6_regression_model_luther.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Lasso, LassoCV, Ridge, RidgeCV, ElasticNet, ElasticNetCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.style as style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('result_data.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(509, 74)"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Birthday', 'Total_Height_in', 'Weight', 'Yrs_Played_College',\n",
       "       'GP_College', 'MPG_College', 'FGM_College', 'FGA_College',\n",
       "       'FG_Perc_College', '3PM_College', '3PA_College', '3P_Perc_College',\n",
       "       'FTM_College', 'FTA_College', 'FT_Perc_College', 'TOV_College',\n",
       "       'PF_College', 'ORB_College', 'DRB_College', 'RPG_College',\n",
       "       'APG_College', 'SPG_College', 'BPG_College', 'PPG_College', 'College',\n",
       "       'Total_BPM_NBA', 'Rookie_Age', 'Position', 'GP_Rookie',\n",
       "       'MP_Total_Rookie', 'PER', 'TS%', '3PAr', 'FTr', 'ORB%', 'DRB%', 'TRB%',\n",
       "       'AST%', 'STL%', 'BLK%', 'TOV%', 'USG%', 'OWS', 'DWS', 'WS_Rookie',\n",
       "       'WS_per_48_Rookie', 'OBPM', 'DBPM', 'BPM_Rookie', 'VORP_Rookie',\n",
       "       'College_above_5', 'College_adj', 'Bad',\n",
       "       'Georgia Institute of Technology', 'Indiana University',\n",
       "       'Stanford University', 'University of Florida',\n",
       "       'University of Illinois at Urbana-Champaign', 'University of Kansas',\n",
       "       'University of Memphis', 'University of North Carolina',\n",
       "       'University of Pittsburgh', 'University of Washington', 'Yr_Tm',\n",
       "       'BPM_top3', 'BPM_top5', 'BPM_top7', 'BPM_top13', 'top3/top5',\n",
       "       'top3/top7', 'top3/top13', 'top5/top13', 'top7/top13'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.loc[:,[#'Total_Height_in',\n",
    " #'Weight',\n",
    " 'Yrs_Played_College',\n",
    " 'FG_Perc_College',\n",
    " 'FT_Perc_College',\n",
    " 'RPG_College',\n",
    " 'APG_College',\n",
    " 'SPG_College',\n",
    " 'BPG_College',\n",
    " 'PPG_College',\n",
    " 'Rookie_Age',\n",
    " 'Georgia Institute of Technology',\n",
    " 'Indiana University',\n",
    " 'Stanford University',\n",
    " 'University of Florida',\n",
    " 'University of Illinois at Urbana-Champaign',\n",
    " 'University of Kansas',\n",
    " 'University of Memphis',\n",
    " 'University of North Carolina',\n",
    " 'University of Pittsburgh',\n",
    " 'University of Washington',\n",
    " 'BPM_top3',\n",
    " 'BPM_top5',\n",
    " 'BPM_top7',\n",
    " 'BPM_top13',\n",
    " 'top3/top5',\n",
    " 'top3/top7',]] \n",
    " #'top3/top13',]]\n",
    " #'top5/top13',]]\n",
    " #'top7/top13']]\n",
    "\n",
    "y = df['BPM_Rookie']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = StandardScaler()\n",
    "std.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_tr = std.transform(X_train)\n",
    "X_te = std.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1377b1198>"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gQ23SvotUH5kpGW5VSrI5b3Oy5Nd1iDW9KYvtHRM/w5KCq3ocHj/lzqmKVaDA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\nYbFYJSIiIiKH9f8S5IVexdiZ2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13746e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(10,4))\n",
    "\n",
    "ax[0].scatter(X_train['Rookie_Age'],y_train, alpha = .5)\n",
    "ax[0].set_xlabel('Rookie Age')\n",
    "ax[0].set_ylabel('BPM Rookie Yr')\n",
    "\n",
    "ax[1].scatter(X_train['top3/top5'],y_train, alpha = .5)\n",
    "ax[1].set_xlabel('BPM Top3/Top5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model1 = LinearRegression()\n",
    "lr_model1.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Yrs_Played_College', 0.56853081692613083),\n",
       " ('FG_Perc_College', 0.61467078480562631),\n",
       " ('FT_Perc_College', 0.30723760810495848),\n",
       " ('RPG_College', 0.69279061915306861),\n",
       " ('APG_College', 0.48806377556909841),\n",
       " ('SPG_College', 1.087618177500735),\n",
       " ('BPG_College', 0.4037017279166728),\n",
       " ('PPG_College', -0.61525314813702081),\n",
       " ('Rookie_Age', -0.51573662045809343),\n",
       " ('Georgia Institute of Technology', 0.10165584427104399),\n",
       " ('Indiana University', 0.060428232684091032),\n",
       " ('Stanford University', 0.27717296512669987),\n",
       " ('University of Florida', 0.19737941981503213),\n",
       " ('University of Illinois at Urbana-Champaign', 0.14587458827714428),\n",
       " ('University of Kansas', 0.10381907834086057),\n",
       " ('University of Memphis', 0.041938602788586077),\n",
       " ('University of North Carolina', 0.16915224418039942),\n",
       " ('University of Pittsburgh', 0.20198887413093997),\n",
       " ('University of Washington', 0.18630888609075341),\n",
       " ('BPM_top3', -0.47665887641854443),\n",
       " ('BPM_top5', 1.5251061235319201),\n",
       " ('BPM_top7', -2.539553736589319),\n",
       " ('BPM_top13', 1.451468695046185),\n",
       " ('top3/top5', 0.1170555047965082),\n",
       " ('top3/top7', 0.046950380037026396)]"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model1.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred1 = lr_model1.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1379bb630>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VtbeDsk6nw9mzZ2E2myEQCBATE0O0bf7+/khOTmadPzIygqKiIjJ5i0QipKWlsTI8+Pv7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13782fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "style.use('fivethirtyeight')\n",
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "plt.scatter(lr_test_pred1,y_test,alpha=0.5,)\n",
    "plt.plot(np.linspace(-8,2,1000),np.linspace(-8,2,1000))\n",
    "plt.xlabel('Predicted BPM')\n",
    "plt.ylabel('Actual BPM')\n",
    "\n",
    "\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 18)\n",
    "plt.axvline(x = -8, color = 'black', linewidth = 1.3, alpha = .7)\n",
    "plt.axhline(y = -20, color = 'black', linewidth = 1.3, alpha = .7)\n",
    "\n",
    "plt.text(x = -8.8, y = 13, s = \"Predicted Rookie Year BPM vs Actual\",\n",
    "               fontsize = 34, weight = 'bold', alpha = .75,color = '#f0f0f0', backgroundcolor = 'grey')\n",
    "plt.text(x = -8.8, y = 8, s = \"Each point represents a drafted rookie since 2003 who played at\\nleast one game in the NBA.\",\n",
    "               fontsize = 19, weight = 'bold', alpha = .75)\n",
    "\n",
    "plt.text(x = -6.6, y = -17, s = 'Julius Randle', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')\n",
    "plt.text(x = -2, y = 5, s = 'James Augustine', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')\n",
    "plt.text(x = -2.5, y = -19, s = 'Multiple Linear Regression    r^2 = 0.168', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinReg MAD:  2.4485198412285922\n",
      "LinReg RMSE:  3.41450607665\n",
      "LinReg MSE:  2.44851984123\n",
      "LinReg r2:  0.168336511944\n"
     ]
    }
   ],
   "source": [
    "print('LinReg MAD: ',np.mean(np.abs(lr_test_pred1 - y_test)))\n",
    "print('LinReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred1)))\n",
    "print('LinReg MSE: ', mean_absolute_error(y_test,lr_test_pred1))\n",
    "print('LinReg r2: ',r2_score(y_test, lr_test_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression on Entire Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_modelX = LinearRegression()\n",
    "lr_modelX.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_predX = lr_modelX.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1367293c8>"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YJBJh1apVHJ/EuLg4TkQ0MBYp7UhnZydHIFAqlVi9ejXrmzObFDinqrPb7bh+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\nIiIiducfxAOpKzVngucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1365f09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "style.use('fivethirtyeight')\n",
    "plt.figure(figsize = (10,6))\n",
    "\n",
    "plt.scatter(lr_test_predX,y,alpha=0.5,)\n",
    "plt.plot(np.linspace(-8,3,1000),np.linspace(-8,3,1000))\n",
    "plt.xlabel('Predicted BPM')\n",
    "plt.ylabel('Actual BPM')\n",
    "\n",
    "\n",
    "plt.tick_params(axis = 'both', which = 'major', labelsize = 18)\n",
    "plt.axvline(x = -8, color = 'black', linewidth = 1.3, alpha = .7)\n",
    "\n",
    "plt.text(x = -8.8, y = 15, s = \"Predicted Rookie Year BPM vs Actual\",\n",
    "               fontsize = 34, weight = 'bold', alpha = .75,color = '#f0f0f0', backgroundcolor = 'grey')\n",
    "plt.text(x = -8.8, y = 9, s = \"Each point represents a drafted rookie since 2003 who played at\\nleast one game in the NBA.\",\n",
    "               fontsize = 19, weight = 'bold', alpha = .75)\n",
    "\n",
    "plt.text(x = -6.1, y = -27, s = 'Ben Bentil', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')\n",
    "plt.text(x = -7.3, y = 5.9, s = 'Demetrius Jackson', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')\n",
    "plt.text(x = -1.2, y = 5.9, s = 'Chris Paul', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')\n",
    "plt.text(x = -2.5, y = -26, s = 'Multiple Linear Regression    r^2 = 0.191', weight = 'bold', rotation = 0,\n",
    "              backgroundcolor = '#f0f0f0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinReg MAD:  2.3414902048937374\n",
      "LinReg RMSE:  3.40331340208\n",
      "LinReg MSE:  2.34149020489\n",
      "LinReg r2:  0.19146405421\n"
     ]
    }
   ],
   "source": [
    "print('LinReg MAD: ',np.mean(np.abs(lr_test_predX - y)))\n",
    "print('LinReg RMSE: ',np.sqrt(mean_squared_error(y, lr_test_predX)))\n",
    "print('LinReg MSE: ', mean_absolute_error(y,lr_test_predX))\n",
    "print('LinReg r2: ',r2_score(y, lr_test_predX))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LassoCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alphavec = 10**np.linspace(-3,9,27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha:  0.0242446201708\n",
      "cv:  5\n"
     ]
    }
   ],
   "source": [
    "lr_model2 = LassoCV(alphas = alphavec, cv=5)\n",
    "lr_model2.fit(X_tr,y_train)\n",
    "\n",
    "print('alpha: ',lr_model2.alpha_)\n",
    "print('cv: ',lr_model2.cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model3 = Lasso(alpha=0.01)\n",
    "lr_model3.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Yrs_Played_College', 0.53971797382086717),\n",
       " ('FG_Perc_College', 0.5953771644939061),\n",
       " ('FT_Perc_College', 0.28081306853585181),\n",
       " ('RPG_College', 0.68416474829219742),\n",
       " ('APG_College', 0.47742840207283227),\n",
       " ('SPG_College', 1.0616815734583647),\n",
       " ('BPG_College', 0.37826184860164075),\n",
       " ('PPG_College', -0.5918458755467344),\n",
       " ('Rookie_Age', -0.49113670895159572),\n",
       " ('Georgia Institute of Technology', 0.096731419723166498),\n",
       " ('Indiana University', 0.050618853113416261),\n",
       " ('Stanford University', 0.26433287784716647),\n",
       " ('University of Florida', 0.18305564414788161),\n",
       " ('University of Illinois at Urbana-Champaign', 0.14126062482307783),\n",
       " ('University of Kansas', 0.10156045117885974),\n",
       " ('University of Memphis', 0.024533270106040742),\n",
       " ('University of North Carolina', 0.15732207700580647),\n",
       " ('University of Pittsburgh', 0.18974891239413363),\n",
       " ('University of Washington', 0.16621747951444177),\n",
       " ('BPM_top3', -0.0),\n",
       " ('BPM_top5', -0.0),\n",
       " ('BPM_top7', -1.242738312821787),\n",
       " ('BPM_top13', 1.1903419353146636),\n",
       " ('top3/top5', 0.11037131820936431),\n",
       " ('top3/top7', 0.02750630351458664)]"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model3.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred2 = lr_model3.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x133b9fe48>]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X+uJZbYld6EVeGTv8ZioVOl3QWpVIuT4bNGx/a3d6z003BX50IoiPxg0oAOrdUtZrp5sR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133b9fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "\n",
    "plt.scatter(lr_test_pred2,y_test,alpha=0.5)\n",
    "plt.plot(np.linspace(-8,2,1000),np.linspace(-8,2,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LassoReg MAD:  2.448321875853369\n",
      "LassoReg RMSE:  3.42145454119\n",
      "LassoReg MSE:  2.44832187585\n",
      "LassoReg r2:  0.164948224448\n"
     ]
    }
   ],
   "source": [
    "print('LassoReg MAD: ',np.mean(np.abs(lr_test_pred2 - y_test)))\n",
    "print('LassoReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred2)))\n",
    "print('LassoReg MSE: ', mean_absolute_error(y_test,lr_test_pred2))\n",
    "print('LassoReg r2: ',r2_score(y_test, lr_test_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha:  41.246263829\n",
      "cv:  5\n"
     ]
    }
   ],
   "source": [
    "lr_model3 = RidgeCV(alphas = alphavec, cv=5)\n",
    "lr_model3.fit(X_tr,y_train)\n",
    "\n",
    "print('alpha: ',lr_model3.alpha_)\n",
    "print('cv: ',lr_model3.cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=10, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model4 = Ridge(alpha=10)#lr_model3.alpha_)\n",
    "lr_model4.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.53055420324408797),\n",
       " ('Weight', 0.5766023507247553),\n",
       " ('Yrs_Played_College', 0.27100667386679689),\n",
       " ('FG_Perc_College', 0.67035803850470665),\n",
       " ('FT_Perc_College', 0.48212996378133782),\n",
       " ('RPG_College', 1.0400033944472988),\n",
       " ('APG_College', 0.38806647369087627),\n",
       " ('SPG_College', -0.57511323260562486),\n",
       " ('BPG_College', -0.48936260772288809),\n",
       " ('PPG_College', 0.10170544713719594),\n",
       " ('Rookie_Age', 0.061800177422067289),\n",
       " ('College_BPM', 0.27301325869183707),\n",
       " ('BPM_top3', 0.19066471584309275),\n",
       " ('BPM_top5', 0.15246533618658387),\n",
       " ('BPM_top7', 0.1143757810291678),\n",
       " ('BPM_top13', 0.03581881055668517),\n",
       " ('top3/top5', 0.16591578983634481),\n",
       " ('top3/top7', 0.19771467884271632)]"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model4.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred3 = lr_model4.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1364c3d68>]"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1364c3e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "\n",
    "plt.scatter(lr_test_pred3,y_test,alpha=0.5)\n",
    "plt.plot(np.linspace(-8,2,1000),np.linspace(-8,2,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RidgeReg MAD:  2.4513803656649826\n",
      "RidgeReg RMSE:  3.42863462424\n",
      "RidgeReg MSE:  2.45138036566\n",
      "RidgeReg r2:  0.161439756222\n"
     ]
    }
   ],
   "source": [
    "print('RidgeReg MAD: ',np.mean(np.abs(lr_test_pred3 - y_test)))\n",
    "print('RidgeReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred3)))\n",
    "print('RidgeReg MSE: ', mean_absolute_error(y_test,lr_test_pred3))\n",
    "print('RidgeReg r2: ',r2_score(y_test, lr_test_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elasticnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=5, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elasticnet\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n",
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
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       " mean: 0.08868, std: 0.05293, params: {'alpha': 0.11993539462092344, 'l1_ratio': 0.75},\n",
       " mean: 0.10598, std: 0.05386, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.0},\n",
       " mean: 0.09694, std: 0.04923, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.25},\n",
       " mean: 0.08631, std: 0.04907, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.5},\n",
       " mean: 0.07481, std: 0.04440, params: {'alpha': 0.19240241827602392, 'l1_ratio': 0.75},\n",
       " mean: 0.10267, std: 0.04965, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.0},\n",
       " mean: 0.08620, std: 0.04506, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.25},\n",
       " mean: 0.06456, std: 0.03713, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.5},\n",
       " mean: 0.05186, std: 0.03228, params: {'alpha': 0.30865526123848624, 'l1_ratio': 0.75},\n",
       " mean: 0.09410, std: 0.04354, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.0},\n",
       " mean: 0.06333, std: 0.03339, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.25},\n",
       " mean: 0.03867, std: 0.02269, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.5},\n",
       " mean: 0.02054, std: 0.01288, params: {'alpha': 0.49515006694731312, 'l1_ratio': 0.75},\n",
       " mean: 0.08043, std: 0.03544, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.0},\n",
       " mean: 0.03512, std: 0.01886, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.25},\n",
       " mean: 0.00897, std: 0.00566, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.5},\n",
       " mean: -0.01310, std: 0.02176, params: {'alpha': 0.79432823472428149, 'l1_ratio': 0.75}]"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SAcxJV+PubIbOaFTV4vD640Op6Lr+E24juQABYAARDVnZBRsW/r0x6OuUCuCmdDXuvjzT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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1369585c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Elasticnet\")\n",
    "params = {\n",
    "    \"alpha\": np.logspace(-4, -.1, 20),\n",
    "    \"l1_ratio\": np.arange(0,1,0.25)\n",
    "}\n",
    "\n",
    "grid_est = GridSearchCV(ElasticNet(), param_grid=params, cv=kfold)\n",
    "grid_est.fit(X_tr, y_train)\n",
    "df_en = pd.DataFrame(grid_est.grid_scores_)\n",
    "df_en[\"alpha\"] = df_en.parameters.apply(lambda val: val[\"alpha\"])\n",
    "plt.plot(np.log(df_en.alpha), df_en.mean_validation_score)\n",
    "grid_est.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet(alpha=0.19240241827602392, copy_X=True, fit_intercept=True,\n",
       "      l1_ratio=0.0, max_iter=1000, normalize=False, positive=False,\n",
       "      precompute=False, random_state=None, selection='cyclic', tol=0.0001,\n",
       "      warm_start=False)"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_est.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kennyleung/anaconda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:491: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0,\n",
       "      max_iter=1000, normalize=False, positive=False, precompute=False,\n",
       "      random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model5 = ElasticNet(alpha=0.1, l1_ratio=0)\n",
    "lr_model5.fit(X_tr,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.44621804614627048),\n",
       " ('Weight', 0.48368179643281189),\n",
       " ('Yrs_Played_College', 0.16409456327000191),\n",
       " ('FG_Perc_College', 0.58159322063129737),\n",
       " ('FT_Perc_College', 0.42979917331590795),\n",
       " ('RPG_College', 0.93077094128255411),\n",
       " ('APG_College', 0.36388989488253237),\n",
       " ('SPG_College', -0.4560137779139673),\n",
       " ('BPG_College', -0.4186088035450653),\n",
       " ('PPG_College', 0.083698945462539392),\n",
       " ('Rookie_Age', 0.05524090478432879),\n",
       " ('College_BPM', 0.25874748532658753),\n",
       " ('BPM_top3', 0.17924778020383267),\n",
       " ('BPM_top5', 0.15072161738345458),\n",
       " ('BPM_top7', 0.11651169686381162),\n",
       " ('BPM_top13', 0.025822081189939519),\n",
       " ('top3/top5', 0.15361703547491304),\n",
       " ('top3/top7', 0.17943991154584146)]"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,lr_model5.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr_test_pred4 = lr_model5.predict(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1365d23c8>]"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VxHPdibzGQqa3AmUAV5M2FgUlWJCKNrBd74OjUOFAOxULg4eKbvLN27AFLsUs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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1365d24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot predicted vs actual \n",
    "\n",
    "plt.scatter(lr_test_pred4,y_test,alpha=0.5)\n",
    "plt.plot(np.linspace(-8,2,1000),np.linspace(-8,2,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasReg MAD:  2.471304849630792\n",
      "ElasReg RMSE:  3.45870893141\n",
      "ElasReg MSE:  2.47130484963\n",
      "ElasReg r2:  0.146664356672\n"
     ]
    }
   ],
   "source": [
    "print('ElasReg MAD: ',np.mean(np.abs(lr_test_pred4 - y_test)))\n",
    "print('ElasReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred4)))\n",
    "print('ElasReg MSE: ', mean_absolute_error(y_test,lr_test_pred4))\n",
    "print('ElasReg r2: ',r2_score(y_test, lr_test_pred4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sorted_BPM = df.sort_values(['BPM_Rookie'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['University of Kentucky',\n",
       " 'Duke University',\n",
       " 'University of California, Los Angeles',\n",
       " 'University of Kansas',\n",
       " 'University of North Carolina',\n",
       " 'University of Arizona',\n",
       " 'University of Texas at Austin',\n",
       " 'University of Connecticut',\n",
       " 'Syracuse University',\n",
       " 'University of Florida']"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top colleges by number of drafted NBA players\n",
    "list(sorted_BPM.groupby(['College']).count()['BPM_Rookie'].sort_values(ascending=False).head(10).index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_college = df.groupby(['College']).mean().sort_values(by='BPM_Rookie',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# top colleges by rookie BPM\n",
    "good_colleges = ['Georgia Institute of Technology',\n",
    " 'Indiana University',\n",
    " 'Stanford University',\n",
    " 'University of Florida',\n",
    " 'University of Illinois at Urbana-Champaign',\n",
    " 'University of Kansas',\n",
    " 'University of Memphis',\n",
    " 'University of North Carolina',\n",
    " 'University of Pittsburgh',\n",
    " 'University of Washington']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BPM_Rookie = list(df_college.filter(items=good_colleges,axis=0)['BPM_Rookie'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colleges_BPM = list(zip(good_colleges,BPM_Rookie))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colleges_BPM.sort(key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('University of Memphis', -2.5777777777777775),\n",
       " ('University of Washington', -2.541666666666667),\n",
       " ('University of North Carolina', -2.5294117647058822),\n",
       " ('Stanford University', -2.3500000000000001),\n",
       " ('University of Kansas', -2.3333333333333339),\n",
       " ('University of Pittsburgh', -1.9333333333333336),\n",
       " ('Indiana University', -1.9200000000000004),\n",
       " ('Georgia Institute of Technology', -1.6199999999999999),\n",
       " ('University of Florida', -1.2583333333333335),\n",
       " ('University of Illinois at Urbana-Champaign', -0.75)]"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colleges_BPM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rank1 = [i[0] for i in colleges_BPM]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = dict(enumerate(rank1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = dict (zip(a.values(),a.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Georgia Institute of Technology': 8,\n",
       " 'Indiana University': 7,\n",
       " 'Stanford University': 4,\n",
       " 'University of Florida': 9,\n",
       " 'University of Illinois at Urbana-Champaign': 10,\n",
       " 'University of Kansas': 5,\n",
       " 'University of Memphis': 1,\n",
       " 'University of North Carolina': 3,\n",
       " 'University of Pittsburgh': 6,\n",
       " 'University of Washington': 2}"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def CollegeBPM(college):\n",
    "    '''\n",
    "    Returns a ranking from 0-10 based on what college the player played for. Player receives a 0 if they did\n",
    "    not play for the top 10 colleges based on BPM average.\n",
    "    '''\n",
    "    if college in good_colleges:\n",
    "        return res[college]\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['College_BPM'] = df['College'].apply(CollegeBPM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_columns = ['Total_Height_in',\n",
    " 'Weight',\n",
    " 'Yrs_Played_College',\n",
    " 'FG_Perc_College',\n",
    " 'FT_Perc_College',\n",
    " 'RPG_College',\n",
    " 'APG_College',\n",
    " 'SPG_College',\n",
    " 'BPG_College',\n",
    " 'PPG_College',\n",
    " 'Rookie_Age',\n",
    " 'College_BPM',\n",
    " 'BPM_top3',\n",
    " 'BPM_top5',\n",
    " 'BPM_top7',\n",
    " 'BPM_top13',\n",
    " 'top3/top5',\n",
    " 'top3/top7',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.loc[:,feature_columns]\n",
    "y = df.loc[:,\"BPM_Rookie\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_testrf = train_test_split(X, y, test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=9, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=5,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=500, n_jobs=-1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmodel1 = RandomForestRegressor(n_estimators = 500, min_samples_leaf=5, max_features=9, n_jobs=-1)\n",
    "rfmodel1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_test_pred1 = rfmodel1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x136936278>]"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mZwnp59kiS/ArEqKmA1sAQgCOANq15NeFAOJOMiC2qTK2L24p+fFqWaCS7z26ep6K/7yS\nQMgQ+NG5GK5N2IhV2KJtVUdybSa9PrN6ngpZktDfP4qVi0o/B7MZA88sVO/OwPXOvNwsbphp6tBy\nJvenyZmycUSytDhryuZDCyy8oaqunJf0OR5K2Hhj1ILpJANaV4uMjlTpcsQSCKWmgqrNGrKzhOzn\n2euXcUUAluPAsCXYQmC+X8Gmbh1W1rSVk/p6qWpZoCKEwJ8s1HF40MAfhg1cnnAwHHdQyZ0zbaqE\nd2YVALxzvo7OOlfsNQMGHnJdPTMvt4sbSpk6zA60Lalprp6W5MUfmMxq5snArpXunJf0Of7LJT6o\nGIdflTM3aqaDnQIHP70Qm7bFdSVZw9QsIT015TgCrWqya4nkCHT5JoNLeizlZnVuv4ZxS+DocG6l\n2bVYZTdormhXc0qa18xTocizo9LMSww8VDP1yLzcLm4odeowXx+0iCWgy5M7c55/c/KYbp2X61pV\nbOrx5R3fUFxAVxzXsobsALukTYUxbuFSNBlsVQnobVOwplPDsWEDR4YMrJqnVZTVVfsaXoxYySAz\nmOxp9scRE2YFcSagSri1R8v0NNsyX0d3y+ytNPMSAw/NKrUobih16jCoyQh2yPh0h+Zptpev3cuN\n7RoSU9aTgOrK2qdmsqoMfPtYGDIktChS5rE29/oQMmz87apWLGorv6CgnNcwYQv8cVzG/z0eRl8q\n0FyeqCybuTGoZLKZrb061nZqUJnN1AQDDzW8ciqbalHcUMnUYT2yvex2LxOmgFXg+lttWXv6uV2I\nWJAgoT1PlJAgIZA6/oWIlfe1K/S6FnsNu30Kfn0pkdlv5uiwAcNpATBe1nPwKxJu6ZlsN7Nlvl7W\nGhRVh4GHGlallU21Km6od9FGIemF+Hm6gnmpktyhuI0z4xa2tEyv0a2mrD07WBTLTGQAP70Qx6Wo\nlfc1mOl13bmiFc+cjuLgQAIXow4G4zYGY05WF4DyLG1TcroA3Nyl5dx4S95i4KGGVWllUyOUlXul\n0EJ8my4DEIgYTuq/kyrN/Ap9CFjapuJi1J6WmQzHHfiU/FsvpP976vf+5Y0IlrWrOdlMvIKS5hYF\nuKUnGWTSU2cLWpnNNBIGHmpIblQ2NWqG4qZiC/HLO1T0+OXMz1ST+RX6ELA4oGBZMLdEfFFAgWE7\nedeX3ggZkEQyMF6dsHEpmvzf5QkbYxVmMwt9Dt59fSCTzazv0ureHYGKY+ChhjQbW+/UgiYDcctB\nS1YZdZpfkXDXmjYAqCrzK/Yh4HzEwhc2tQPwZx5jNOHgu69Hcn42ajq4PGHjZMjCUNzBSNxBBbs0\nQ5eBTd2pTCb1v8ilN7Fy5ZLyD0Z1w8BDDamRWu+Uy4t9iLKnvt4K24iMWJl7hxR5+r0z1YyjlA8B\nS7Kq1yxHIJSwcXbcwtsRG5ejNsYr2D0TAK5vTW0F0OvD1vk6NnRr0wJsf0VHpnpi4KGG1Aitd8rl\n5T5E2VNfW+brOD5iYjBuo29AZDpiu9UpYqYPAY4Afv52DH0DBg4OGHhtyES0gnRGkYBN3Vomk1nb\nqcGvStxIcBZi4KGGVe/WO+Xyah+iqVNfiixhY48OwxYYNRzsvrkN17VW96cdtXLLoNMfAlQZGIon\nt2e+ELExFHfw349HK3qMVlXCAn9yK4XNPTo+vzGIdl3OBPAf9kfn1EaCcwkDDzWsZqpOK6UYwi2F\npr50JXkjZ6H7d0qRvuj3va2hbTwCxxGAlGz6+bO343grbFXUBUBGsq/bAr+Mu9a04T3X+dDpkzCW\nZyuAubaR4FzEwEMNz+vqtErWaEpZB3FLrda/HCHwyB/CODxk4EJIwdDQBEYSla3N+BVgcUDF9YFk\nRrOwVYEmSxgzHNx+vQ9L2lSETQdiyibQs3kjQZrEwEOUUs0aTSnB4KpL43Rr/SuUcHB4yMjcN/P7\nQSOrCCB5H1ApFAm4uTN7bUbF/z4TRas2/d4ZXU72QHv6VDTveWY149zAwEOUUskUT3Z2NFMwcCvw\nAOWvfzlCoH/MynRn7hswcDJklRhacnX5kpVmt6U6Adzao2Xa46Qd7jDznotFAQU/OB3FcCx5Y+vU\n8/yxd/grzua8qCYkdzDwEKH8KZ582dHSNhWLAwrOR/K3iSl3PPkuotlfL7b+NW44ODI0uRXA7weN\nitvNtKoS2lQJN3eq+NqWDqzv0iBJxTPAqYFRBjAcdxAxbRwaMOFLba2wLlX+nT7PgL/sbM50UDCD\nYjFCY2LgIUL5N6zmy44uRm0sC6r4wqb2ioshCk33/dcb/fg/b8XyXlwXBxScHbfw4kCypPnQoIET\no5VlMy0KoMsSArKNLr+ONk2CBKDDJ+PGdhWdPnnGoANMLwz56YU4fIqNmC2gyRI0WULIcHB8xMTG\nnmQ/ufR5LjebO3BNQax18rUwbIE/jhiIn3Lw6bXBCs4C1RoDDxHKW7CfKTsC/FjSVtmfVqHpvs//\nLoTrAwr8qgzFFrgyYeP3gxP4lxMRXJ1wMFJB8YIEYM285MZmW3p13NypQYKDB/vGMRq1oKkSBJLP\nfX2XBsMRZRcupAPvpaiV2axOlgBbCJgOMBi3M9uBp89zOdWMYdPBhbiMRUEJtiNwfMTESCK5FfeR\nQQOAhDtXB5j5NBgGHiKUt2BfqwXwfAFNiOT21a8OJNChy7gaczAYcyrKZvwKsG2BD7eligA2z9fR\nkXXfzPNvTcBwAEWW0SILbOrR0KrJFe0imi37fKlSsn3OcMKBhOSW2EeGDGzs0rC8I/f4pVQzjiYc\nWCJ5No6PmAgZTjKjAhC3gdMhk2XYDYiBhyil1CmeWpUzjyYcRC2BwbiFy1Ebl6LJ/mYTmS4A5WU1\nXT4JiwIqFqVKmoNqcoO4qRfhqVnWrT0a/l8UODZiYk2nhjZNqurG3ezzdXzERECTELclxGwAAhhP\nOLgWc/DA5vKP3+mToUrJwDiScHK2OlAkIKjLLMNuQAw8RCmlTvG4Vc4shMD5iJ1Zl3n1WgLHRypb\nmwlqEt6Z2gZgXZeK/7ySQKdvejnz1Ivw1CwrPV0VcyTAdJBwBFYH1KoW6tPn63TITAUHGQtbZRiW\nA78m49b5OhwhkHCm75hayrFvaHFwzUxOr2mpr9uOQKcvma2NGYJl2A2GgYdoilKmeCpp5xO3gd9d\nS2QqzfoGDQzEKruxtMuXvHiv79Tw2fVB3DRPhZL6tH8hYuG3V428vzd1KnDqtGFmukoCJEVGuybj\n6oRT9XTVzhWt+M7rYcQsBxOSBJ8socevZJqaDsUcvDFqYm1n+dN5dyyw8bKt4shgcv8eRUpmQuu7\nkmGo0ZvKzkUMPEQo/x6QmbIjIQQuRu2cIPOHYT9sMVT22BQp2aVZkYF2TcatPTradalgyXA5U4HZ\nPzt1ukqRAJ8iudY1IKDKcCBg2YAKAQkybAEcHzJwLWZDADnTeqVmP5oMfHp1EICE0yETQV3OdLBu\n5KaycxkDD81p1XaUTmdHcUvg4LUEDqVuzuwbNHBlYmo2U9qFdGmbgrjlQJUltGky2jWgqyWZHURM\nB397U1tyzabAxbScqcDsn43ZIjNdZQtgvi/rAl5l14BnTkfxi7djiJpAzHIQliWMGSYuRCwEVAm9\nfgXz/cmpwUr7st25OpB5LccM0fBNZecyBh6a0yptSHkpml6bSaBvwMAfhpOBq1x+RcLNXSpu69Xx\npwt92NKr43+ejuLFczEEsqrbxhIOjo2YuLFdRUCVipYXjyYcfGhpC358Pl7SVGB62vCNkAHLETAl\nIKiIzFQVUN10Vdh08JMLcUxYAte1KhiI2YjZQNQSGIo72LpAz32sCjOsZmoqO9cx8FCO9IUratV/\nDLVufVJqt4LkDYnm5LTZgIFLE3ZFj7m0TcHWXh239ugYjtuI2w4sIUGRgIGYAxnAtZgNXc7NjhRZ\nwmjCwY0i/700hTK3z60PYsKa3gF66vNNXrD9+N6JKIZiNozwRGbNqNrpqosRC8MJG22p87ywVYXl\nCExYApeiFpa2Ta5PZZ5PFRnWXNjyvNkx8BCA6ReuyJiGLXLU07YjXm6kBhS+HydiOugfs/DAoTGc\nDFk4OmwgUUGcaVGAW3p0bE1Vm3VHLuJdN68EkGzxMiCAQFYjzXNhC0+fmoCEZDuZkOFAyeoSYNrA\nglY170W1UOZ24Hy85CmroCbjMze3JbdFGAPGDMe96aoppXqqLCGgAYok5Z2AZEHA7MbAQwCmX7hs\n2fs9ULzeh6XTJ0ORkl0ALkXt1L0zlW/TvKRNyQSZrfN1rOvScgJmf2qP5mKZ1rWYBSEB67q0nLvw\nFQnobJGxa9X0AFBq5lZKJpnOfjY6l9G9pM2V6arFbSq6W2RMmCI3sxFAV4uMlikfKlgQMPsx8FBD\n7IHi1RgGYnZmuuzQYLJ5ZiUbm/kUYFN3sjvzllQngOtap983k0+xzgeQJCz0yxg3RGZX0bgtIANY\nNU9DV8v0x5ipk8JgzMZzb+bv81YokwyoqLjtz1RBTcYHb/DjZ2/HMG6ITCBt1yV89MYAWlS5aXaZ\nJXcw8FBD7IFSizFYqZsh09sAHBo0cC5c2drMolYl09Ns63wdG7q1TMVXuWYqd/7kqgAOlFgYUMrx\nfnExjqsTTl139PzEqgBUWcKJUQNhSyCoSljTqWeCX9h0cClqQwiBxW0qe6vNcgw8VLMWMF6PYThu\nZ+6ZOTRg4MiQmdVupnSaDGzs1rBlvo7bepOVZosCpWUzpchX7mzYAuOmg1UdKrpblLKqs6YeL5HK\nkhQANwTVTIPObF7v6JldwDD1ORm2KDsjo+bmWuARQuCOO+7AkiVLAADr16/HZz7zGbcOTzXkVgsY\nL8dgOwJvhCz85IqKt66MoG/AwJsVZjML/XJmumzrfB0bu3W0qLW94KVLmE+FTBwfNRE2HLRrMlrk\nZOHBzhWt06qziq3R7FzRimdOR/GTCzEMxx1AArp9Cub5ZMQtwJ/nL70eO3rmqzjzem2P6s+1wHPx\n4kWsXr0ajz76qFuHJA9NbQETc4B1QdXTufZibWhG4jb6Bs3MlNmRQQMRSwDQAcRKfgxVAtZ3a9g6\nPxlotvTqWBJQStpjxk3pDOBf3oggYYucu+2nXnRLqfbTFQmqLGHNPA0OgJZUx4FQwsGZcQtbWvTp\nY2iAyrFGWF8k77kWeE6ePInBwUHs3r0bPp8Pe/bswdKlS906PNXY1Jvvht8exCaPP22mxxBK2Dg8\nZKB/zMbBAQPfeT2C/rHKbizq9cvYkg4y8/Vkq3+1MS5kyXUNCz3+3Gm8qRfd7IxATk2jTW33n76A\nt02Zr0z+WyBiODnfa5TKsUZYXyTvSaFQqOxJ8BdeeAH79+/P+dr999+PkZERvP/978fRo0fx2GOP\n4emnny7peP3pOlOak8IWcCws49i4gmNhGcfDMqJ2+RmIAoGVAYH17TbWBx2sb3ewyCfgcTJTsitx\nCc9eVhDMs44RtgT+ZpGNoCrwvbc16BJwOiJh3JbgiORman5F4BurDMzTCx/LcIAhA7i+RSDmSLAc\nAVWWcEOLgzsW2Mi+pkctYNyS0K4KBEr8SFrJ70z9/e+9rcGfJ7bEHOCuJWZFx6X6WrlyZdHvV/SS\n7tixAzt27Mj5Wjweh6IkP7lt2rQJQ0NDEEKUNIUx0yCL6e/vr+r3vcSxAo4QOD1mTZY0Dxg4VWE2\n0+2TsTZg4M9v7MLWXh23dGsIVPnpuJZdE6ae04Wmg1/Gx6dNMwGA33Kw6aZ2jCYctI1H8Na4BUtz\nEPRN/j1FLQf/aXdiz8r2acfK7MZpOEjYAkvadKxv17B9kQ/zW3P7vGWm8iKTU3ltsRHc9yc3Flzc\nz/c7lRYEbJGjedf21gXVkrLuZvm7apZxArUfq2ufJZ588kl0dHTgk5/8JE6fPo0FCxZ4Pm9OjWfc\ncHB4MLkuc2gged/MmFF+pZksAWs7NdyWmjLb2qvjxqCCM2fOYOXKYNXj9LprAlBGQYUQGJ2yyRkA\n6LKEqzE7MyWXfaz09gYygIWtCjp8Cq5O2Hj5qjHjRnAAcDkuF13cd7MgoJItJqi5uRZ4du3ahQcf\nfBCvvPIKFEXBvn373Do0NQkhBM6MWziYymb6BgycCFW2sVmnT0p1AfBhy3wdt86v7XpEvSqrZrro\nBjUZC/ykdRCHAAAT+0lEQVQqTMfMmRazhUCXT4YEKbMOkt3s88qEDZ8i5e5Lk2fBPmw6eCNkIN24\nJh0ANXn6pnFpbhcEsLnn3ONa4Glvb8djjz3m1uGoCYRNB0cGJ/eb6Rs0MJooP8xIANZ0qpPtZnp1\nrGhXPcuY61lZVcpFd9fqVrx8NYGIOdk+p8snY12XBsOZbBqaPtaJUQ0RU2B+izLtJtfsBXvDFvje\nG1G8es2AKkk5x536s9lqVRDA5p5zB5ftqCRCCLw5bmf2mzk0aOCNURNOBelMhy5NtpqZr+PW+To6\nCt096oFGqKwqdtHtalGwY5kfp0NmTql0ocq0xW0qulN76aRvJm1RpOSmblkl1PvPTGAobsMnS5lp\nvJDh4PiIiSVS4XLrRrjhmJobAw/lFbOBl68kMl0A+gYMDCcq26b5pnlqJtDc1qtjZYcKuYHW/5rh\nQrpzRSu+fyqCEyELhi0QyNqpc6qgJmNpm5rpjZaugmvXJfyXJf5Mw9B0+XWnT8ZYwoEiS1AkCSMJ\nB10qsK5AuXUj3HBMzY2BhyCEwPnI5DbNhwYMvD7ih43yt2lu1yRszgoym3v0hrhwF9PoF9J04cOl\nqA0ZycrARYESCh9Ezv/lbE2QneWt79JwbMTEaKoTtuUIdLWKoov7LAigajDwzEExS+C1oVSQSU2d\nDcYr26Z5ZYea6mmWDDarO6Zv6tUMKrmQerVhXU7hQ+ov9uqEXbDwIWw6OB+xsLnXl+lunZ6eOx+x\nEDadnCxPkSVs6tGRsAUStoAQAv+tY7xoUGNBAFWDgWeWE0LgQsSenDIbNHBs2EQFvTPRpk5mM+lC\ngM4Gz2ZKVc6F1MvS60oKH7KzGT0VcDJjT61ZLWlTp2V5PiVZ27YsqCFQ4svKggCqBAPPLBO3BI4O\nTxYA9A0YuBqrbG1mebuSumcm2aF57bzmzGbKUcqF1MvS60oKH0pdsyqW5Z1/09WnQZSDgafJXYxY\nOdnMH4bNijY2a1Ul3NqjZXqadUcuYuva5rjLuhKVTpN5XXpdSeFDqWtWnC6jemHgaSIJW+CPw+Zk\nSfNAApcnKstmlgWVnPtmbu7UoGZlM7O1fV6102Rel15XWvhQzpoVp8vIaww8DezKxGSlWd+AgT+M\nGEhUsOVMiwLc0qPnbAXQ63dvY7Nm8v1TEZwes9CuyejQ829DUEw9Sq8rKXxgNkONjIGnQRi2wLER\nM2cHzYvRyjY2W9KmZILM1l4d67q0aX2+vOJV5ddMj2vYAt8/FcX/OjMBecpd+uVMk9Wj9LraIFJJ\nyyKiWmLgqZNrE5NdAPoGDbw2ZCBeQZzxKcAt3cksJt08c2Fr/bOZejTdLPa4lpPcw0aRpEwbmfRd\n+ht79LKmyep1D0s5U2L1Ov9EpWDg8YDpCLw+YuInl1WcuzyCgwMGLkQqy2YWB5TJdjO9OjakPrE3\nmno13cz3uKdDJk6ELGzo1pCd+KXv0jdsUdY0WTNMY3E7aWpkDDw1MBS3M+syBwcMvDZkImaXv02z\nLgMbu7XUfTPJkuZFgfpnMzOpV9PNQo9rAxhO2JCg5bSHAQBbJH9vfZde9pgadVGe20lTo2PgqZLl\nCLwxaqJv0MhsB/BWuLJs5rpWOaen2YYuHS1q42UzM6lX081Cj9uiSIAA4raY1h7GEQIrO9RZ1eql\n2vMfNh1ciUtYyABFNcLAU6aReFaH5gEDR4ZMRCtoA6BKwIbu5H0z6bLmxQFlVmyeV6+mm4Ue16dI\n6G6RISO3PUzYcLBqnoZPr22ryXjqpdLzn70uNDCi4Jfxca4LUU0w8BRhOwInQlZOF4Az45Vt09zr\nl7G21cD7buzCll4dm7p1+JswmylFvZpuFnvcD97ghypLOYvtG7r1WZXppFV6/rPXhYKKBL8qc12I\naoKBJ0so4eR0ATg8aCBslp/NKFKy4292T7Olbe5t09wM6lX5VexxdUVq6IIAN5V7/rkuRF6as4HH\nEQKnQpPtZg4NGDg9Vlk209MiZ0qZt/TquKVbQ2CO/5FWW/lV6f0/Mz1uoxYEuK3c898Im+HR3DFn\nAs+Y4eBwVgHA74cMjBvlZzOyBNzcOdnT7LZeHcuCs2NtphbKvdC7df/JXAkwMyn1PDTDZng0e8zK\nwOMIgTNjVjLIpNZmToasiu7g7vRJqekyH7b26ri1R0MbL2g1w/tP6qPRN8Oj2WVWBJ5xw8GRocme\nZn2DBkIVZDMSgDWdanJTs9TU2fJ2ldmMR7jOUF/Z60JhS8BvOdxVlGqiqQPPmOHg40dacPaVK3Aq\nSGc6dCmnQ/OtPTraC803UM1xnaG+steFjp4cwqab2nm+qSaaOvC0axJCFkoOOmvmqTk9zVZ2qJCZ\nzTQMrjM0hqAmY2GLYNChmmnqwCNJEtYHHfx6ePofSLsm4Z1Z2czmHp0XrgbHdQaiuaGpAw+AVOAB\nVnWomftmtvbqWD2P2Uwzqtf9P0TknaYPPB9eaOG+dy9FJ7OZWaEZOj8TUXWaPvC0q2DQmYV4Hw7R\n7MW/bCIi8hQDDxEReYqBh4iIPMXAQ0REnmLgISIiTzHwEBGRpxh4iIjIUww8RETkKQYeogqETQcX\nIhbCplPvoXhuLj93ckfTdy4g8pJbO6QWUumW37U6TrZaP3eaOxh4iMpQqx1S3bqo1zI4cHdYcgun\n2ohKlN4hdeoFPHuH1EplX9Q7dBl+Vc5c1OtxnKlq+dxp7mHgISpReofUfNI7pFbCrYt6LYNDrZ47\nzU1VBZ6XXnoJDzzwQObfx44dw5133om77roLTz75ZNWDI2oktdoh1a2Lei2DA3eHJTdV/G555JFH\n8N3vfhdCTO47/c1vfhNf//rX8eSTT+L111/HqVOnXBkkUSNI75Bq2Ll7rVe7Q6pbF/VaBodaPXea\nmyouLtiwYQPe+9734vnnnwcARCIRmKaJxYsXAwC2bduGQ4cOYfXq1TMeq7+/v9JhuPL7XuJY3efl\nOLcAuDah4EJchuUIqLKEG1ocbAnaKGUYhcbaFlNwOS4j+/ptOsD1LQ6unhvE1RLH59Zx8o212ude\nragFjFsS2lWBwJQrF9+r7qtmrCtXriz6/RkDzwsvvID9+/fnfG3fvn3Yvn07Dh8+nPlaNBpFIDBZ\n2dLa2opLly65Mshi+vv7q/p9L3Gs7qvHONeuRkU7pBYb633vmF6Ntq6CajS3jlNorKU+dzfLuTOV\nepH8lXp8r7qv1mOdMfDs2LEDO3bsmPFAgUAAExOTlTMTExMIBoPVjY6oQbm9Q6pbW357sXV4sede\ni3JulnHPPq69I9va2qCqKi5evAghBF599VVs2rTJrcMTzQlBTcaSNrXqYOHWccrldjk3y7hnJ1dv\nIP3Sl76Effv2wbZt3HbbbVi3bp2bhyeiBpYOEn41N9hlB4lyA2G6Us+f53ss425eVQWezZs3Y/Pm\nzZl/r1+/Hk899VTVgyKi5lNKkCg38JRSqVdOwQQ1BtZAEpEralHOzTLu2YmvGhG5olZBYueKViwL\nqohZDsYMBzHLwbKgip0rWt0YNtUBm4QSkWt2rmgtWNVWKS8q9chbDDxE5JpaBgm3S9ipfhh4iMh1\nDBJUDN8ZRETkKQYeIiLyFAMPERF5ioGHiIg8xcBDRESeYuAhIiJPMfAQEZGnGHiIiMhTDDxEROQp\nBh4iIvIUAw8REXmKgYeIiDzFwENERJ5i4CEiIk8x8BARkacYeIiIyFMMPERE5CkGHiIi8hQDDxER\neYqBh4iIPMXAQ0REnmLgISIiTzHwEBGRpxh4iIjIUww8RHNI2HRwIWIhbDr1HgrNYWq9B0BEtWfY\nAvvPTODsuAnDAXQZWN6uYeeKVuiKVO/h0RzDjIdoDth/ZgLnwhb8qowOXYZflXEubGH/mYl6D43m\nIAYeolkubDo4O25Oy2x0RcLZcZPTbuQ5Bh6iWW404cAoEFsMBwglGHjIWww8RLNcp0+GXuAvXZeB\neT5eBshbfMcRzXJBTcbydg2GLXK+btgCy9s1BDVeBshbfMcRzQE7V7RiWVBFzHIwZjiIWQ6WBVXs\nXNFa76HRHMRyaqI5QFck7FodQNh0EEo4mOeTmelQ3TDwEM0hQY0Bh+qvqnfgSy+9hAceeCDn3x/9\n6Edxzz334J577sGRI0eqHiAREc0uFWc8jzzyCF599VWsWrUq87WTJ0/i3nvvxfve9z5XBkdERLNP\nxRnPhg0b8MUvfjHnaydPnsSPf/xj3H333fj2t78Ny7KqHiAREc0uUigUEsV+4IUXXsD+/ftzvrZv\n3z6sXbsWhw8fxnPPPYeHHnoIAPDDH/4Qt99+O66//np885vfxPLly/FXf/VXMw6iv7+/iqdARESN\nZOXKlUW/P+NU244dO7Bjx46SHuzDH/4wgsEgAOA973kPfv3rX5f0ezMNspj+/v6qft9LHKv7mmWc\nAMdaK80y1mYZJ1D7sbpW3iKEwMc//nFcu3YNANDX14c1a9a4dXgiIpolXCunliQJe/fuxRe/+EX4\nfD7ceOON+MhHPuLW4YmIaJaoKvBs3rwZmzdvzvx727Zt2LZtW9WDIiKi2Yt3khERkacYeIiIyFMM\nPERE5CkGHiIi8hQDDxEReYqBh4iIPMXAQ0REnmLgISIiTzHwEDW4sOngQsRC2HTqPRQiV3AHUqIG\nZdgC+89M4Oy4CcMBdBlY3q5h54pW6IpU7+ERVYwZD1GD2n9mAufCFvyqjA5dhl+VcS5sYf+ZiXoP\njagqDDxEDShsOjg7bk7LbHRFwtlxk9Nu1NQYeIga0GjCgVEgthgOEEpUHni4ZkT1xjUeogbU6ZOh\nF/hYqMvAPF/5nxm5ZkSNghkPUQMKajKWt2sw7Nyd6Q1bYHm7hqBW/p8u14yoUTDwEDWonStasSyo\nImY5GDMcxCwHy4Iqdq5oLftYXDOiRsKpNqIGpSsSdq0OIGw6CCUczPPJFWU6wOSakT/P99JrRpUe\nm6hcDDxEDS6oVR5w0mqxZkRUKb7biOaAWqwZEVWK7zaiOcLNNSOianCqjWiOcHPNiKgaDDxEc4wb\na0ZE1eC7j4iIPMXAQ0REnmLgISIiTzHwEBGRp6RQKCRm/jEiIiJ3MOMhIiJPMfAQEZGnGHiIiMhT\nDDxEROQpBh4iIvIUAw8REXmKgYeIiDzV9E1CI5EI9u7di4mJCei6jq997Wvo6emp97Dyevrpp/G7\n3/0OABAOhzE8PIyf//zndR7VdLZt49vf/jZOnDgBwzBw991348/+7M/qPay8hBC44447sGTJEgDA\n+vXr8ZnPfKbOoyru3LlzuPPOO/Hzn/8cPp+v3sPJKxaL4atf/SrGx8ehaRoefPBB9Pb21ntY00Qi\nEezbtw/RaBSmaeK+++7Dhg0b6j2sol566SX86le/wje+8Y16D2Uax3Hw8MMPo7+/H7quY+/evZm/\nLTc1feA5cOAAli9fjs9+9rP40Y9+hH/7t3/DfffdV+9h5bVr1y7s2rULALBnzx7ce++9dR5Rfj/7\n2c9gWRa+973vYWBgAL/61a/qPaSCLl68iNWrV+PRRx+t91BKEolE8Pjjj0PX9XoPpagf/ehHuOmm\nm3DXXXfhwIEDeOaZZ/D5z3++3sOa5tlnn8WWLVuwc+dOnD9/Hg888ACeeeaZeg+roEceeQSvvvoq\nVq1aVe+h5PWb3/wGhmHgqaeewrFjx/D444/jW9/6luuP0/SBZ/ny5Th37hwAIBqNQlUb/ym99NJL\naG9vx7Zt2+o9lLxeffVVvOMd78CePXsghMA//MM/1HtIBZ08eRKDg4PYvXs3fD4f9uzZg6VLl9Z7\nWHkJIfBP//RP2L17N77whS/UezhF7dy5E7ZtAwCuXr2KYDBY5xHlt3PnTmiaBgCwLKvhA/qGDRvw\n3ve+F88//3y9h5LX0aNH8a53vQtAcvbgxIkTNXmcxr9KZ3nhhRewf//+nK/df//9OHjwIP76r/8a\nY2Nj+Nd//dc6jS5XvrHu27cPa9euxQ9+8IOGSbPzjbOzsxO6ruPRRx/Fa6+9hn/8x39siPNa6PXf\ntWsX3v/+9+Po0aPYt28fnn766TqNcFK+sV533XXYvn17w33aLfZe3b17N86ePYvvfOc7dRrdpGLj\nHBoawoMPPoi///u/r9PochUa6/bt23H48OE6jWpm0WgUbW1tmX/LsgzLslz/QN/0vdruv/9+bNu2\nDR/72MfQ39+PBx98EM8++2y9h1XQm2++iUcffbQh/pAL2bt3L/7iL/4C73vf+wAAH/jABxpyLQoA\n4vE4FEXJfOr94Ac/iAMHDkCSpDqPbLqPfexjmXWS48ePY+3atQ0R0Gdy7tw57Nmzp2E/pZ85cwZ7\n9+7F5z73Obz73e+u93BmdPjwYTz33HN46KGH6j2UaR577DGsW7cO27dvBwDccccdOHDggOuP0/RV\nbcFgMBOhOzs7EY1G6zyi4vr6+jKpbKPauHEjXnnlFQDA6dOnsXDhwjqPqLAnn3wy88ny9OnTWLBg\nQUMGHQB47rnn8MQTT+CJJ55Ad3c3/vmf/7neQyroBz/4AX76058CAPx+P2S5MS8Vb775Jr785S/j\n61//elMEnUa3ceNG/Pa3vwUAHDt2DMuXL6/J4zTVVFs+99xzDx566CH8x3/8ByzLwle+8pV6D6mo\n8+fPY+vWrfUeRlEf+chH8PDDD+Pv/u7vIITAl770pXoPqaBdu3bhwQcfxCuvvAJFUbBv3756D2lW\n+NCHPoSvfe1rePHFF2HbdsOe1+9+97swDCNTXNLW1laTxfC54vbbb8fBgwfxqU99CkKImr3uTT/V\nRkREzaUx82ciIpq1GHiIiMhTDDxEROQpBh4iIvIUAw8REXmKgYeIiDzFwENERJ5i4CEiIk/9f1zm\nV6oi1kCkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1369360b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(rf_test_pred1,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(-8,0,1000),np.linspace(-8,0,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.033073070309728225),\n",
       " ('Weight', 0.04463838669743974),\n",
       " ('Yrs_Played_College', 0.011233176597726239),\n",
       " ('FG_Perc_College', 0.092341950420620197),\n",
       " ('FT_Perc_College', 0.055059258427671003),\n",
       " ('RPG_College', 0.079875180062566081),\n",
       " ('APG_College', 0.098649178438206281),\n",
       " ('SPG_College', 0.11429390701000072),\n",
       " ('BPG_College', 0.062470746232222915),\n",
       " ('PPG_College', 0.076651764158986585),\n",
       " ('Rookie_Age', 0.034188295248780198),\n",
       " ('College_BPM', 0.008565584193588131),\n",
       " ('BPM_top3', 0.050367851058289642),\n",
       " ('BPM_top5', 0.05472528542425921),\n",
       " ('BPM_top7', 0.042574260792567035),\n",
       " ('BPM_top13', 0.067305098932863461),\n",
       " ('top3/top5', 0.034587938124709304),\n",
       " ('top3/top7', 0.039399067869775359)]"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfmodel1.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest1 MAD:  2.5840308667504384\n",
      "RandomForest1 RMSE:  3.60807065769\n",
      "RandomForest1 MSE:  2.58403086675\n",
      "RandomForest1 r2:  0.0713716818715\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest1 MAD: ',np.mean(np.abs(rf_test_pred1 - y_testrf)))\n",
    "print('RandomForest1 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred1)))\n",
    "print('RandomForest1 MSE: ', mean_absolute_error(y_testrf,rf_test_pred1))\n",
    "print('RandomForest1 r2: ',r2_score(y_testrf, rf_test_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=9, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=1000, n_jobs=-1, oob_score=False,\n",
       "           random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmodel2 = RandomForestRegressor(n_estimators = 1000, min_samples_leaf=1, max_features=9, n_jobs=-1)\n",
    "rfmodel2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_test_pred2 = rfmodel2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x136bceba8>]"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ijba1x3zf73DcTj8zc2jAwKtDJqIlPKApI1kVtiigYFOnhi9sbsWS5uKymWKL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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136bcecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(rf_test_pred2,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(-8,0,1000),np.linspace(-8,0,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.03829042545214803),\n",
       " ('Weight', 0.087443839719331853),\n",
       " ('Yrs_Played_College', 0.014271628840042521),\n",
       " ('FG_Perc_College', 0.086307713964138888),\n",
       " ('FT_Perc_College', 0.060579487990803343),\n",
       " ('RPG_College', 0.070548386188091936),\n",
       " ('APG_College', 0.087629588359695615),\n",
       " ('SPG_College', 0.092417305544531009),\n",
       " ('BPG_College', 0.059022902989296605),\n",
       " ('PPG_College', 0.075874473280441654),\n",
       " ('Rookie_Age', 0.036305030350018426),\n",
       " ('College_BPM', 0.010211157315373857),\n",
       " ('BPM_top3', 0.05380572818303344),\n",
       " ('BPM_top5', 0.05637155152259065),\n",
       " ('BPM_top7', 0.042604005382281339),\n",
       " ('BPM_top13', 0.059314105875716568),\n",
       " ('top3/top5', 0.034642196750176914),\n",
       " ('top3/top7', 0.034360472292287086)]"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfmodel2.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest2 MAD:  2.6037598039215695\n",
      "RandomForest2 RMSE:  3.64068081493\n",
      "RandomForest2 MSE:  2.60375980392\n",
      "RandomForest2 r2:  0.0545097256145\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest2 MAD: ',np.mean(np.abs(rf_test_pred2 - y_testrf)))\n",
    "print('RandomForest2 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred2)))\n",
    "print('RandomForest2 MSE: ', mean_absolute_error(y_testrf,rf_test_pred2))\n",
    "print('RandomForest2 r2: ',r2_score(y_testrf, rf_test_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forest 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='sqrt', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=600, n_jobs=-1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmodel3 = RandomForestRegressor(n_estimators = 600, min_samples_leaf=1, max_features='sqrt', n_jobs=-1)\n",
    "rfmodel3.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_test_pred3 = rfmodel3.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x136d9ba58>]"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FKKnglDpeod3XJCk/xwLPhQsXsGHDBjz++ONOPSSVqBPagczGLIxOGekCgFen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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136d9b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(rf_test_pred3,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(-8,0,1000),np.linspace(-8,0,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Total_Height_in', 0.042631021080917417),\n",
       " ('Weight', 0.073424961871677755),\n",
       " ('Yrs_Played_College', 0.017099092492982252),\n",
       " ('FG_Perc_College', 0.074082521362106982),\n",
       " ('FT_Perc_College', 0.058275119827430701),\n",
       " ('RPG_College', 0.067052589751974867),\n",
       " ('APG_College', 0.08380227586113928),\n",
       " ('SPG_College', 0.086025116565103768),\n",
       " ('BPG_College', 0.058476079633569746),\n",
       " ('PPG_College', 0.065972006497391156),\n",
       " ('Rookie_Age', 0.036812051665900479),\n",
       " ('College_BPM', 0.011466951270038807),\n",
       " ('BPM_top3', 0.059654784632707847),\n",
       " ('BPM_top5', 0.057452327877918506),\n",
       " ('BPM_top7', 0.054475901195234648),\n",
       " ('BPM_top13', 0.065351440012469661),\n",
       " ('top3/top5', 0.044114531994099829),\n",
       " ('top3/top7', 0.043831226407336263)]"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfmodel3.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest3 MAD:  2.568478758169935\n",
      "RandomForest3 RMSE:  3.62152693752\n",
      "RandomForest3 MSE:  2.56847875817\n",
      "RandomForest3 r2:  0.0644321369671\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest3 MAD: ',np.mean(np.abs(rf_test_pred3 - y_testrf)))\n",
    "print('RandomForest3 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred3)))\n",
    "print('RandomForest3 MSE: ', mean_absolute_error(y_testrf,rf_test_pred3))\n",
    "print('RandomForest3 r2: ',r2_score(y_testrf, rf_test_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosted Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbmodel1 = GradientBoostingRegressor(n_estimators = 1000, learning_rate = .01, max_depth = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.01, loss='ls', max_depth=1, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=1000, presort='auto', random_state=None,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbmodel1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_vals_gb1 = gbmodel1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13618d198>]"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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teIQQOHz4MDZs2AAA2Lt3Lz7zmc+49fBtI7/SswQwlbLwdkxGLJ5C3BR4bvQq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13618d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot actual vs predicted\n",
    "\n",
    "plt.scatter(pred_vals_gb1,y_testrf,alpha=.5)\n",
    "plt.plot(np.linspace(-6,0,1000),np.linspace(-6,0,1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradBoost MAD:  2.6433299893234876\n",
      "GradBoost RMSE:  3.61637664287\n",
      "GradBoost MSE:  2.64332998932\n",
      "GradBoost r2:  0.0670912495491\n"
     ]
    }
   ],
   "source": [
    "print('GradBoost MAD: ',np.mean(np.abs(pred_vals_gb1 - y_testrf)))\n",
    "print('GradBoost RMSE: ',np.sqrt(mean_squared_error(y_testrf, pred_vals_gb1)))\n",
    "print('GradBoost MSE: ', mean_absolute_error(y_testrf,pred_vals_gb1))\n",
    "print('GradBoost r2: ',r2_score(y_testrf, pred_vals_gb1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinReg MAD:  2.4485198412285922\n",
      "LinReg RMSE:  3.41450607665\n",
      "LinReg MSE:  2.44851984123\n",
      "LinReg r2:  0.168336511944\n",
      "\n",
      "\n",
      "LassoReg MAD:  2.448321875853369\n",
      "LassoReg RMSE:  3.42145454119\n",
      "LassoReg MSE:  2.44832187585\n",
      "LassoReg r2:  0.164948224448\n",
      "\n",
      "\n",
      "RidgeReg MAD:  2.4513803656649826\n",
      "RidgeReg RMSE:  3.42863462424\n",
      "RidgeReg MSE:  2.45138036566\n",
      "RidgeReg r2:  0.161439756222\n",
      "\n",
      "\n",
      "ElasReg MAD:  2.471304849630792\n",
      "ElasReg RMSE:  3.45870893141\n",
      "ElasReg MSE:  2.47130484963\n",
      "ElasReg r2:  0.146664356672\n",
      "\n",
      "\n",
      "RandomForest1 MAD:  2.5840308667504384\n",
      "RandomForest1 RMSE:  3.60807065769\n",
      "RandomForest1 MSE:  2.58403086675\n",
      "RandomForest1 r2:  0.0713716818715\n",
      "\n",
      "\n",
      "RandomForest2 MAD:  2.6037598039215695\n",
      "RandomForest2 RMSE:  3.64068081493\n",
      "RandomForest2 MSE:  2.60375980392\n",
      "RandomForest2 r2:  0.0545097256145\n",
      "\n",
      "\n",
      "RandomForest3 MAD:  2.568478758169935\n",
      "RandomForest3 RMSE:  3.62152693752\n",
      "RandomForest3 MSE:  2.56847875817\n",
      "RandomForest3 r2:  0.0644321369671\n",
      "\n",
      "\n",
      "GradBoost MAD:  2.6433299893234876\n",
      "GradBoost RMSE:  3.61637664287\n",
      "GradBoost MSE:  2.64332998932\n",
      "GradBoost r2:  0.0670912495491\n"
     ]
    }
   ],
   "source": [
    "print('LinReg MAD: ',np.mean(np.abs(lr_test_pred1 - y_test)))\n",
    "print('LinReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred1)))\n",
    "print('LinReg MSE: ', mean_absolute_error(y_test,lr_test_pred1))\n",
    "print('LinReg r2: ',r2_score(y_test, lr_test_pred1))\n",
    "print('\\n')\n",
    "print('LassoReg MAD: ',np.mean(np.abs(lr_test_pred2 - y_test)))\n",
    "print('LassoReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred2)))\n",
    "print('LassoReg MSE: ', mean_absolute_error(y_test,lr_test_pred2))\n",
    "print('LassoReg r2: ',r2_score(y_test, lr_test_pred2))\n",
    "print('\\n')\n",
    "print('RidgeReg MAD: ',np.mean(np.abs(lr_test_pred3 - y_test)))\n",
    "print('RidgeReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred3)))\n",
    "print('RidgeReg MSE: ', mean_absolute_error(y_test,lr_test_pred3))\n",
    "print('RidgeReg r2: ',r2_score(y_test, lr_test_pred3))\n",
    "print('\\n')\n",
    "print('ElasReg MAD: ',np.mean(np.abs(lr_test_pred4 - y_test)))\n",
    "print('ElasReg RMSE: ',np.sqrt(mean_squared_error(y_test, lr_test_pred4)))\n",
    "print('ElasReg MSE: ', mean_absolute_error(y_test,lr_test_pred4))\n",
    "print('ElasReg r2: ',r2_score(y_test, lr_test_pred4))\n",
    "print('\\n')\n",
    "print('RandomForest1 MAD: ',np.mean(np.abs(rf_test_pred1 - y_testrf)))\n",
    "print('RandomForest1 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred1)))\n",
    "print('RandomForest1 MSE: ', mean_absolute_error(y_testrf,rf_test_pred1))\n",
    "print('RandomForest1 r2: ',r2_score(y_testrf, rf_test_pred1))\n",
    "print('\\n')\n",
    "print('RandomForest2 MAD: ',np.mean(np.abs(rf_test_pred2 - y_testrf)))\n",
    "print('RandomForest2 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred2)))\n",
    "print('RandomForest2 MSE: ', mean_absolute_error(y_testrf,rf_test_pred2))\n",
    "print('RandomForest2 r2: ',r2_score(y_testrf, rf_test_pred2))\n",
    "print('\\n')\n",
    "print('RandomForest3 MAD: ',np.mean(np.abs(rf_test_pred3 - y_testrf)))\n",
    "print('RandomForest3 RMSE: ',np.sqrt(mean_squared_error(y_testrf, rf_test_pred3)))\n",
    "print('RandomForest3 MSE: ', mean_absolute_error(y_testrf,rf_test_pred3))\n",
    "print('RandomForest3 r2: ',r2_score(y_testrf, rf_test_pred3))\n",
    "print('\\n')\n",
    "print('GradBoost MAD: ',np.mean(np.abs(pred_vals_gb1 - y_testrf)))\n",
    "print('GradBoost RMSE: ',np.sqrt(mean_squared_error(y_testrf, pred_vals_gb1)))\n",
    "print('GradBoost MSE: ', mean_absolute_error(y_testrf,pred_vals_gb1))\n",
    "print('GradBoost r2: ',r2_score(y_testrf, pred_vals_gb1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x11f57e4e0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KtsDyOiR76OOMsd0H1sGqY2W3lfnVBFAYH6uOR9xWzC36c23v0/wE8ntAK/AY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\n+H/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Vwv0ey1vZA/V1NjK1UJpOsavDzL5ddn50ZppkOlNS6tRq1vJ3r13nUx/bwWtnBINZ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Xn0xca089e6iB4aklTu3bmqTpdvjN9xOwSDudzgEAp9P5msPh+I8f6psfQhTrad84qQK8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\npukeAMXFXLbHsOiU+N1nuzBodfHjEkdp3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e/emOe/nwVXCBfPNmFpOYx5ZwhN9ZWoqSzD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\nxUB+ttvYLEdQpu+jKBFOdhkRiyfh9q+irWF9X78xsYzbE27YnSEc79QTE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PqTJyxAGrgy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KXku7imH2fxSxupqyM2fhcsTlYvsvQNa9ZnKS+z9MhVS62tYTFlfCOXnXsUcxe20bpqM81j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\nsBARERERERGRxFGDhYiIiIiIiIgkzv8HrsSCegYEzFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f4cd0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(pd.concat([X_train.iloc[:,0:5], y_train], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}