium       0  \n",
       "0     Washington Nationals                -165      Nationals Park       1  \n",
       "282   Arizona Diamondbacks                -165          Petco Park       0  \n",
       "\n",
       "[768 rows x 7 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Raw_Dataset.sort_values('game_datetime', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Preprocessed_DF = pd.concat((Raw_Dataset[['game_datetime']], pd.get_dummies(Raw_Dataset[['team_1','team_2','venue_name']]), Raw_Dataset[['team_1_spread_odds', 'team_2_spread_odds', 'spread']]), axis=1)\n",
    "\n",
    "cat_features = Preprocessed_DF.columns.tolist()\n",
    "cat_features.remove(\"game_datetime\")\n",
    "Preprocessed_DF['game_datetime'] = pd.to_numeric(pd.to_datetime(Preprocessed_DF['game_datetime']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Wb0v2bKsmJOc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n",
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n",
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n",
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n",
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n",
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n"
     ]
    },
    {
     "data": {
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       "<style type=\"text/css\">\n",
       "#T_15494_row10_col1, #T_15494_row19_col1 {\n",
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       "<table id=\"T_15494\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_15494_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_15494_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_15494_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_15494_row0_col1\" class=\"data row0 col1\" >1942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_15494_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_15494_row1_col1\" class=\"data row1 col1\" >spread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_15494_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_15494_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_15494_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_15494_row3_col1\" class=\"data row3 col1\" >(768, 7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_15494_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_15494_row4_col1\" class=\"data row4 col1\" >(768, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_15494_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_15494_row5_col1\" class=\"data row5 col1\" >(537, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_15494_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_15494_row6_col1\" class=\"data row6 col1\" >(231, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_15494_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_15494_row7_col1\" class=\"data row7 col1\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_15494_row8_col0\" class=\"data row8 col0\" >Date features</td>\n",
       "      <td id=\"T_15494_row8_col1\" class=\"data row8 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_15494_row9_col0\" class=\"data row9 col0\" >Categorical features</td>\n",
       "      <td id=\"T_15494_row9_col1\" class=\"data row9 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_15494_row10_col0\" class=\"data row10 col0\" >Preprocess</td>\n",
       "      <td id=\"T_15494_row10_col1\" class=\"data row10 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_15494_row11_col0\" class=\"data row11 col0\" >Imputation type</td>\n",
       "      <td id=\"T_15494_row11_col1\" class=\"data row11 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_15494_row12_col0\" class=\"data row12 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_15494_row12_col1\" class=\"data row12 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_15494_row13_col0\" class=\"data row13 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_15494_row13_col1\" class=\"data row13 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_15494_row14_col0\" class=\"data row14 col0\" >Maximum one-hot encoding</td>\n",
       "      <td id=\"T_15494_row14_col1\" class=\"data row14 col1\" >25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_15494_row15_col0\" class=\"data row15 col0\" >Encoding method</td>\n",
       "      <td id=\"T_15494_row15_col1\" class=\"data row15 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_15494_row16_col0\" class=\"data row16 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_15494_row16_col1\" class=\"data row16 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_15494_row17_col0\" class=\"data row17 col0\" >Fold Number</td>\n",
       "      <td id=\"T_15494_row17_col1\" class=\"data row17 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_15494_row18_col0\" class=\"data row18 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_15494_row18_col1\" class=\"data row18 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_15494_row19_col0\" class=\"data row19 col0\" >Use GPU</td>\n",
       "      <td id=\"T_15494_row19_col1\" class=\"data row19 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_15494_row20_col0\" class=\"data row20 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_15494_row20_col1\" class=\"data row20 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_15494_row21_col0\" class=\"data row21 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_15494_row21_col1\" class=\"data row21 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15494_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_15494_row22_col0\" class=\"data row22 col0\" >USI</td>\n",
       "      <td id=\"T_15494_row22_col1\" class=\"data row22 col1\" >cdd8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "text": [
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n",
      "[LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n",
      "[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.\n",
      "[LightGBM] [Info] Number of positive: 1, number of negative: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n",
      "[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_GPU=1\n",
      "[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.\n",
      "Please recompile with CMake option -DUSE_CUDAP=1\n"
     ]
    }
   ],
   "source": [
    "Classification_Setup = setup(data = Raw_Dataset, target = \"spread\", train_size = .70, use_gpu = True, data_split_stratify = False, data_split_shuffle = False, preprocess=True, fold_shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jnTnG8aSK74e"
   },
   "source": [
    "\n",
    "***Compare/Create Model***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "YTAPYfpanoXE"
   },
   "outputs": [
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       "<table id=\"T_4f24e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4f24e_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_4f24e_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_4f24e_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_4f24e_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_4f24e_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_4f24e_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_4f24e_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_4f24e_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_4f24e_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row0\" class=\"row_heading level0 row0\" >knn</th>\n",
       "      <td id=\"T_4f24e_row0_col0\" class=\"data row0 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_4f24e_row0_col1\" class=\"data row0 col1\" >0.6779</td>\n",
       "      <td id=\"T_4f24e_row0_col2\" class=\"data row0 col2\" >0.6903</td>\n",
       "      <td id=\"T_4f24e_row0_col3\" class=\"data row0 col3\" >0.8011</td>\n",
       "      <td id=\"T_4f24e_row0_col4\" class=\"data row0 col4\" >0.7063</td>\n",
       "      <td id=\"T_4f24e_row0_col5\" class=\"data row0 col5\" >0.7485</td>\n",
       "      <td id=\"T_4f24e_row0_col6\" class=\"data row0 col6\" >0.3026</td>\n",
       "      <td id=\"T_4f24e_row0_col7\" class=\"data row0 col7\" >0.3107</td>\n",
       "      <td id=\"T_4f24e_row0_col8\" class=\"data row0 col8\" >0.1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row1\" class=\"row_heading level0 row1\" >lr</th>\n",
       "      <td id=\"T_4f24e_row1_col0\" class=\"data row1 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_4f24e_row1_col1\" class=\"data row1 col1\" >0.6703</td>\n",
       "      <td id=\"T_4f24e_row1_col2\" class=\"data row1 col2\" >0.7177</td>\n",
       "      <td id=\"T_4f24e_row1_col3\" class=\"data row1 col3\" >0.7144</td>\n",
       "      <td id=\"T_4f24e_row1_col4\" class=\"data row1 col4\" >0.7309</td>\n",
       "      <td id=\"T_4f24e_row1_col5\" class=\"data row1 col5\" >0.7220</td>\n",
       "      <td id=\"T_4f24e_row1_col6\" class=\"data row1 col6\" >0.3167</td>\n",
       "      <td id=\"T_4f24e_row1_col7\" class=\"data row1 col7\" >0.3176</td>\n",
       "      <td id=\"T_4f24e_row1_col8\" class=\"data row1 col8\" >0.0890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row2\" class=\"row_heading level0 row2\" >ada</th>\n",
       "      <td id=\"T_4f24e_row2_col0\" class=\"data row2 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_4f24e_row2_col1\" class=\"data row2 col1\" >0.6668</td>\n",
       "      <td id=\"T_4f24e_row2_col2\" class=\"data row2 col2\" >0.6891</td>\n",
       "      <td id=\"T_4f24e_row2_col3\" class=\"data row2 col3\" >0.7705</td>\n",
       "      <td id=\"T_4f24e_row2_col4\" class=\"data row2 col4\" >0.7048</td>\n",
       "      <td id=\"T_4f24e_row2_col5\" class=\"data row2 col5\" >0.7335</td>\n",
       "      <td id=\"T_4f24e_row2_col6\" class=\"data row2 col6\" >0.2877</td>\n",
       "      <td id=\"T_4f24e_row2_col7\" class=\"data row2 col7\" >0.2932</td>\n",
       "      <td id=\"T_4f24e_row2_col8\" class=\"data row2 col8\" >0.1390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "      <td id=\"T_4f24e_row3_col0\" class=\"data row3 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_4f24e_row3_col1\" class=\"data row3 col1\" >0.6631</td>\n",
       "      <td id=\"T_4f24e_row3_col2\" class=\"data row3 col2\" >0.6814</td>\n",
       "      <td id=\"T_4f24e_row3_col3\" class=\"data row3 col3\" >0.7548</td>\n",
       "      <td id=\"T_4f24e_row3_col4\" class=\"data row3 col4\" >0.7040</td>\n",
       "      <td id=\"T_4f24e_row3_col5\" class=\"data row3 col5\" >0.7277</td>\n",
       "      <td id=\"T_4f24e_row3_col6\" class=\"data row3 col6\" >0.2857</td>\n",
       "      <td id=\"T_4f24e_row3_col7\" class=\"data row3 col7\" >0.2888</td>\n",
       "      <td id=\"T_4f24e_row3_col8\" class=\"data row3 col8\" >0.2580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row4\" class=\"row_heading level0 row4\" >ridge</th>\n",
       "      <td id=\"T_4f24e_row4_col0\" class=\"data row4 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_4f24e_row4_col1\" class=\"data row4 col1\" >0.6611</td>\n",
       "      <td id=\"T_4f24e_row4_col2\" class=\"data row4 col2\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row4_col3\" class=\"data row4 col3\" >0.7362</td>\n",
       "      <td id=\"T_4f24e_row4_col4\" class=\"data row4 col4\" >0.7098</td>\n",
       "      <td id=\"T_4f24e_row4_col5\" class=\"data row4 col5\" >0.7218</td>\n",
       "      <td id=\"T_4f24e_row4_col6\" class=\"data row4 col6\" >0.2876</td>\n",
       "      <td id=\"T_4f24e_row4_col7\" class=\"data row4 col7\" >0.2895</td>\n",
       "      <td id=\"T_4f24e_row4_col8\" class=\"data row4 col8\" >0.0630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row5\" class=\"row_heading level0 row5\" >gbc</th>\n",
       "      <td id=\"T_4f24e_row5_col0\" class=\"data row5 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_4f24e_row5_col1\" class=\"data row5 col1\" >0.6593</td>\n",
       "      <td id=\"T_4f24e_row5_col2\" class=\"data row5 col2\" >0.6858</td>\n",
       "      <td id=\"T_4f24e_row5_col3\" class=\"data row5 col3\" >0.7547</td>\n",
       "      <td id=\"T_4f24e_row5_col4\" class=\"data row5 col4\" >0.7030</td>\n",
       "      <td id=\"T_4f24e_row5_col5\" class=\"data row5 col5\" >0.7258</td>\n",
       "      <td id=\"T_4f24e_row5_col6\" class=\"data row5 col6\" >0.2750</td>\n",
       "      <td id=\"T_4f24e_row5_col7\" class=\"data row5 col7\" >0.2797</td>\n",
       "      <td id=\"T_4f24e_row5_col8\" class=\"data row5 col8\" >0.1630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row6\" class=\"row_heading level0 row6\" >lda</th>\n",
       "      <td id=\"T_4f24e_row6_col0\" class=\"data row6 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_4f24e_row6_col1\" class=\"data row6 col1\" >0.6555</td>\n",
       "      <td id=\"T_4f24e_row6_col2\" class=\"data row6 col2\" >0.7007</td>\n",
       "      <td id=\"T_4f24e_row6_col3\" class=\"data row6 col3\" >0.7394</td>\n",
       "      <td id=\"T_4f24e_row6_col4\" class=\"data row6 col4\" >0.7021</td>\n",
       "      <td id=\"T_4f24e_row6_col5\" class=\"data row6 col5\" >0.7192</td>\n",
       "      <td id=\"T_4f24e_row6_col6\" class=\"data row6 col6\" >0.2737</td>\n",
       "      <td id=\"T_4f24e_row6_col7\" class=\"data row6 col7\" >0.2760</td>\n",
       "      <td id=\"T_4f24e_row6_col8\" class=\"data row6 col8\" >0.0840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row7\" class=\"row_heading level0 row7\" >et</th>\n",
       "      <td id=\"T_4f24e_row7_col0\" class=\"data row7 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_4f24e_row7_col1\" class=\"data row7 col1\" >0.6538</td>\n",
       "      <td id=\"T_4f24e_row7_col2\" class=\"data row7 col2\" >0.6832</td>\n",
       "      <td id=\"T_4f24e_row7_col3\" class=\"data row7 col3\" >0.7520</td>\n",
       "      <td id=\"T_4f24e_row7_col4\" class=\"data row7 col4\" >0.6965</td>\n",
       "      <td id=\"T_4f24e_row7_col5\" class=\"data row7 col5\" >0.7215</td>\n",
       "      <td id=\"T_4f24e_row7_col6\" class=\"data row7 col6\" >0.2638</td>\n",
       "      <td id=\"T_4f24e_row7_col7\" class=\"data row7 col7\" >0.2680</td>\n",
       "      <td id=\"T_4f24e_row7_col8\" class=\"data row7 col8\" >0.2340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row8\" class=\"row_heading level0 row8\" >nb</th>\n",
       "      <td id=\"T_4f24e_row8_col0\" class=\"data row8 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_4f24e_row8_col1\" class=\"data row8 col1\" >0.6499</td>\n",
       "      <td id=\"T_4f24e_row8_col2\" class=\"data row8 col2\" >0.6848</td>\n",
       "      <td id=\"T_4f24e_row8_col3\" class=\"data row8 col3\" >0.6242</td>\n",
       "      <td id=\"T_4f24e_row8_col4\" class=\"data row8 col4\" >0.7507</td>\n",
       "      <td id=\"T_4f24e_row8_col5\" class=\"data row8 col5\" >0.6799</td>\n",
       "      <td id=\"T_4f24e_row8_col6\" class=\"data row8 col6\" >0.3003</td>\n",
       "      <td id=\"T_4f24e_row8_col7\" class=\"data row8 col7\" >0.3077</td>\n",
       "      <td id=\"T_4f24e_row8_col8\" class=\"data row8 col8\" >0.0830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row9\" class=\"row_heading level0 row9\" >lightgbm</th>\n",
       "      <td id=\"T_4f24e_row9_col0\" class=\"data row9 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_4f24e_row9_col1\" class=\"data row9 col1\" >0.6407</td>\n",
       "      <td id=\"T_4f24e_row9_col2\" class=\"data row9 col2\" >0.6487</td>\n",
       "      <td id=\"T_4f24e_row9_col3\" class=\"data row9 col3\" >0.7241</td>\n",
       "      <td id=\"T_4f24e_row9_col4\" class=\"data row9 col4\" >0.6916</td>\n",
       "      <td id=\"T_4f24e_row9_col5\" class=\"data row9 col5\" >0.7060</td>\n",
       "      <td id=\"T_4f24e_row9_col6\" class=\"data row9 col6\" >0.2433</td>\n",
       "      <td id=\"T_4f24e_row9_col7\" class=\"data row9 col7\" >0.2461</td>\n",
       "      <td id=\"T_4f24e_row9_col8\" class=\"data row9 col8\" >0.2520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row10\" class=\"row_heading level0 row10\" >svm</th>\n",
       "      <td id=\"T_4f24e_row10_col0\" class=\"data row10 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_4f24e_row10_col1\" class=\"data row10 col1\" >0.6109</td>\n",
       "      <td id=\"T_4f24e_row10_col2\" class=\"data row10 col2\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row10_col3\" class=\"data row10 col3\" >0.7074</td>\n",
       "      <td id=\"T_4f24e_row10_col4\" class=\"data row10 col4\" >0.6291</td>\n",
       "      <td id=\"T_4f24e_row10_col5\" class=\"data row10 col5\" >0.6422</td>\n",
       "      <td id=\"T_4f24e_row10_col6\" class=\"data row10 col6\" >0.1694</td>\n",
       "      <td id=\"T_4f24e_row10_col7\" class=\"data row10 col7\" >0.1755</td>\n",
       "      <td id=\"T_4f24e_row10_col8\" class=\"data row10 col8\" >0.0650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row11\" class=\"row_heading level0 row11\" >dummy</th>\n",
       "      <td id=\"T_4f24e_row11_col0\" class=\"data row11 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_4f24e_row11_col1\" class=\"data row11 col1\" >0.5997</td>\n",
       "      <td id=\"T_4f24e_row11_col2\" class=\"data row11 col2\" >0.5000</td>\n",
       "      <td id=\"T_4f24e_row11_col3\" class=\"data row11 col3\" >1.0000</td>\n",
       "      <td id=\"T_4f24e_row11_col4\" class=\"data row11 col4\" >0.5997</td>\n",
       "      <td id=\"T_4f24e_row11_col5\" class=\"data row11 col5\" >0.7497</td>\n",
       "      <td id=\"T_4f24e_row11_col6\" class=\"data row11 col6\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row11_col7\" class=\"data row11 col7\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row11_col8\" class=\"data row11 col8\" >0.0870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row12\" class=\"row_heading level0 row12\" >dt</th>\n",
       "      <td id=\"T_4f24e_row12_col0\" class=\"data row12 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_4f24e_row12_col1\" class=\"data row12 col1\" >0.5623</td>\n",
       "      <td id=\"T_4f24e_row12_col2\" class=\"data row12 col2\" >0.5424</td>\n",
       "      <td id=\"T_4f24e_row12_col3\" class=\"data row12 col3\" >0.6429</td>\n",
       "      <td id=\"T_4f24e_row12_col4\" class=\"data row12 col4\" >0.6339</td>\n",
       "      <td id=\"T_4f24e_row12_col5\" class=\"data row12 col5\" >0.6358</td>\n",
       "      <td id=\"T_4f24e_row12_col6\" class=\"data row12 col6\" >0.0852</td>\n",
       "      <td id=\"T_4f24e_row12_col7\" class=\"data row12 col7\" >0.0866</td>\n",
       "      <td id=\"T_4f24e_row12_col8\" class=\"data row12 col8\" >0.0850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4f24e_level0_row13\" class=\"row_heading level0 row13\" >qda</th>\n",
       "      <td id=\"T_4f24e_row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_4f24e_row13_col1\" class=\"data row13 col1\" >0.4003</td>\n",
       "      <td id=\"T_4f24e_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row13_col3\" class=\"data row13 col3\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row13_col4\" class=\"data row13 col4\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row13_col5\" class=\"data row13 col5\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row13_col6\" class=\"data row13 col6\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row13_col7\" class=\"data row13 col7\" >0.0000</td>\n",
       "      <td id=\"T_4f24e_row13_col8\" class=\"data row13 col8\" >0.0840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29d6f0400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classification_Models = Classification_Setup.compare_models(turbo = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KK66V4NXFy0n"
   },
   "source": [
    "You can use the multi-layer perceptron (\"mlp\") as outlined in the post, but a random forest (\"rf\") classification is more effective. A neural-network is a ***bit*** of an overkill."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "tY82ShLirM3B"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_89d9c_row10_col0, #T_89d9c_row10_col1, #T_89d9c_row10_col2, #T_89d9c_row10_col3, #T_89d9c_row10_col4, #T_89d9c_row10_col5, #T_89d9c_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_89d9c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_89d9c_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_89d9c_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th id=\"T_89d9c_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th id=\"T_89d9c_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th id=\"T_89d9c_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th id=\"T_89d9c_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th id=\"T_89d9c_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_89d9c_row0_col0\" class=\"data row0 col0\" >0.6852</td>\n",
       "      <td id=\"T_89d9c_row0_col1\" class=\"data row0 col1\" >0.6537</td>\n",
       "      <td id=\"T_89d9c_row0_col2\" class=\"data row0 col2\" >0.6667</td>\n",
       "      <td id=\"T_89d9c_row0_col3\" class=\"data row0 col3\" >0.7857</td>\n",
       "      <td id=\"T_89d9c_row0_col4\" class=\"data row0 col4\" >0.7213</td>\n",
       "      <td id=\"T_89d9c_row0_col5\" class=\"data row0 col5\" >0.3651</td>\n",
       "      <td id=\"T_89d9c_row0_col6\" class=\"data row0 col6\" >0.3717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_89d9c_row1_col0\" class=\"data row1 col0\" >0.6481</td>\n",
       "      <td id=\"T_89d9c_row1_col1\" class=\"data row1 col1\" >0.6883</td>\n",
       "      <td id=\"T_89d9c_row1_col2\" class=\"data row1 col2\" >0.5758</td>\n",
       "      <td id=\"T_89d9c_row1_col3\" class=\"data row1 col3\" >0.7917</td>\n",
       "      <td id=\"T_89d9c_row1_col4\" class=\"data row1 col4\" >0.6667</td>\n",
       "      <td id=\"T_89d9c_row1_col5\" class=\"data row1 col5\" >0.3133</td>\n",
       "      <td id=\"T_89d9c_row1_col6\" class=\"data row1 col6\" >0.3313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_89d9c_row2_col0\" class=\"data row2 col0\" >0.6296</td>\n",
       "      <td id=\"T_89d9c_row2_col1\" class=\"data row2 col1\" >0.7145</td>\n",
       "      <td id=\"T_89d9c_row2_col2\" class=\"data row2 col2\" >0.5312</td>\n",
       "      <td id=\"T_89d9c_row2_col3\" class=\"data row2 col3\" >0.7727</td>\n",
       "      <td id=\"T_89d9c_row2_col4\" class=\"data row2 col4\" >0.6296</td>\n",
       "      <td id=\"T_89d9c_row2_col5\" class=\"data row2 col5\" >0.2838</td>\n",
       "      <td id=\"T_89d9c_row2_col6\" class=\"data row2 col6\" >0.3040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_89d9c_row3_col0\" class=\"data row3 col0\" >0.5926</td>\n",
       "      <td id=\"T_89d9c_row3_col1\" class=\"data row3 col1\" >0.6548</td>\n",
       "      <td id=\"T_89d9c_row3_col2\" class=\"data row3 col2\" >0.5312</td>\n",
       "      <td id=\"T_89d9c_row3_col3\" class=\"data row3 col3\" >0.7083</td>\n",
       "      <td id=\"T_89d9c_row3_col4\" class=\"data row3 col4\" >0.6071</td>\n",
       "      <td id=\"T_89d9c_row3_col5\" class=\"data row3 col5\" >0.2016</td>\n",
       "      <td id=\"T_89d9c_row3_col6\" class=\"data row3 col6\" >0.2107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_89d9c_row4_col0\" class=\"data row4 col0\" >0.6852</td>\n",
       "      <td id=\"T_89d9c_row4_col1\" class=\"data row4 col1\" >0.7060</td>\n",
       "      <td id=\"T_89d9c_row4_col2\" class=\"data row4 col2\" >0.7188</td>\n",
       "      <td id=\"T_89d9c_row4_col3\" class=\"data row4 col3\" >0.7419</td>\n",
       "      <td id=\"T_89d9c_row4_col4\" class=\"data row4 col4\" >0.7302</td>\n",
       "      <td id=\"T_89d9c_row4_col5\" class=\"data row4 col5\" >0.3526</td>\n",
       "      <td id=\"T_89d9c_row4_col6\" class=\"data row4 col6\" >0.3529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_89d9c_row5_col0\" class=\"data row5 col0\" >0.6111</td>\n",
       "      <td id=\"T_89d9c_row5_col1\" class=\"data row5 col1\" >0.5980</td>\n",
       "      <td id=\"T_89d9c_row5_col2\" class=\"data row5 col2\" >0.5938</td>\n",
       "      <td id=\"T_89d9c_row5_col3\" class=\"data row5 col3\" >0.7037</td>\n",
       "      <td id=\"T_89d9c_row5_col4\" class=\"data row5 col4\" >0.6441</td>\n",
       "      <td id=\"T_89d9c_row5_col5\" class=\"data row5 col5\" >0.2222</td>\n",
       "      <td id=\"T_89d9c_row5_col6\" class=\"data row5 col6\" >0.2261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_89d9c_row6_col0\" class=\"data row6 col0\" >0.7037</td>\n",
       "      <td id=\"T_89d9c_row6_col1\" class=\"data row6 col1\" >0.7713</td>\n",
       "      <td id=\"T_89d9c_row6_col2\" class=\"data row6 col2\" >0.6875</td>\n",
       "      <td id=\"T_89d9c_row6_col3\" class=\"data row6 col3\" >0.7857</td>\n",
       "      <td id=\"T_89d9c_row6_col4\" class=\"data row6 col4\" >0.7333</td>\n",
       "      <td id=\"T_89d9c_row6_col5\" class=\"data row6 col5\" >0.4033</td>\n",
       "      <td id=\"T_89d9c_row6_col6\" class=\"data row6 col6\" >0.4079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_89d9c_row7_col0\" class=\"data row7 col0\" >0.6038</td>\n",
       "      <td id=\"T_89d9c_row7_col1\" class=\"data row7 col1\" >0.6622</td>\n",
       "      <td id=\"T_89d9c_row7_col2\" class=\"data row7 col2\" >0.6250</td>\n",
       "      <td id=\"T_89d9c_row7_col3\" class=\"data row7 col3\" >0.6897</td>\n",
       "      <td id=\"T_89d9c_row7_col4\" class=\"data row7 col4\" >0.6557</td>\n",
       "      <td id=\"T_89d9c_row7_col5\" class=\"data row7 col5\" >0.1917</td>\n",
       "      <td id=\"T_89d9c_row7_col6\" class=\"data row7 col6\" >0.1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_89d9c_row8_col0\" class=\"data row8 col0\" >0.6604</td>\n",
       "      <td id=\"T_89d9c_row8_col1\" class=\"data row8 col1\" >0.7009</td>\n",
       "      <td id=\"T_89d9c_row8_col2\" class=\"data row8 col2\" >0.6250</td>\n",
       "      <td id=\"T_89d9c_row8_col3\" class=\"data row8 col3\" >0.7692</td>\n",
       "      <td id=\"T_89d9c_row8_col4\" class=\"data row8 col4\" >0.6897</td>\n",
       "      <td id=\"T_89d9c_row8_col5\" class=\"data row8 col5\" >0.3234</td>\n",
       "      <td id=\"T_89d9c_row8_col6\" class=\"data row8 col6\" >0.3320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_89d9c_row9_col0\" class=\"data row9 col0\" >0.6792</td>\n",
       "      <td id=\"T_89d9c_row9_col1\" class=\"data row9 col1\" >0.6979</td>\n",
       "      <td id=\"T_89d9c_row9_col2\" class=\"data row9 col2\" >0.6875</td>\n",
       "      <td id=\"T_89d9c_row9_col3\" class=\"data row9 col3\" >0.7586</td>\n",
       "      <td id=\"T_89d9c_row9_col4\" class=\"data row9 col4\" >0.7213</td>\n",
       "      <td id=\"T_89d9c_row9_col5\" class=\"data row9 col5\" >0.3457</td>\n",
       "      <td id=\"T_89d9c_row9_col6\" class=\"data row9 col6\" >0.3480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_89d9c_row10_col0\" class=\"data row10 col0\" >0.6499</td>\n",
       "      <td id=\"T_89d9c_row10_col1\" class=\"data row10 col1\" >0.6848</td>\n",
       "      <td id=\"T_89d9c_row10_col2\" class=\"data row10 col2\" >0.6242</td>\n",
       "      <td id=\"T_89d9c_row10_col3\" class=\"data row10 col3\" >0.7507</td>\n",
       "      <td id=\"T_89d9c_row10_col4\" class=\"data row10 col4\" >0.6799</td>\n",
       "      <td id=\"T_89d9c_row10_col5\" class=\"data row10 col5\" >0.3003</td>\n",
       "      <td id=\"T_89d9c_row10_col6\" class=\"data row10 col6\" >0.3077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_89d9c_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_89d9c_row11_col0\" class=\"data row11 col0\" >0.0370</td>\n",
       "      <td id=\"T_89d9c_row11_col1\" class=\"data row11 col1\" >0.0438</td>\n",
       "      <td id=\"T_89d9c_row11_col2\" class=\"data row11 col2\" >0.0627</td>\n",
       "      <td id=\"T_89d9c_row11_col3\" class=\"data row11 col3\" >0.0358</td>\n",
       "      <td id=\"T_89d9c_row11_col4\" class=\"data row11 col4\" >0.0433</td>\n",
       "      <td id=\"T_89d9c_row11_col5\" class=\"data row11 col5\" >0.0694</td>\n",
       "      <td id=\"T_89d9c_row11_col6\" class=\"data row11 col6\" >0.0694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2a49c9b80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "model_name = \"nb\"\n",
    "Classification_Model = Classification_Setup.create_model(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "ZKYRxxPLXsE3"
   },
   "outputs": [
    {
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_dd6d4_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_dd6d4_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
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       "      <th id=\"T_dd6d4_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_dd6d4_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_dd6d4_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_dd6d4_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
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       "      <th id=\"T_dd6d4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_dd6d4_row0_col0\" class=\"data row0 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_dd6d4_row0_col1\" class=\"data row0 col1\" >0.5887</td>\n",
       "      <td id=\"T_dd6d4_row0_col2\" class=\"data row0 col2\" >0.6450</td>\n",
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       "      <td id=\"T_dd6d4_row0_col6\" class=\"data row0 col6\" >0.1864</td>\n",
       "      <td id=\"T_dd6d4_row0_col7\" class=\"data row0 col7\" >0.1924</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>game_datetime</th>\n",
       "      <th>team_1</th>\n",
       "      <th>team_1_spread_odds</th>\n",
       "      <th>team_2</th>\n",
       "      <th>team_2_spread_odds</th>\n",
       "      <th>venue_name</th>\n",
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       "      <th>prediction_label</th>\n",
       "      <th>prediction_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>2023-06-06 01:10:00</td>\n",
       "      <td>San Diego Padres</td>\n",
       "      <td>-105</td>\n",
       "      <td>Chicago Cubs</td>\n",
       "      <td>-125</td>\n",
       "      <td>Petco Park</td>\n",
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       "      <td>0.5217</td>\n",
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       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>2023-06-06 22:40:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
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       "      <td>Minnesota Twins</td>\n",
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       "      <td>Tropicana Field</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>2023-06-06 22:40:00</td>\n",
       "      <td>Miami Marlins</td>\n",
       "      <td>-135</td>\n",
       "      <td>Kansas City Royals</td>\n",
       "      <td>105</td>\n",
       "      <td>loanDepot park</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9572</td>\n",
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       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>2023-06-06 23:05:00</td>\n",
       "      <td>New York Yankees</td>\n",
       "      <td>135</td>\n",
       "      <td>Chicago White Sox</td>\n",
       "      <td>-165</td>\n",
       "      <td>Yankee Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2023-06-06 23:20:00</td>\n",
       "      <td>Atlanta Braves</td>\n",
       "      <td>105</td>\n",
       "      <td>New York Mets</td>\n",
       "      <td>-135</td>\n",
       "      <td>Truist Park</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>2023-06-07 01:38:00</td>\n",
       "      <td>Los Angeles Angels</td>\n",
       "      <td>130</td>\n",
       "      <td>Chicago Cubs</td>\n",
       "      <td>-160</td>\n",
       "      <td>Angel Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>2023-06-07 16:35:00</td>\n",
       "      <td>Pittsburgh Pirates</td>\n",
       "      <td>125</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>-155</td>\n",
       "      <td>PNC Park</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>2023-06-07 20:10:00</td>\n",
       "      <td>San Diego Padres</td>\n",
       "      <td>115</td>\n",
       "      <td>Seattle Mariners</td>\n",
       "      <td>-145</td>\n",
       "      <td>Petco Park</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>2023-06-07 23:10:00</td>\n",
       "      <td>Cleveland Guardians</td>\n",
       "      <td>-130</td>\n",
       "      <td>Boston Red Sox</td>\n",
       "      <td>100</td>\n",
       "      <td>Progressive Field</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>2023-06-07 23:40:00</td>\n",
       "      <td>Milwaukee Brewers</td>\n",
       "      <td>-135</td>\n",
       "      <td>Baltimore Orioles</td>\n",
       "      <td>105</td>\n",
       "      <td>American Family Field</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>2023-06-08 01:38:00</td>\n",
       "      <td>Los Angeles Angels</td>\n",
       "      <td>-125</td>\n",
       "      <td>Chicago Cubs</td>\n",
       "      <td>-105</td>\n",
       "      <td>Angel Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>2023-06-08 20:10:00</td>\n",
       "      <td>New York Yankees</td>\n",
       "      <td>160</td>\n",
       "      <td>Chicago White Sox</td>\n",
       "      <td>-190</td>\n",
       "      <td>Yankee Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>2023-06-08 23:10:00</td>\n",
       "      <td>Cleveland Guardians</td>\n",
       "      <td>105</td>\n",
       "      <td>Boston Red Sox</td>\n",
       "      <td>-135</td>\n",
       "      <td>Progressive Field</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>2023-06-09 22:40:00</td>\n",
       "      <td>Tampa Bay Rays</td>\n",
       "      <td>125</td>\n",
       "      <td>Texas Rangers</td>\n",
       "      <td>-155</td>\n",
       "      <td>Tropicana Field</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>2023-06-09 23:05:00</td>\n",
       "      <td>Pittsburgh Pirates</td>\n",
       "      <td>120</td>\n",
       "      <td>New York Mets</td>\n",
       "      <td>-150</td>\n",
       "      <td>PNC Park</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2023-06-10 00:10:00</td>\n",
       "      <td>Milwaukee Brewers</td>\n",
       "      <td>110</td>\n",
       "      <td>Oakland Athletics</td>\n",
       "      <td>-140</td>\n",
       "      <td>American Family Field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>2023-06-10 00:15:00</td>\n",
       "      <td>St. Louis Cardinals</td>\n",
       "      <td>-110</td>\n",
       "      <td>Cincinnati Reds</td>\n",
       "      <td>-120</td>\n",
       "      <td>Busch Stadium</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>2023-06-10 18:15:00</td>\n",
       "      <td>St. Louis Cardinals</td>\n",
       "      <td>100</td>\n",
       "      <td>Cincinnati Reds</td>\n",
       "      <td>-130</td>\n",
       "      <td>Busch Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>2023-06-10 19:07:00</td>\n",
       "      <td>Toronto Blue Jays</td>\n",
       "      <td>105</td>\n",
       "      <td>Minnesota Twins</td>\n",
       "      <td>-135</td>\n",
       "      <td>Rogers Centre</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023-06-10 20:05:00</td>\n",
       "      <td>Baltimore Orioles</td>\n",
       "      <td>-145</td>\n",
       "      <td>Kansas City Royals</td>\n",
       "      <td>115</td>\n",
       "      <td>Oriole Park at Camden Yards</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          game_datetime               team_1  team_1_spread_odds  \\\n",
       "537 2023-06-06 01:10:00     San Diego Padres                -105   \n",
       "538 2023-06-06 22:40:00       Tampa Bay Rays                 145   \n",
       "539 2023-06-06 22:40:00        Miami Marlins                -135   \n",
       "540 2023-06-06 23:05:00     New York Yankees                 135   \n",
       "541 2023-06-06 23:20:00       Atlanta Braves                 105   \n",
       "542 2023-06-07 01:38:00   Los Angeles Angels                 130   \n",
       "543 2023-06-07 16:35:00   Pittsburgh Pirates                 125   \n",
       "544 2023-06-07 20:10:00     San Diego Padres                 115   \n",
       "545 2023-06-07 23:10:00  Cleveland Guardians                -130   \n",
       "546 2023-06-07 23:40:00    Milwaukee Brewers                -135   \n",
       "547 2023-06-08 01:38:00   Los Angeles Angels                -125   \n",
       "548 2023-06-08 20:10:00     New York Yankees                 160   \n",
       "549 2023-06-08 23:10:00  Cleveland Guardians                 105   \n",
       "550 2023-06-09 22:40:00       Tampa Bay Rays                 125   \n",
       "551 2023-06-09 23:05:00   Pittsburgh Pirates                 120   \n",
       "552 2023-06-10 00:10:00    Milwaukee Brewers                 110   \n",
       "553 2023-06-10 00:15:00  St. Louis Cardinals                -110   \n",
       "554 2023-06-10 18:15:00  St. Louis Cardinals                 100   \n",
       "555 2023-06-10 19:07:00    Toronto Blue Jays                 105   \n",
       "556 2023-06-10 20:05:00    Baltimore Orioles                -145   \n",
       "\n",
       "                 team_2  team_2_spread_odds                   venue_name  \\\n",
       "537        Chicago Cubs                -125                   Petco Park   \n",
       "538     Minnesota Twins                -180              Tropicana Field   \n",
       "539  Kansas City Royals                 105               loanDepot park   \n",
       "540   Chicago White Sox                -165               Yankee Stadium   \n",
       "541       New York Mets                -135                  Truist Park   \n",
       "542        Chicago Cubs                -160                Angel Stadium   \n",
       "543   Oakland Athletics                -155                     PNC Park   \n",
       "544    Seattle Mariners                -145                   Petco Park   \n",
       "545      Boston Red Sox                 100            Progressive Field   \n",
       "546   Baltimore Orioles                 105        American Family Field   \n",
       "547        Chicago Cubs                -105                Angel Stadium   \n",
       "548   Chicago White Sox                -190               Yankee Stadium   \n",
       "549      Boston Red Sox                -135            Progressive Field   \n",
       "550       Texas Rangers                -155              Tropicana Field   \n",
       "551       New York Mets                -150                     PNC Park   \n",
       "552   Oakland Athletics                -140        American Family Field   \n",
       "553     Cincinnati Reds                -120                Busch Stadium   \n",
       "554     Cincinnati Reds                -130                Busch Stadium   \n",
       "555     Minnesota Twins                -135                Rogers Centre   \n",
       "556  Kansas City Royals                 115  Oriole Park at Camden Yards   \n",
       "\n",
       "     spread  prediction_label  prediction_score  \n",
       "537       1                 0            0.5217  \n",
       "538       1                 1            0.5807  \n",
       "539       1                 1            0.9572  \n",
       "540       0                 0            0.7904  \n",
       "541       1                 0            0.7028  \n",
       "542       1                 0            0.9254  \n",
       "543       0                 1            0.9762  \n",
       "544       1                 0            0.8263  \n",
       "545       1                 1            0.8284  \n",
       "546       1                 1            0.9557  \n",
       "547       1                 0            0.6110  \n",
       "548       1                 0            0.8112  \n",
       "549       1                 0            0.9174  \n",
       "550       1                 1            0.6243  \n",
       "551       1                 0            0.6938  \n",
       "552       0                 1            0.9741  \n",
       "553       1                 0            0.8492  \n",
       "554       0                 0            0.9643  \n",
       "555       0                 0            0.7304  \n",
       "556       1                 1            0.9787  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prediction = Classification_Setup.predict_model(Classification_Model)\n",
    "Prediction.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "Cjj3McLgxFfT"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a090a98868047318cd0849f921aad50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Classification_Setup.evaluate_model(Classification_Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['game_datetime_year', 'game_datetime_month', 'game_datetime_day',\n",
       "       'team_1', 'team_1_spread_odds', 'team_2', 'team_2_spread_odds',\n",
       "       'venue_name'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classification_Model.feature_names_in_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "odO3tScVvMTy"
   },
   "source": [
    "***Save Model***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "d16cozQ4vLxv"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Saved\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(memory=FastMemory(location=/var/folders/2c/yw3tyqqx7tn_wgl7j28qh2dw0000gn/T/joblib),\n",
       "          steps=[('date_feature_extractor',\n",
       "                  TransformerWrapper(exclude=None, include=['game_datetime'],\n",
       "                                     transformer=ExtractDateTimeFeatures(features=['day',\n",
       "                                                                                   'month',\n",
       "                                                                                   'year']))),\n",
       "                 ('numerical_imputer',\n",
       "                  TransformerWrapper(exclude=None,\n",
       "                                     include=['team_1_spread_odds',\n",
       "                                              'team_2_spre...\n",
       "                  TransformerWrapper(exclude=None,\n",
       "                                     include=['team_1', 'team_2', 'venue_name'],\n",
       "                                     transformer=TargetEncoder(cols=['team_1',\n",
       "                                                                     'team_2',\n",
       "                                                                     'venue_name'],\n",
       "                                                               drop_invariant=False,\n",
       "                                                               handle_missing='return_nan',\n",
       "                                                               handle_unknown='value',\n",
       "                                                               hierarchy=None,\n",
       "                                                               min_samples_leaf=20,\n",
       "                                                               return_df=True,\n",
       "                                                               smoothing=10,\n",
       "                                                               verbose=0))),\n",
       "                 ('actual_estimator',\n",
       "                  GaussianNB(priors=None, var_smoothing=1e-09))],\n",
       "          verbose=False),\n",
       " 'models/2023-08-12_Baseball_Spread_model_nb.pkl')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Finalized_Model = pycaret.classification.finalize_model(Classification_Model)\n",
    "Finalized_Model_save_to_file_string = f\"models/{datetime.today().strftime('%Y-%m-%d')}_Baseball_Spread_model_{model_name}\"\n",
    "pycaret.classification.save_model(Finalized_Model, Finalized_Model_save_to_file_string)\n",
    "#files.download(f\"{Finalized_Model_save_to_file_string}.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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