model_kobe_shots_made.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "\n",
    "sns.set(rc={\"figure.figsize\": (12, 12)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kobe = pd.read_csv('/Users/Stav/github-repos/3-TA-DSI/datasets/kobe_bryant/kobe_superwide_games.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
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       "   SHOTS_MADE  AWAY_GAME  SEASON_OPPONENT:atl:1996-97  \\\n",
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       "\n",
       "[5 rows x 645 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_mat = kobe.SHOTS_MADE\n",
    "del kobe['SHOTS_MADE']\n",
    "x_mat = kobe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "   AWAY_GAME  SEASON_OPPONENT:atl:1996-97  SEASON_OPPONENT:atl:1997-98  \\\n",
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       "3                                      0.0                               0.0   \n",
       "4                                      0.0                               0.0   \n",
       "\n",
       "   ACTION_TYPE:turnaround_jump_shot  SEASON_GAME_NUMBER  CAREER_GAME_NUMBER  \n",
       "0                               0.0                   1                   1  \n",
       "1                               0.0                   2                   2  \n",
       "2                               0.0                   3                   3  \n",
       "3                               0.0                   4                   4  \n",
       "4                               0.0                   5                   5  \n",
       "\n",
       "[5 rows x 644 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_mat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normalize our dataset\n",
    "x_mat = (x_mat - x_mat.mean()) / x_mat.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AWAY_GAME</th>\n",
       "      <th>SEASON_OPPONENT:atl:1996-97</th>\n",
       "      <th>SEASON_OPPONENT:atl:1997-98</th>\n",
       "      <th>SEASON_OPPONENT:atl:1999-00</th>\n",
       "      <th>SEASON_OPPONENT:atl:2000-01</th>\n",
       "      <th>SEASON_OPPONENT:atl:2001-02</th>\n",
       "      <th>SEASON_OPPONENT:atl:2002-03</th>\n",
       "      <th>SEASON_OPPONENT:atl:2003-04</th>\n",
       "      <th>SEASON_OPPONENT:atl:2004-05</th>\n",
       "      <th>SEASON_OPPONENT:atl:2005-06</th>\n",
       "      <th>...</th>\n",
       "      <th>ACTION_TYPE:tip_layup_shot</th>\n",
       "      <th>ACTION_TYPE:tip_shot</th>\n",
       "      <th>ACTION_TYPE:turnaround_bank_shot</th>\n",
       "      <th>ACTION_TYPE:turnaround_fadeaway_bank_jump_shot</th>\n",
       "      <th>ACTION_TYPE:turnaround_fadeaway_shot</th>\n",
       "      <th>ACTION_TYPE:turnaround_finger_roll_shot</th>\n",
       "      <th>ACTION_TYPE:turnaround_hook_shot</th>\n",
       "      <th>ACTION_TYPE:turnaround_jump_shot</th>\n",
       "      <th>SEASON_GAME_NUMBER</th>\n",
       "      <th>CAREER_GAME_NUMBER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.000963</td>\n",
       "      <td>-0.035840</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.281716</td>\n",
       "      <td>-0.183863</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.342481</td>\n",
       "      <td>-0.035735</td>\n",
       "      <td>-0.0884</td>\n",
       "      <td>-0.643012</td>\n",
       "      <td>-1.610350</td>\n",
       "      <td>-1.732488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.998397</td>\n",
       "      <td>-0.035840</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.281716</td>\n",
       "      <td>-0.183863</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.342481</td>\n",
       "      <td>-0.035735</td>\n",
       "      <td>-0.0884</td>\n",
       "      <td>-0.643012</td>\n",
       "      <td>-1.571960</td>\n",
       "      <td>-1.730265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.998397</td>\n",
       "      <td>-0.035840</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.281716</td>\n",
       "      <td>-0.183863</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.342481</td>\n",
       "      <td>-0.035735</td>\n",
       "      <td>-0.0884</td>\n",
       "      <td>-0.643012</td>\n",
       "      <td>-1.533569</td>\n",
       "      <td>-1.728043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.998397</td>\n",
       "      <td>-0.035840</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.281716</td>\n",
       "      <td>-0.183863</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.342481</td>\n",
       "      <td>-0.035735</td>\n",
       "      <td>-0.0884</td>\n",
       "      <td>-0.643012</td>\n",
       "      <td>-1.495179</td>\n",
       "      <td>-1.725820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.000963</td>\n",
       "      <td>27.883698</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03584</td>\n",
       "      <td>-0.281716</td>\n",
       "      <td>-0.183863</td>\n",
       "      <td>-0.025335</td>\n",
       "      <td>-0.342481</td>\n",
       "      <td>-0.035735</td>\n",
       "      <td>-0.0884</td>\n",
       "      <td>-0.643012</td>\n",
       "      <td>-1.456788</td>\n",
       "      <td>-1.723597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 644 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AWAY_GAME  SEASON_OPPONENT:atl:1996-97  SEASON_OPPONENT:atl:1997-98  \\\n",
       "0  -1.000963                    -0.035840                     -0.03584   \n",
       "1   0.998397                    -0.035840                     -0.03584   \n",
       "2   0.998397                    -0.035840                     -0.03584   \n",
       "3   0.998397                    -0.035840                     -0.03584   \n",
       "4  -1.000963                    27.883698                     -0.03584   \n",
       "\n",
       "   SEASON_OPPONENT:atl:1999-00  SEASON_OPPONENT:atl:2000-01  \\\n",
       "0                    -0.025335                    -0.025335   \n",
       "1                    -0.025335                    -0.025335   \n",
       "2                    -0.025335                    -0.025335   \n",
       "3                    -0.025335                    -0.025335   \n",
       "4                    -0.025335                    -0.025335   \n",
       "\n",
       "   SEASON_OPPONENT:atl:2001-02  SEASON_OPPONENT:atl:2002-03  \\\n",
       "0                     -0.03584                     -0.03584   \n",
       "1                     -0.03584                     -0.03584   \n",
       "2                     -0.03584                     -0.03584   \n",
       "3                     -0.03584                     -0.03584   \n",
       "4                     -0.03584                     -0.03584   \n",
       "\n",
       "   SEASON_OPPONENT:atl:2003-04  SEASON_OPPONENT:atl:2004-05  \\\n",
       "0                    -0.025335                    -0.025335   \n",
       "1                    -0.025335                    -0.025335   \n",
       "2                    -0.025335                    -0.025335   \n",
       "3                    -0.025335                    -0.025335   \n",
       "4                    -0.025335                    -0.025335   \n",
       "\n",
       "   SEASON_OPPONENT:atl:2005-06         ...          \\\n",
       "0                     -0.03584         ...           \n",
       "1                     -0.03584         ...           \n",
       "2                     -0.03584         ...           \n",
       "3                     -0.03584         ...           \n",
       "4                     -0.03584         ...           \n",
       "\n",
       "   ACTION_TYPE:tip_layup_shot  ACTION_TYPE:tip_shot  \\\n",
       "0                    -0.03584             -0.281716   \n",
       "1                    -0.03584             -0.281716   \n",
       "2                    -0.03584             -0.281716   \n",
       "3                    -0.03584             -0.281716   \n",
       "4                    -0.03584             -0.281716   \n",
       "\n",
       "   ACTION_TYPE:turnaround_bank_shot  \\\n",
       "0                         -0.183863   \n",
       "1                         -0.183863   \n",
       "2                         -0.183863   \n",
       "3                         -0.183863   \n",
       "4                         -0.183863   \n",
       "\n",
       "   ACTION_TYPE:turnaround_fadeaway_bank_jump_shot  \\\n",
       "0                                       -0.025335   \n",
       "1                                       -0.025335   \n",
       "2                                       -0.025335   \n",
       "3                                       -0.025335   \n",
       "4                                       -0.025335   \n",
       "\n",
       "   ACTION_TYPE:turnaround_fadeaway_shot  \\\n",
       "0                             -0.342481   \n",
       "1                             -0.342481   \n",
       "2                             -0.342481   \n",
       "3                             -0.342481   \n",
       "4                             -0.342481   \n",
       "\n",
       "   ACTION_TYPE:turnaround_finger_roll_shot  ACTION_TYPE:turnaround_hook_shot  \\\n",
       "0                                -0.035735                           -0.0884   \n",
       "1                                -0.035735                           -0.0884   \n",
       "2                                -0.035735                           -0.0884   \n",
       "3                                -0.035735                           -0.0884   \n",
       "4                                -0.035735                           -0.0884   \n",
       "\n",
       "   ACTION_TYPE:turnaround_jump_shot  SEASON_GAME_NUMBER  CAREER_GAME_NUMBER  \n",
       "0                         -0.643012           -1.610350           -1.732488  \n",
       "1                         -0.643012           -1.571960           -1.730265  \n",
       "2                         -0.643012           -1.533569           -1.728043  \n",
       "3                         -0.643012           -1.495179           -1.725820  \n",
       "4                         -0.643012           -1.456788           -1.723597  \n",
       "\n",
       "[5 rows x 644 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_mat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try vanilla linear regression: \n",
    "- this will perform terribly.. we have 600 predictors and too many coefficients\n",
    "- alphas are ridiculous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
      "-8.01239122685e+27\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_mat, y_mat, test_size=0.33, random_state=0)\n",
    "\n",
    "\n",
    "lr = LinearRegression()\n",
    "\n",
    "print lr.fit(X_train, y_train)\n",
    "print lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[91897771874798.578,\n",
       " 52667516635666.336,\n",
       " 50859350897159.453,\n",
       " 44137069186597.914,\n",
       " 40323622559244.242,\n",
       " 37096373490116.711,\n",
       " 32623938855471.203,\n",
       " 31559159620469.625,\n",
       " 30466792215872.34,\n",
       " 28158610171804.637]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(lr.coef_, reverse = True)[0:10] #alphas have too high of a magnitude we should normalize our coefficients w/ regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regularization Lasso:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Stav/anaconda2/envs/dsi/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/Stav/anaconda2/envs/dsi/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/Stav/anaconda2/envs/dsi/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "/Users/Stav/anaconda2/envs/dsi/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.1}\n",
      "0.65302919607\n"
     ]
    }
   ],
   "source": [
    "# Lasso\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import Lasso, LinearRegression, Ridge, ElasticNet\n",
    "\n",
    "\n",
    "model = Lasso()\n",
    "\n",
    "params = {'alpha': [.1,.01,.001,.0001]}\n",
    "grid_search = GridSearchCV(estimator = model, param_grid = params, cv = 5, n_jobs = -1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)              \n",
    "\n",
    "\n",
    "print grid_search.best_params_\n",
    "\n",
    "print grid_search.score(X_test, y_test)\n",
    "\n",
    "lasso_model = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1949512391368722,\n",
       " 1.0728603409316082,\n",
       " 0.3777209424047861,\n",
       " 0.28895477392363489,\n",
       " 0.25029132413434663,\n",
       " 0.11222642388759059,\n",
       " 0.11041138868743934,\n",
       " 0.095270469162873572,\n",
       " 0.088734793869166273,\n",
       " 0.081172025195324196,\n",
       " 0.079787627797918828,\n",
       " 0.064776612204095099,\n",
       " 0.06458654797032444,\n",
       " 0.064548713001307712,\n",
       " 0.063297499461582918,\n",
       " 0.062317828052773569,\n",
       " 0.059053135247324681,\n",
       " 0.058449206946308591,\n",
       " 0.057616023098817318,\n",
       " 0.055006086767135969,\n",
       " 0.052411824313284644,\n",
       " 0.050726383273628911,\n",
       " 0.046155697872063209,\n",
       " 0.041039115214133566,\n",
       " 0.040870991920356245,\n",
       " 0.040449696815756234,\n",
       " 0.036919877200056149,\n",
       " 0.035903917101158561,\n",
       " 0.033766250002639442,\n",
       " 0.032539313062569734,\n",
       " 0.031930242750595167,\n",
       " 0.02762435858909924,\n",
       " 0.026616497305208792,\n",
       " 0.025187403157833649,\n",
       " 0.024080837617922846,\n",
       " 0.023571232470169089,\n",
       " 0.023423329455291242,\n",
       " 0.022861827073584765,\n",
       " 0.022371979097217463,\n",
       " 0.022208410227824903,\n",
       " 0.021102588032858426,\n",
       " 0.019575816651561516,\n",
       " 0.019429506408152539,\n",
       " 0.018824495437586058,\n",
       " 0.017257875575956035,\n",
       " 0.015858261951314911,\n",
       " 0.014459326209199028,\n",
       " 0.013336585493947929,\n",
       " 0.012613900986404707,\n",
       " 0.012589486408299107,\n",
       " 0.011947897988910904,\n",
       " 0.011403554950555917,\n",
       " 0.0074706515635068844,\n",
       " 0.0072273560424939891,\n",
       " 0.0057800967332558625,\n",
       " 0.0035807547909490179,\n",
       " 0.0021786220810249548,\n",
       " -0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.0,\n",
       " 0.0,\n",
       " -0.0]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(grid_search.best_estimator_.coef_, reverse = True)[0:100]  # alphas from lasso, better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge Regularization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 10}\n",
      "0.443766762025\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Ridge, ElasticNet\n",
    "\n",
    "\n",
    "model = Ridge()\n",
    "\n",
    "params = {'alpha': [.1,.01,.001,.0001,1,2,3,4,5,6,7,8,9,10]}\n",
    "grid_search = GridSearchCV(estimator = model, param_grid = params, cv = 5, n_jobs = -1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)              \n",
    "\n",
    "\n",
    "print grid_search.best_params_\n",
    "\n",
    "print grid_search.score(X_test, y_test)\n",
    "\n",
    "ridge_model = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.66907346721165883,\n",
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       " 0.41821687536876556,\n",
       " 0.38841811324050601,\n",
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       " 0.33867442670787945,\n",
       " 0.30310353211218888,\n",
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       " 0.26659586809187719,\n",
       " 0.22024227095842625,\n",
       " 0.20756427725892299,\n",
       " 0.20162735612710167,\n",
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       " 0.18612255538027264,\n",
       " 0.18293019985665016,\n",
       " 0.18238309081336018,\n",
       " 0.17814493271893547,\n",
       " 0.17495241157191488,\n",
       " 0.17484514139131707,\n",
       " 0.17456519457310168,\n",
       " 0.17193076487456938,\n",
       " 0.17168156900331624,\n",
       " 0.16626535816850574,\n",
       " 0.16613007475352332,\n",
       " 0.16525697467883432,\n",
       " 0.16501834479241634,\n",
       " 0.16486380267254827,\n",
       " 0.1644382973556614,\n",
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       " 0.15842469454830915,\n",
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       " 0.14573855773102432,\n",
       " 0.14565218600257029,\n",
       " 0.14358750649005306,\n",
       " 0.14280135822418669,\n",
       " 0.13531105076599032,\n",
       " 0.1342294453335437,\n",
       " 0.13399661332725127,\n",
       " 0.13210287824885805,\n",
       " 0.13199977841037577,\n",
       " 0.13089656684852544,\n",
       " 0.12820405071857413,\n",
       " 0.12721945088003059,\n",
       " 0.1265461006458746,\n",
       " 0.12391247321560973,\n",
       " 0.12213000156234047,\n",
       " 0.12071726633270664,\n",
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       " 0.11142370620553808,\n",
       " 0.11127011888995363,\n",
       " 0.11091228707248536,\n",
       " 0.11086088610384698,\n",
       " 0.10879638463697648,\n",
       " 0.10534195753979228,\n",
       " 0.10456238872371587,\n",
       " 0.1025378513488208,\n",
       " 0.10185148720829305,\n",
       " 0.10170837019326776,\n",
       " 0.10098357868558902,\n",
       " 0.10013050268199934,\n",
       " 0.099568266189307503,\n",
       " 0.099253803989722944,\n",
       " 0.097003737898864503,\n",
       " 0.094687486155135572,\n",
       " 0.093671629935430087,\n",
       " 0.093285551365870245,\n",
       " 0.09249747694700107,\n",
       " 0.091647785086189301,\n",
       " 0.090800992538167771,\n",
       " 0.090474296427131937,\n",
       " 0.088923955571694649,\n",
       " 0.088873126830615934,\n",
       " 0.088752091938311475,\n",
       " 0.088561051948392241,\n",
       " 0.087802376957771197,\n",
       " 0.087607599018399193,\n",
       " 0.086570361429761322,\n",
       " 0.086299653677224461,\n",
       " 0.084726692316949917,\n",
       " 0.083295655966112853,\n",
       " 0.080335869085672681,\n",
       " 0.078247579429709044,\n",
       " 0.078036548394448166,\n",
       " 0.077139648312723033,\n",
       " 0.076681899454921224,\n",
       " 0.073248263916095832,\n",
       " 0.073065208618894137,\n",
       " 0.069490525272153644,\n",
       " 0.069072160912284922,\n",
       " 0.068132844771704601,\n",
       " 0.067836142608215952,\n",
       " 0.06699366659041639]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(grid_search.best_estimator_.coef_, reverse = True)[0:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso gave the best regularization, r^2 = .65 compared to lasso = .44 and elastic_net = .637"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'l1_ratio': 0.10000000000000001}\n",
      "0.637317291484\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import Lasso, LinearRegression, Ridge, ElasticNet\n",
    "\n",
    "\n",
    "model = ElasticNet()\n",
    "\n",
    "params = {'l1_ratio': np.arange(.1,1,.1)}\n",
    "grid_search = GridSearchCV(estimator = model, param_grid = params, cv = 5, n_jobs = -1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)              \n",
    "\n",
    "\n",
    "print grid_search.best_params_\n",
    "\n",
    "print grid_search.score(X_test, y_test)\n",
    "\n",
    "elastic_model = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ridge_model.fit(X_train, y_train)\n",
    "lasso_model.fit(X_train, y_train)\n",
    "elastic_model.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "ridge_residuals = y_test - ridge_model.predict(X_test)\n",
    "lasso_residuals = y_test - lasso_model.predict(X_test)\n",
    "elastic_residuals =  y_test - elastic_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.,   3.,   3.,   4.,   5.,   4.,   4.,  10.,  11.,  21.,  14.,\n",
       "         12.,  14.,  20.,  20.,  20.,  21.,  19.,  30.,  19.,  31.,  21.,\n",
       "         25.,  20.,  17.,  14.,  10.,   8.,  17.,  16.,  14.,  11.,   7.,\n",
       "          8.,   5.,   7.,   4.,   3.,   5.,   5.,   1.,   2.,   0.,   2.,\n",
       "          0.,   2.,   1.,   0.,   2.,   1.]),\n",
       " array([-5.02318177, -4.77461846, -4.52605515, -4.27749184, -4.02892853,\n",
       "        -3.78036523, -3.53180192, -3.28323861, -3.0346753 , -2.78611199,\n",
       "        -2.53754868, -2.28898537, -2.04042206, -1.79185875, -1.54329544,\n",
       "        -1.29473213, -1.04616883, -0.79760552, -0.54904221, -0.3004789 ,\n",
       "        -0.05191559,  0.19664772,  0.44521103,  0.69377434,  0.94233765,\n",
       "         1.19090096,  1.43946426,  1.68802757,  1.93659088,  2.18515419,\n",
       "         2.4337175 ,  2.68228081,  2.93084412,  3.17940743,  3.42797074,\n",
       "         3.67653405,  3.92509736,  4.17366066,  4.42222397,  4.67078728,\n",
       "         4.91935059,  5.1679139 ,  5.41647721,  5.66504052,  5.91360383,\n",
       "         6.16216714,  6.41073045,  6.65929376,  6.90785706,  7.15642037,\n",
       "         7.40498368]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ef67fd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 979,
       "width": 982
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "sns.set(rc={\"figure.figsize\": (12, 12)})\n",
    "\n",
    "\n",
    "plt.hist(ridge_residuals, alpha = .8, bins = 50, color = 'red',histtype = 'step')\n",
    "plt.hist(lasso_residuals, alpha = .8, bins = 50, color = 'blue',histtype = 'step')\n",
    "plt.hist(elastic_residuals, alpha = .8, bins = 50, color = 'green', histtype = 'step')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}