{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1. Gather data\n",
    "#### Import data\n",
    "First and second rows of csv are set as headers. We only need first so drop second."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(10169, 35)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv', header=[0])\n",
    "df = df.drop(df.index[[0]])\n",
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Drop columns that are not required"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(10169, 34)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['Ball'])\n",
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2. Assess/Clean\n",
    "#### List remaining col names"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "['Club',\n 'Club Speed',\n 'Attack Angle',\n 'Club Path',\n 'Low Point',\n 'Swing Plane',\n 'Swing Direction',\n 'Dyn. Loft',\n 'Face Angle',\n 'Face To Path',\n 'Ball Speed',\n 'Smash Factor',\n 'Launch Angle',\n 'Launch Direction',\n 'Spin Rate',\n 'Spin Axis',\n 'Max Height - Dist.',\n 'Max Height - Height',\n 'Max Height - Side',\n 'Last data Point - Length',\n 'Last data Point - Side',\n 'Last data Point - Height',\n 'Last data Point - Time',\n 'Carry Flat - Length',\n 'Carry Flat - Side',\n 'Carry Flat - Land. Angle',\n 'Carry Flat - Ball Speed',\n 'Carry Flat - Time',\n 'Total',\n 'Side Total',\n 'Dynamic Lie',\n 'Impact Offset',\n 'Impact Height',\n 'Curve']"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Get only swings labelled as Driver"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "     Club   Club Speed Attack Angle Club Path     Low Point Swing Plane  \\\n1  Driver  87.85343593        0.775     2.541  -0.826771654      54.771   \n2  Driver  93.90882248        1.123     1.535  -1.220472441      53.859   \n3  Driver  94.77451682          NaN       NaN           NaN      51.358   \n4  Driver   94.3293665          NaN       NaN           NaN      52.158   \n5  Driver  92.26691124        0.477     0.671  -0.511811024      55.176   \n\n  Swing Direction Dyn. Loft Face Angle Face To Path  ... Carry Flat - Side  \\\n1           3.089    10.886     -3.002       -5.543  ...       -18.4510903   \n2           2.356     9.287      0.747       -0.788  ...       0.609679459   \n3           1.175       NaN        NaN          NaN  ...      -3.836037506   \n4          -2.023       NaN        NaN          NaN  ...      -8.590043774   \n5           1.003    12.803     -7.837       -8.507  ...      -48.00254026   \n\n  Carry Flat - Land. Angle Carry Flat - Ball Speed Carry Flat - Time  \\\n1              18.14669052             72.29461445           3.73667   \n2              15.34821803              78.7016999           3.46965   \n3              44.00031097             60.14810943           6.75169   \n4              39.48587065             60.07020526           6.30518   \n5              25.67797516             64.53268171           4.81192   \n\n         Total    Side Total Dynamic Lie Impact Offset Impact Height  \\\n1  211.1884376  -26.94829178         NaN           NaN           NaN   \n2  219.5067402   0.196844595         NaN           NaN           NaN   \n3  235.6025707  -4.084941913         NaN           NaN           NaN   \n4  235.0102209  -9.343194603         NaN           NaN           NaN   \n5  228.5802886  -60.62426861         NaN           NaN           NaN   \n\n          Curve  \n1  -35.33800773  \n2  -5.541544559  \n3           NaN  \n4           NaN  \n5   -75.9272069  \n\n[5 rows x 34 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Club</th>\n      <th>Club Speed</th>\n      <th>Attack Angle</th>\n      <th>Club Path</th>\n      <th>Low Point</th>\n      <th>Swing Plane</th>\n      <th>Swing Direction</th>\n      <th>Dyn. Loft</th>\n      <th>Face Angle</th>\n      <th>Face To Path</th>\n      <th>...</th>\n      <th>Carry Flat - Side</th>\n      <th>Carry Flat - Land. Angle</th>\n      <th>Carry Flat - Ball Speed</th>\n      <th>Carry Flat - Time</th>\n      <th>Total</th>\n      <th>Side Total</th>\n      <th>Dynamic Lie</th>\n      <th>Impact Offset</th>\n      <th>Impact Height</th>\n      <th>Curve</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>Driver</td>\n      <td>87.85343593</td>\n      <td>0.775</td>\n      <td>2.541</td>\n      <td>-0.826771654</td>\n      <td>54.771</td>\n      <td>3.089</td>\n      <td>10.886</td>\n      <td>-3.002</td>\n      <td>-5.543</td>\n      <td>...</td>\n      <td>-18.4510903</td>\n      <td>18.14669052</td>\n      <td>72.29461445</td>\n      <td>3.73667</td>\n      <td>211.1884376</td>\n      <td>-26.94829178</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-35.33800773</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Driver</td>\n      <td>93.90882248</td>\n      <td>1.123</td>\n      <td>1.535</td>\n      <td>-1.220472441</td>\n      <td>53.859</td>\n      <td>2.356</td>\n      <td>9.287</td>\n      <td>0.747</td>\n      <td>-0.788</td>\n      <td>...</td>\n      <td>0.609679459</td>\n      <td>15.34821803</td>\n      <td>78.7016999</td>\n      <td>3.46965</td>\n      <td>219.5067402</td>\n      <td>0.196844595</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.541544559</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Driver</td>\n      <td>94.77451682</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>51.358</td>\n      <td>1.175</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>-3.836037506</td>\n      <td>44.00031097</td>\n      <td>60.14810943</td>\n      <td>6.75169</td>\n      <td>235.6025707</td>\n      <td>-4.084941913</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Driver</td>\n      <td>94.3293665</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>52.158</td>\n      <td>-2.023</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>-8.590043774</td>\n      <td>39.48587065</td>\n      <td>60.07020526</td>\n      <td>6.30518</td>\n      <td>235.0102209</td>\n      <td>-9.343194603</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Driver</td>\n      <td>92.26691124</td>\n      <td>0.477</td>\n      <td>0.671</td>\n      <td>-0.511811024</td>\n      <td>55.176</td>\n      <td>1.003</td>\n      <td>12.803</td>\n      <td>-7.837</td>\n      <td>-8.507</td>\n      <td>...</td>\n      <td>-48.00254026</td>\n      <td>25.67797516</td>\n      <td>64.53268171</td>\n      <td>4.81192</td>\n      <td>228.5802886</td>\n      <td>-60.62426861</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-75.9272069</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 34 columns</p>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Club'] == 'Driver']\n",
    "df.head()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2. Assess/Clean\n",
    "#### Assess for missing data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x720 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "          Club   Club Speed Attack Angle Club Path    Low Point Swing Plane  \\\ncount    10169         9521         8697      8697         8697        9403   \nunique       1         7522         4757      4987          574        7514   \ntop     Driver  100.8254295          0.1         0  1.771653543      50.102   \nfreq     10169            5           60        45           47           6   \n\n       Swing Direction Dyn. Loft Face Angle Face To Path  ...  \\\ncount             9403      9079       9079         8695  ...   \nunique            7578      7443       6950         7138  ...   \ntop              6.562    15.355     -0.884        -3.75  ...   \nfreq                 6         5          7            6  ...   \n\n       Carry Flat - Side Carry Flat - Land. Angle Carry Flat - Ball Speed  \\\ncount              10169                    10152                   10152   \nunique             10153                    10152                   10141   \ntop                    0               10.4426753             63.91178222   \nfreq                  17                        1                       2   \n\n       Carry Flat - Time  Total Side Total  Dynamic Lie Impact Offset  \\\ncount              10169  10168      10168         2284          2331   \nunique             10136  10152      10152         2284          2331   \ntop                    0      0          0  68.54472867  -13.91762792   \nfreq                  17     17         17            1             1   \n\n       Impact Height        Curve  \ncount           2331         9231  \nunique          2331         9231  \ntop     -1.620595665  15.94575042  \nfreq               1            1  \n\n[4 rows x 34 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Club</th>\n      <th>Club Speed</th>\n      <th>Attack Angle</th>\n      <th>Club Path</th>\n      <th>Low Point</th>\n      <th>Swing Plane</th>\n      <th>Swing Direction</th>\n      <th>Dyn. Loft</th>\n      <th>Face Angle</th>\n      <th>Face To Path</th>\n      <th>...</th>\n      <th>Carry Flat - Side</th>\n      <th>Carry Flat - Land. Angle</th>\n      <th>Carry Flat - Ball Speed</th>\n      <th>Carry Flat - Time</th>\n      <th>Total</th>\n      <th>Side Total</th>\n      <th>Dynamic Lie</th>\n      <th>Impact Offset</th>\n      <th>Impact Height</th>\n      <th>Curve</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>10169</td>\n      <td>9521</td>\n      <td>8697</td>\n      <td>8697</td>\n      <td>8697</td>\n      <td>9403</td>\n      <td>9403</td>\n      <td>9079</td>\n      <td>9079</td>\n      <td>8695</td>\n      <td>...</td>\n      <td>10169</td>\n      <td>10152</td>\n      <td>10152</td>\n      <td>10169</td>\n      <td>10168</td>\n      <td>10168</td>\n      <td>2284</td>\n      <td>2331</td>\n      <td>2331</td>\n      <td>9231</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>1</td>\n      <td>7522</td>\n      <td>4757</td>\n      <td>4987</td>\n      <td>574</td>\n      <td>7514</td>\n      <td>7578</td>\n      <td>7443</td>\n      <td>6950</td>\n      <td>7138</td>\n      <td>...</td>\n      <td>10153</td>\n      <td>10152</td>\n      <td>10141</td>\n      <td>10136</td>\n      <td>10152</td>\n      <td>10152</td>\n      <td>2284</td>\n      <td>2331</td>\n      <td>2331</td>\n      <td>9231</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>Driver</td>\n      <td>100.8254295</td>\n      <td>0.1</td>\n      <td>0</td>\n      <td>1.771653543</td>\n      <td>50.102</td>\n      <td>6.562</td>\n      <td>15.355</td>\n      <td>-0.884</td>\n      <td>-3.75</td>\n      <td>...</td>\n      <td>0</td>\n      <td>10.4426753</td>\n      <td>63.91178222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>68.54472867</td>\n      <td>-13.91762792</td>\n      <td>-1.620595665</td>\n      <td>15.94575042</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>10169</td>\n      <td>5</td>\n      <td>60</td>\n      <td>45</td>\n      <td>47</td>\n      <td>6</td>\n      <td>6</td>\n      <td>5</td>\n      <td>7</td>\n      <td>6</td>\n      <td>...</td>\n      <td>17</td>\n      <td>1</td>\n      <td>2</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 34 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msno.matrix(df)\n",
    "plt.show()\n",
    "df.describe()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Drop columns that have a large proportion of missing data\n",
    "We would have liked to have kept both Impact offset and Impact height as this tells us how close the impact was to the sweet\n",
    "spot on the club head. But there is just too much missing data. However, smash factor is a good pseudo measure of\n",
    "this ability to hit the sweet spot as it is the ratio of ball speed to club speed, and you maximise this transfer of\n",
    "energy by hitting the sweet spot."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x720 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "         Club Speed Attack Angle Club Path    Low Point Swing Plane  \\\ncount          9521         8697      8697         8697        9403   \nunique         7522         4757      4987          574        7514   \ntop     100.8254295          0.1         0  1.771653543      50.102   \nfreq              5           60        45           47           6   \n\n       Swing Direction Dyn. Loft Face Angle Face To Path   Ball Speed  ...  \\\ncount             9403      9079       9079         8695        10165  ...   \nunique            7578      7443       6950         7138        10038  ...   \ntop              6.562    15.355     -0.884        -3.75  127.1315766  ...   \nfreq                 6         5          7            6            3  ...   \n\n       Last data Point - Height Last data Point - Time Carry Flat - Length  \\\ncount                     10169                  10169               10169   \nunique                    10153                   9616               10153   \ntop                           0                      0                   0   \nfreq                         17                     17                  17   \n\n       Carry Flat - Side Carry Flat - Land. Angle Carry Flat - Ball Speed  \\\ncount              10169                    10152                   10152   \nunique             10153                    10152                   10141   \ntop                    0               10.4426753             63.91178222   \nfreq                  17                        1                       2   \n\n       Carry Flat - Time  Total Side Total        Curve  \ncount              10169  10168      10168         9231  \nunique             10136  10152      10152         9231  \ntop                    0      0          0  15.94575042  \nfreq                  17     17         17            1  \n\n[4 rows x 30 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Club Speed</th>\n      <th>Attack Angle</th>\n      <th>Club Path</th>\n      <th>Low Point</th>\n      <th>Swing Plane</th>\n      <th>Swing Direction</th>\n      <th>Dyn. Loft</th>\n      <th>Face Angle</th>\n      <th>Face To Path</th>\n      <th>Ball Speed</th>\n      <th>...</th>\n      <th>Last data Point - Height</th>\n      <th>Last data Point - Time</th>\n      <th>Carry Flat - Length</th>\n      <th>Carry Flat - Side</th>\n      <th>Carry Flat - Land. Angle</th>\n      <th>Carry Flat - Ball Speed</th>\n      <th>Carry Flat - Time</th>\n      <th>Total</th>\n      <th>Side Total</th>\n      <th>Curve</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>9521</td>\n      <td>8697</td>\n      <td>8697</td>\n      <td>8697</td>\n      <td>9403</td>\n      <td>9403</td>\n      <td>9079</td>\n      <td>9079</td>\n      <td>8695</td>\n      <td>10165</td>\n      <td>...</td>\n      <td>10169</td>\n      <td>10169</td>\n      <td>10169</td>\n      <td>10169</td>\n      <td>10152</td>\n      <td>10152</td>\n      <td>10169</td>\n      <td>10168</td>\n      <td>10168</td>\n      <td>9231</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>7522</td>\n      <td>4757</td>\n      <td>4987</td>\n      <td>574</td>\n      <td>7514</td>\n      <td>7578</td>\n      <td>7443</td>\n      <td>6950</td>\n      <td>7138</td>\n      <td>10038</td>\n      <td>...</td>\n      <td>10153</td>\n      <td>9616</td>\n      <td>10153</td>\n      <td>10153</td>\n      <td>10152</td>\n      <td>10141</td>\n      <td>10136</td>\n      <td>10152</td>\n      <td>10152</td>\n      <td>9231</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>100.8254295</td>\n      <td>0.1</td>\n      <td>0</td>\n      <td>1.771653543</td>\n      <td>50.102</td>\n      <td>6.562</td>\n      <td>15.355</td>\n      <td>-0.884</td>\n      <td>-3.75</td>\n      <td>127.1315766</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>10.4426753</td>\n      <td>63.91178222</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>15.94575042</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>5</td>\n      <td>60</td>\n      <td>45</td>\n      <td>47</td>\n      <td>6</td>\n      <td>6</td>\n      <td>5</td>\n      <td>7</td>\n      <td>6</td>\n      <td>3</td>\n      <td>...</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>1</td>\n      <td>2</td>\n      <td>17</td>\n      <td>17</td>\n      <td>17</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['Dynamic Lie', 'Impact Offset', 'Impact Height', 'Club'])\n",
    "msno.matrix(df)\n",
    "plt.show()\n",
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### How much data left if we drop all nans\n",
    "The majority of our missing data is related to metrics that characterise the behaviour of the club head at impact. Given\n",
    "that 1) each swing is considered to be independent and 2) these are critical variables to our analyses it may not be\n",
    "appropriate to impute them with the mean/median, so we will first assess the model by removing all missing data.\n",
    "\n",
    "For X, we only want variables related to the moment of impact as the other variables occur as a result of these.\n",
    "Therefore we will only select variables the characterise the club-ball impact."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "(8675, 13)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['Club Speed', 'Attack Angle', 'Club Path', 'Low Point', 'Swing Plane', 'Swing Direction', 'Dyn. Loft',\n",
    "         'Face Angle', 'Face To Path', 'Ball Speed', 'Smash Factor', 'Side Total', 'Total']]\n",
    "df['Total'] = df['Total'].replace(0, np.nan) # anything with a zero should be considered an error\n",
    "df = df.dropna()\n",
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Set data types"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "df = df.apply(pd.to_numeric, errors='ignore')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Histograms of dependant variables"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data=df, x=\"Total\")\n",
    "ax.set_xlabel('Total (yds)')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('Distance')\n",
    "plt.show()\n",
    "\n",
    "ax2 = sns.histplot(data=df, x='Side Total')\n",
    "ax2.set_xlabel('Side Total (yds)')\n",
    "ax2.set_ylabel('Count')\n",
    "ax2.set_title('Accuracy')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Assess outliers\n",
    "\n",
    "There are a number of outliers. We have no reason to think they are due to measurement error. Given they also only account\n",
    "for a small proportion we will leave them for this model iteration."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Club Speed : 0.028 %\n",
      "Attack Angle : 0.001 %\n",
      "Club Path : 0.002 %\n",
      "Low Point : 0.002 %\n",
      "Swing Plane : 0.013 %\n",
      "Swing Direction : 0.002 %\n",
      "Dyn. Loft : 0.023 %\n",
      "Face Angle : 0.012 %\n",
      "Face To Path : 0.012 %\n",
      "Ball Speed : 0.027 %\n",
      "Smash Factor : 0.024 %\n",
      "Side Total : 0.003 %\n",
      "Total : 0.018 %\n"
     ]
    }
   ],
   "source": [
    "def count_outliers(data):\n",
    "    \"\"\"\n",
    "    Calculate the Z scores for each value, and find the number of those that sit outside of 3 standard deviations\n",
    "    :param data: a column of data e.g. a panda series.\n",
    "    :return n_outliers: a count of the number of values greater than 3 SDs\n",
    "    \"\"\"\n",
    "    z_scores = stats.zscore(data)\n",
    "    abs_z_scores = np.abs(z_scores)\n",
    "    n_outliers_bool_array = (abs_z_scores > 3)  # find those values that sit outside of 3 standard deviations\n",
    "    n_outliers = np.count_nonzero(n_outliers_bool_array)\n",
    "    return n_outliers\n",
    "\n",
    "for column in df:\n",
    "    col = df[column]\n",
    "    print(column, ':', round(count_outliers(col)/col.shape[0], 3),'%')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3. Analyse/Model\n",
    "#### Correlation matrix and biplot\n",
    "We will look at the relationship between the variables in 2 ways.\n",
    "1. A correlation plot to see how the variables are interrelated.\n",
    "2. A biplot to see which variables explain similar variance.\n",
    "\n",
    "The correlation and bi plot tells us how closely the variables are related to each other. But using the in conjunction\n",
    "with each other helps us determine what should be entered into the model.\n",
    "\n",
    "For example, we can see that ball speed and club speed are well correlated. But inspection of the bi plot tells us that\n",
    "ball and club speed also are closely related to smash factor (which we would have expected as smash factor is ratio\n",
    "of ball speed to club speed."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = df.corr()\n",
    "sns.set_context('paper', font_scale=1.2)\n",
    "sns.heatmap(corrMatrix)\n",
    "plt.show()\n",
    "\n",
    "X = df[['Club Speed', 'Attack Angle', 'Club Path', 'Low Point', 'Swing Plane', 'Swing Direction', 'Dyn. Loft',\n",
    "         'Face Angle', 'Face To Path', 'Ball Speed', 'Smash Factor']]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)\n",
    "X=scaler.transform(X)\n",
    "pca = PCA()\n",
    "x_new = pca.fit_transform(X)\n",
    "var_labels = ['Club Speed', 'Attack Angle', 'Club Path', 'Low Point', 'Swing Plane', 'Swing Direction', 'Dyn. Loft',\n",
    "         'Face Angle', 'Face To Path', 'Ball Speed', 'Smash Factor']\n",
    "\n",
    "def bi_plot(score, coeff, labels=None):\n",
    "    '''\n",
    "    Plots the PC and the vector loadings for each of the variable in X-train.\n",
    "    :param score: output from pca.fit_transform\n",
    "    :param coeff: model coefficients\n",
    "    :param labels: variable names for plot\n",
    "    :return: a bi plot\n",
    "    '''\n",
    "    xs = score[:,0]\n",
    "    ys = score[:,1]\n",
    "    n = coeff.shape[0]\n",
    "    scalex = 1.0/(xs.max() - xs.min())\n",
    "    scaley = 1.0/(ys.max() - ys.min())\n",
    "    plt.xlim([-.6, .6])\n",
    "    plt.ylim([-.6, .6])\n",
    "\n",
    "    plt.scatter(xs * scalex,ys * scaley, s=1, color= 'gray')\n",
    "    for i in range(n):\n",
    "        plt.arrow(0, 0, coeff[i,0], coeff[i,1], color = 'k', alpha = 0.5)\n",
    "        if labels is None:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, \"Var\"+str(i+1), color = 'green', ha = 'center', va = 'center')\n",
    "        else:\n",
    "            plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, labels[i], color = 'blue', ha = 'center', va = 'center')\n",
    "\n",
    "    plt.xlabel(\"PC{}\".format(1))\n",
    "    plt.ylabel(\"PC{}\".format(2))\n",
    "\n",
    "#Call the function. Use only the 2 PCs.\n",
    "bi_plot(x_new[:,0:2], np.transpose(pca.components_[0:2, :]), var_labels)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Set up functions for models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "def coef_weights(coefficients, X_train):\n",
    "    \"\"\"\n",
    "    Provides a dataframe that can be used to understand the most influential coefficients\n",
    "    in a linear model by providing the coefficient estimates along with the name of the\n",
    "    variable attached to the coefficient.\n",
    "    :param1 coefficients: the coefficients of the linear model\n",
    "    :param2 X_train: the training data, so the column names can be used\n",
    "    :return coefs_df:a dataframe holding the coefficient, estimate, and abs(estimate)\n",
    "    \"\"\"\n",
    "    coefs_df = pd.DataFrame()\n",
    "    coefs_df['est_int'] = X_train.columns\n",
    "    coefs_df['coefs'] = coefficients.coef_\n",
    "    coefs_df['abs_coefs'] = np.abs(coefficients.coef_)\n",
    "    coefs_df = coefs_df.sort_values('abs_coefs', ascending=False)\n",
    "    return coefs_df\n",
    "\n",
    "\n",
    "def linear_regression(dependant_var_name) -> tuple:\n",
    "    \"\"\"\n",
    "    Runs regression model and output the r2 score and the model coefficients\n",
    "    :param dependant_var_name: The dependant variable that you are looking to predict\n",
    "    :return r2 and model coefficients:\n",
    "    \"\"\"\n",
    "    X = df[['Club Speed', 'Attack Angle', 'Club Path', 'Low Point', 'Swing Plane', 'Swing Direction', 'Dyn. Loft', 'Face Angle', 'Face To Path', 'Ball Speed', 'Smash Factor']]\n",
    "    y = df[dependant_var_name]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state=42)\n",
    "    lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "    lm_model.fit(X_train, y_train) #Fit\n",
    "\n",
    "    #Predict and score the model\n",
    "    y_test_preds = lm_model.predict(X_test)\n",
    "    coef = coef_weights(lm_model, X_train)\n",
    "    return \"R-squared score: {} on {} values.\".format(round(r2_score(y_test, y_test_preds),2), len(y_test)), \\\n",
    "           coef.head(20)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Run model 1 - What variables are predictive of total distance"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "'R-squared score: 0.86 on 2603 values.'"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_regression = linear_regression('Total')\n",
    "distance_regression[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Run model 2 - What variables are predictive of total accuracy"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "'R-squared score: 0.63 on 2603 values.'"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_regression = linear_regression('Side Total')\n",
    "accuracy_regression[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Create hybrid metric of both distance and accuracy\n",
    "We want to create a hybrid metric for both distance and accuracy. We will take the first PC of the two measures."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df[['Total', 'Side Total']]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(y)\n",
    "Y=scaler.transform(y)\n",
    "pca = PCA(n_components=1)\n",
    "y_new = pca.fit_transform(Y)\n",
    "\n",
    "df['length_accuracy']  = pd.DataFrame(y_new, index=df.index) # add back into dataframe with original index"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Run model 3 - What variables are predictive of total accuracy"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "length_accuracy_regression = linear_regression('length_accuracy')\n",
    "length_accuracy_regression[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3. Visualise\n",
    "#### Which variables are the most important to focus on for distance, accuracy and a distance/accuracy trade off\n",
    "\n",
    "#### Maximising distance\n",
    "1. Model performance is good. Most important variable is smash factor. 8 * more important than the next variable, followed\n",
    "by Club Path, Face Angle and Face to Path.\n",
    "\n",
    "#### Maximising accuracy\n",
    "2. Model performance is OK. Most important variable is smash factor. 4 * more important than the next variable, followed\n",
    "by Face to Path, Club Path and Face Angle.\n",
    "\n",
    "#### Maximising distance/accuracy\n",
    "3. Model performance is good. Most important variable is smash factor. 6 * more important than the next variable, followed\n",
    "by Face to Path, Club Path and Face Angle."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "ax = sns.barplot(x=\"abs_coefs\", y=\"est_int\", data=distance_regression[1])\n",
    "ax.set_xlabel('Absolute model coefficient')\n",
    "ax.set_ylabel('')\n",
    "ax.set_title('Distance')\n",
    "ax.text(28, 10, distance_regression[0])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "ax1 = sns.barplot(x=\"abs_coefs\", y=\"est_int\", data=accuracy_regression[1])\n",
    "ax1.set_xlabel('Absolute model coefficient')\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_title('Accuracy')\n",
    "ax1.text(15, 10, accuracy_regression[0])\n",
    "plt.show()\n",
    "\n",
    "ax2 = sns.barplot(x=\"abs_coefs\", y=\"est_int\", data=length_accuracy_regression[1])\n",
    "ax2.set_xlabel('Absolute model coefficient')\n",
    "ax2.set_ylabel('')\n",
    "ax2.set_title('Distance/accuracy')\n",
    "ax2.text(0.65, 10, length_accuracy_regression[0])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
 "nbformat": 4,
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