Simple horse model v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: category_encoders in c:\\users\\don\\anaconda3\\lib\\site-packages (1.3.0)\n",
      "Requirement already satisfied: patsy>=0.4.1 in c:\\users\\don\\anaconda3\\lib\\site-packages (from category_encoders) (0.5.1)\n",
      "Requirement already satisfied: statsmodels>=0.6.1 in c:\\users\\don\\anaconda3\\lib\\site-packages (from category_encoders) (0.9.0)\n",
      "Requirement already satisfied: scikit-learn>=0.17.1 in c:\\users\\don\\anaconda3\\lib\\site-packages (from category_encoders) (0.20.1)\n",
      "Requirement already satisfied: pandas>=0.20.1 in c:\\users\\don\\anaconda3\\lib\\site-packages (from category_encoders) (0.23.4)\n",
      "Requirement already satisfied: numpy>=1.11.1 in c:\\users\\don\\anaconda3\\lib\\site-packages (from category_encoders) (1.15.4)\n",
      "Requirement already satisfied: scipy>=0.17.0 in c:\\users\\don\\anaconda3\\lib\\site-packages (from category_encoders) (1.1.0)\n",
      "Requirement already satisfied: six in c:\\users\\don\\appdata\\roaming\\python\\python37\\site-packages (from patsy>=0.4.1->category_encoders) (1.12.0)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\users\\don\\anaconda3\\lib\\site-packages (from pandas>=0.20.1->category_encoders) (2.7.5)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\don\\anaconda3\\lib\\site-packages (from pandas>=0.20.1->category_encoders) (2018.7)\n"
     ]
    }
   ],
   "source": [
    "!pip install category_encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import category_encoders as ce\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "races = pd.read_csv('2018_2017.csv')\n",
    "# races = races.iloc[:, :-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Track Name</th>\n",
       "      <th>Race Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Track Miles</th>\n",
       "      <th>Surface</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Track Condition</th>\n",
       "      <th>Race</th>\n",
       "      <th>Horse Name</th>\n",
       "      <th>Jockey</th>\n",
       "      <th>Weight</th>\n",
       "      <th>M/E</th>\n",
       "      <th>PP</th>\n",
       "      <th>Finish</th>\n",
       "      <th>Money Won</th>\n",
       "      <th>Trainer</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Lengths Behind Winner</th>\n",
       "      <th>Length Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OAKLAWN PARK</td>\n",
       "      <td>Race 11</td>\n",
       "      <td>April 14, 2018</td>\n",
       "      <td>One And One Eighth</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>THREE YEAR</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Fast</td>\n",
       "      <td>Arkansas Derby Grade 1 - Thoroughbred</td>\n",
       "      <td>Magnum Moon</td>\n",
       "      <td>Saez, Luis</td>\n",
       "      <td>122</td>\n",
       "      <td>L</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>$600,000</td>\n",
       "      <td>Pletcher, Todd</td>\n",
       "      <td>Low, Lawana L. and Robert E.</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OAKLAWN PARK</td>\n",
       "      <td>Race 11</td>\n",
       "      <td>April 14, 2018</td>\n",
       "      <td>One And One Eighth</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>THREE YEAR</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Fast</td>\n",
       "      <td>Arkansas Derby Grade 1 - Thoroughbred</td>\n",
       "      <td>Quip</td>\n",
       "      <td>Geroux, Florent</td>\n",
       "      <td>122</td>\n",
       "      <td>L</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>$200,000</td>\n",
       "      <td>Brisset, Rodolphe</td>\n",
       "      <td>WinStar Farm LLC, China Horse Club Internatio...</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OAKLAWN PARK</td>\n",
       "      <td>Race 11</td>\n",
       "      <td>April 14, 2018</td>\n",
       "      <td>One And One Eighth</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>THREE YEAR</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Fast</td>\n",
       "      <td>Arkansas Derby Grade 1 - Thoroughbred</td>\n",
       "      <td>Solomini</td>\n",
       "      <td>Prat, Flavien</td>\n",
       "      <td>118</td>\n",
       "      <td>L b</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>$100,000</td>\n",
       "      <td>Baffert, Bob</td>\n",
       "      <td>Zayat Stables, LLC, Magnier, Mrs. John, Tabor...</td>\n",
       "      <td>4.25</td>\n",
       "      <td>12.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OAKLAWN PARK</td>\n",
       "      <td>Race 11</td>\n",
       "      <td>April 14, 2018</td>\n",
       "      <td>One And One Eighth</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>THREE YEAR</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Fast</td>\n",
       "      <td>Arkansas Derby Grade 1 - Thoroughbred</td>\n",
       "      <td>Combatant</td>\n",
       "      <td>Santana, Jr., Ricardo</td>\n",
       "      <td>118</td>\n",
       "      <td>L</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>$50,000</td>\n",
       "      <td>Asmussen, Steven</td>\n",
       "      <td>Winchell Thoroughbreds LLC and Willis Horton ...</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OAKLAWN PARK</td>\n",
       "      <td>Race 11</td>\n",
       "      <td>April 14, 2018</td>\n",
       "      <td>One And One Eighth</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>THREE YEAR</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Fast</td>\n",
       "      <td>Arkansas Derby Grade 1 - Thoroughbred</td>\n",
       "      <td>Tenfold</td>\n",
       "      <td>Espinoza, Victor</td>\n",
       "      <td>118</td>\n",
       "      <td>L</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>$30,000</td>\n",
       "      <td>Asmussen, Steven</td>\n",
       "      <td>Winchell Thoroughbreds LLC</td>\n",
       "      <td>4.50</td>\n",
       "      <td>22.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Track Name Race Number              Date          Track Miles Surface  \\\n",
       "0  OAKLAWN PARK      Race 11   April 14, 2018   One And One Eighth     Dirt   \n",
       "1  OAKLAWN PARK      Race 11   April 14, 2018   One And One Eighth     Dirt   \n",
       "2  OAKLAWN PARK      Race 11   April 14, 2018   One And One Eighth     Dirt   \n",
       "3  OAKLAWN PARK      Race 11   April 14, 2018   One And One Eighth     Dirt   \n",
       "4  OAKLAWN PARK      Race 11   April 14, 2018   One And One Eighth     Dirt   \n",
       "\n",
       "           Age  Weather Track Condition  \\\n",
       "0  THREE YEAR    Cloudy            Fast   \n",
       "1  THREE YEAR    Cloudy            Fast   \n",
       "2  THREE YEAR    Cloudy            Fast   \n",
       "3  THREE YEAR    Cloudy            Fast   \n",
       "4  THREE YEAR    Cloudy            Fast   \n",
       "\n",
       "                                    Race    Horse Name                 Jockey  \\\n",
       "0  Arkansas Derby Grade 1 - Thoroughbred  Magnum Moon              Saez, Luis   \n",
       "1  Arkansas Derby Grade 1 - Thoroughbred         Quip         Geroux, Florent   \n",
       "2  Arkansas Derby Grade 1 - Thoroughbred     Solomini           Prat, Flavien   \n",
       "3  Arkansas Derby Grade 1 - Thoroughbred    Combatant   Santana, Jr., Ricardo   \n",
       "4  Arkansas Derby Grade 1 - Thoroughbred      Tenfold        Espinoza, Victor   \n",
       "\n",
       "   Weight  M/E  PP  Finish  Money Won             Trainer  \\\n",
       "0     122    L   6       1  $600,000       Pletcher, Todd   \n",
       "1     122    L   8       2  $200,000    Brisset, Rodolphe   \n",
       "2     118  L b   5       3  $100,000         Baffert, Bob   \n",
       "3     118    L   9       4   $50,000     Asmussen, Steven   \n",
       "4     118    L   3       5   $30,000     Asmussen, Steven   \n",
       "\n",
       "                                               Owner  Lengths Behind Winner  \\\n",
       "0                       Low, Lawana L. and Robert E.                   0.00   \n",
       "1   WinStar Farm LLC, China Horse Club Internatio...                   4.00   \n",
       "2   Zayat Stables, LLC, Magnier, Mrs. John, Tabor...                   4.25   \n",
       "3   Winchell Thoroughbreds LLC and Willis Horton ...                   4.25   \n",
       "4                         Winchell Thoroughbreds LLC                   4.50   \n",
       "\n",
       "   Length Rating  \n",
       "0           0.00  \n",
       "1           8.00  \n",
       "2          12.75  \n",
       "3          17.00  \n",
       "4          22.50  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "races.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(453, 20)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "races.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('2018_Derby_actual.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Track Miles</th>\n",
       "      <th>Surface</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Track Condition</th>\n",
       "      <th>Horse Name</th>\n",
       "      <th>Jockey</th>\n",
       "      <th>Weight</th>\n",
       "      <th>M/E</th>\n",
       "      <th>Actual Finish</th>\n",
       "      <th>Trainer</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Average Length Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One And One Fourth Miles</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>Showery</td>\n",
       "      <td>Sloppy (Sealed)</td>\n",
       "      <td>Justify</td>\n",
       "      <td>Smith, Mike</td>\n",
       "      <td>126</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>Baffert, Bob</td>\n",
       "      <td>China Horse Club, Head of Plains Partners LLC...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>One And One Fourth Miles</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>Showery</td>\n",
       "      <td>Sloppy (Sealed)</td>\n",
       "      <td>Good Magic</td>\n",
       "      <td>Ortiz, Jose</td>\n",
       "      <td>126</td>\n",
       "      <td>L</td>\n",
       "      <td>2</td>\n",
       "      <td>Brown, Chad</td>\n",
       "      <td>e Five Racing Thoroughbreds and Stonestreet S...</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>One And One Fourth Miles</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>Showery</td>\n",
       "      <td>Sloppy (Sealed)</td>\n",
       "      <td>Audible</td>\n",
       "      <td>Castellano, Javier</td>\n",
       "      <td>126</td>\n",
       "      <td>L</td>\n",
       "      <td>3</td>\n",
       "      <td>Pletcher, Todd</td>\n",
       "      <td>China Horse Club, Head of Plains Partners LLC...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>One And One Fourth Miles</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>Showery</td>\n",
       "      <td>Sloppy (Sealed)</td>\n",
       "      <td>Instilled Regard</td>\n",
       "      <td>Van Dyke, Drayden</td>\n",
       "      <td>126</td>\n",
       "      <td>L</td>\n",
       "      <td>4</td>\n",
       "      <td>Hollendorfer, Jerry</td>\n",
       "      <td>OXO Equine LLC</td>\n",
       "      <td>17.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>One And One Fourth Miles</td>\n",
       "      <td>Dirt</td>\n",
       "      <td>Showery</td>\n",
       "      <td>Sloppy (Sealed)</td>\n",
       "      <td>My Boy Jack</td>\n",
       "      <td>Desormeaux, Kent</td>\n",
       "      <td>126</td>\n",
       "      <td>L</td>\n",
       "      <td>5</td>\n",
       "      <td>Desormeaux, J.</td>\n",
       "      <td>Don't Tell My Wife Stables, Monomoy Stables, ...</td>\n",
       "      <td>6.187500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Track Miles Surface  Weather  Track Condition  \\\n",
       "0  One And One Fourth Miles    Dirt  Showery  Sloppy (Sealed)   \n",
       "1  One And One Fourth Miles    Dirt  Showery  Sloppy (Sealed)   \n",
       "2  One And One Fourth Miles    Dirt  Showery  Sloppy (Sealed)   \n",
       "3  One And One Fourth Miles    Dirt  Showery  Sloppy (Sealed)   \n",
       "4  One And One Fourth Miles    Dirt  Showery  Sloppy (Sealed)   \n",
       "\n",
       "          Horse Name              Jockey  Weight M/E  Actual Finish  \\\n",
       "0           Justify          Smith, Mike     126   L              1   \n",
       "1        Good Magic          Ortiz, Jose     126   L              2   \n",
       "2           Audible   Castellano, Javier     126   L              3   \n",
       "3  Instilled Regard    Van Dyke, Drayden     126   L              4   \n",
       "4       My Boy Jack     Desormeaux, Kent     126   L              5   \n",
       "\n",
       "                Trainer                                              Owner  \\\n",
       "0          Baffert, Bob   China Horse Club, Head of Plains Partners LLC...   \n",
       "1           Brown, Chad   e Five Racing Thoroughbreds and Stonestreet S...   \n",
       "2        Pletcher, Todd   China Horse Club, Head of Plains Partners LLC...   \n",
       "3   Hollendorfer, Jerry                                     OXO Equine LLC   \n",
       "4        Desormeaux, J.   Don't Tell My Wife Stables, Monomoy Stables, ...   \n",
       "\n",
       "   Average Length Rating  \n",
       "0               0.000000  \n",
       "1               6.750000  \n",
       "2               0.000000  \n",
       "3              17.333333  \n",
       "4               6.187500  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = races['Finish']\n",
    "X_train = races.drop(columns=['Finish', 'Track Name',\n",
    "'Race Number',\n",
    "'Date',\n",
    "'Age',\n",
    "'Race',\n",
    "'PP',\n",
    "'Money Won',\n",
    "'Lengths Behind Winner'\n",
    "])\n",
    "X_test  = test_df.drop(columns=['Actual Finish'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Don\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\Don\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:465: DataConversionWarning: Data with input dtype int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('onehotencoder', OneHotEncoder(cols=['Track Miles', 'Surface', 'Weather', 'Track Condition', 'Horse Name', 'Jockey', 'M/E', 'Trainer', 'Owner'],\n",
       "       drop_invariant=False, handle_unknown='impute', impute_missing=True,\n",
       "       return_df=True, use_cat_names=True, verbose=0)), ('standardscaler...ors=10000, n_jobs=-1, oob_score=False,\n",
       "            random_state=None, verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = make_pipeline(\n",
    "    ce.OneHotEncoder(use_cat_names=True), \n",
    "    StandardScaler(),  \n",
    "    RandomForestClassifier(class_weight='balanced', \n",
    "                            n_jobs=-1, \n",
    "                            n_estimators=10000))\n",
    "\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Don\\Anaconda3\\lib\\site-packages\\sklearn\\pipeline.py:331: DataConversionWarning: Data with input dtype int32, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "y_pred = pipeline.predict(X_test)"
   ]
  },
  {
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    "y_val  = test_df['Actual Finish']\n"
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