Part2-NN/Other_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax_new_dset = pd.read_csv('minmax.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divide the data to train , validation and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "TrainSize=0.1 #0.7\n",
    "validationsize = 0.1\n",
    "train=minmax_new_dset.sample(frac=TrainSize,random_state=200)\n",
    "test=minmax_new_dset.drop(train.index)\n",
    "validation = test.sample(frac=validationsize/(1.0-TrainSize),random_state=200)\n",
    "test=test.drop(validation.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((40663, 35), (40663, 35), (325304, 35))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, validation.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minutes_remaining</th>\n",
       "      <th>period</th>\n",
       "      <th>seconds_remaining</th>\n",
       "      <th>shot_distance</th>\n",
       "      <th>label</th>\n",
       "      <th>loc_x</th>\n",
       "      <th>loc_y</th>\n",
       "      <th>season</th>\n",
       "      <th>is_home</th>\n",
       "      <th>numpos</th>\n",
       "      <th>...</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Reverse</th>\n",
       "      <th>Fadeaway</th>\n",
       "      <th>Back</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Jump</th>\n",
       "      <th>Hook</th>\n",
       "      <th>Slam</th>\n",
       "      <th>Floating</th>\n",
       "      <th>Oop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.338983</td>\n",
       "      <td>0.034884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.066152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.068357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.034884</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.069460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.184123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.525424</td>\n",
       "      <td>0.232558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.273429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   minutes_remaining    period  seconds_remaining  shot_distance  label  \\\n",
       "0           0.166667  0.333333           0.338983       0.034884    1.0   \n",
       "1           0.500000  0.333333           0.694915       0.023256    1.0   \n",
       "2           0.500000  0.333333           0.016949       0.034884    0.0   \n",
       "3           0.333333  0.333333           0.745763       0.279070    0.0   \n",
       "4           0.166667  0.333333           0.525424       0.232558    0.0   \n",
       "\n",
       "   loc_x     loc_y  season  is_home  numpos ...   Bank  Reverse  Fadeaway  \\\n",
       "0  0.574  0.066152     0.0      0.0     0.0 ...    0.0      0.0       0.0   \n",
       "1  0.540  0.068357     0.0      1.0     0.0 ...    0.0      0.0       0.0   \n",
       "2  0.564  0.069460     0.0      1.0     0.0 ...    0.0      0.0       0.0   \n",
       "3  0.064  0.184123     0.0      1.0     0.0 ...    0.0      0.0       0.0   \n",
       "4  0.578  0.273429     0.0      1.0     0.0 ...    0.0      0.0       0.0   \n",
       "\n",
       "   Back  Alley  Jump  Hook  Slam  Floating  Oop  \n",
       "0   0.0    0.0   0.0   0.0   0.0       0.0  0.0  \n",
       "1   0.0    0.0   0.0   0.0   0.0       0.0  0.0  \n",
       "2   0.0    0.0   1.0   0.0   0.0       0.0  0.0  \n",
       "3   0.0    0.0   1.0   0.0   0.0       0.0  0.0  \n",
       "4   0.0    0.0   1.0   0.0   0.0       0.0  0.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmax_new_dset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tqdm.tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier as RFC\n",
    "from sklearn.ensemble import RandomForestRegressor as RFR\n",
    "from sklearn.metrics import roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RFC(n_jobs=3, n_estimators=500, max_depth=40)\n",
    "model.fit(train.drop('label', axis=1),train.label)\n",
    "predtrain= model.predict(train.drop('label', axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999932834818\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19707e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_true ,y_scores = train.label, predtrain\n",
    "fpr, tpr, _ = roc_curve(y_true.ravel(), y_scores.ravel())\n",
    "AUC=(roc_auc_score(y_true, y_scores))\n",
    "plt.xlim([-0.02,1.01])\n",
    "plt.ylim([-0.01,1.02])\n",
    "ax =plt.plot(fpr, tpr)\n",
    "#leg = zip(alphas,AUC)\n",
    "plt.title('ROC curves of different alphas')\n",
    "#l = plt.legend(map(lambda x: 'alpha: '+str(x[0])+' AUC:'+str(round(x[1],3)),leg), loc = 2)\n",
    "print AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.619088475441\n"
     ]
    },
    {
     "data": {
      "image/png": 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AF5FBq21sY8feSnYeqKKlrZPEhFjW503jnuXTGJeeFOryok6vgW6M8QA/BBYBbcDD1tqT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\nCKFAFxGJEAp0EZEIoUAXEYkQ/x/deuNycvAFZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19b116d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predval= model.predict(validation.drop('label', axis=1))\n",
    "y_true ,y_scores = validation.label, predval\n",
    "fpr, tpr, _ = roc_curve(y_true.ravel(), y_scores.ravel())\n",
    "AUC=(roc_auc_score(y_true, y_scores))\n",
    "plt.xlim([-0.02,1.01])\n",
    "plt.ylim([-0.01,1.02])\n",
    "ax =plt.plot(fpr, tpr)\n",
    "#leg = zip(alphas,AUC)\n",
    "plt.title('ROC curves of different alphas')\n",
    "#l = plt.legend(map(lambda x: 'alpha: '+str(x[0])+' AUC:'+str(round(x[1],3)),leg), loc = 2)\n",
    "print AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.14773652258643485, 'shot_distance'),\n",
       " (0.14712529199207564, 'seconds_remaining'),\n",
       " (0.13843102830120879, 'angle'),\n",
       " (0.11143172649736428, 'loc_x'),\n",
       " (0.10220667237872853, 'loc_y'),\n",
       " (0.09198298372863975, 'minutes_remaining'),\n",
       " (0.047685911339429767, 'numpos'),\n",
       " (0.047196249030635851, 'period'),\n",
       " (0.026430841282935384, 'Dunk'),\n",
       " (0.02074972140943785, 'is_home')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetimp = sorted(zip(list(model.feature_importances_),train.columns.drop('label')),reverse = True)\n",
    "fetimp[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4trbgAZCfX9iQvB217Oysdj2PoLWVb/3W/Tz62jrWbd1PWmoKZ005hgtPHU5W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\nKICIiEhSFEBERCQp/x+5E5oBEKwSiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b462eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from paretochart import pareto\n",
    "plt.title('Features Pareto', fontsize=18)\n",
    "par = pareto(model.feature_importances_,train.columns.drop('label'), limit = 0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.svm as svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = svm.SVC()\n",
    "clf.fit(train.drop('label', axis=1), train.label) \n",
    "preds= clf.predict(train.drop('label', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.622735658243\n"
     ]
    },
    {
     "data": {
      "image/png": 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FHAW6iAzY5fpWtu2p4JUDZ2lpC5CU6GP1wnzWLC5gTIx3PPRSr4FujPEBPwDmAW3AQ9ba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\nzXRumJbrdUlhIda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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba3a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_true ,y_scores = train.label, preds\n",
    "fpr, tpr, _ = roc_curve(y_true.ravel(), y_scores.ravel())\n",
    "AUC=(roc_auc_score(y_true, y_scores))\n",
    "plt.xlim([-0.02,1.01])\n",
    "plt.ylim([-0.01,1.02])\n",
    "ax =plt.plot(fpr, tpr)\n",
    "#leg = zip(alphas,AUC)\n",
    "plt.title('ROC curves of different alphas')\n",
    "#l = plt.legend(map(lambda x: 'alpha: '+str(x[0])+' AUC:'+str(round(x[1],3)),leg), loc = 2)\n",
    "print AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.616149679371\n"
     ]
    },
    {
     "data": {
      "image/png": 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UVNT2suzQ8tudJzn2wWVWLZjCDTPHjthxgJH9PGinMdAYwEfHoLfB3lOgvwis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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc9a6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predval= clf.predict(validation.drop('label', axis=1))\n",
    "y_true ,y_scores = validation.label, predval\n",
    "fpr, tpr, _ = roc_curve(y_true.ravel(), y_scores.ravel())\n",
    "AUC=(roc_auc_score(y_true, y_scores))\n",
    "plt.xlim([-0.02,1.01])\n",
    "plt.ylim([-0.01,1.02])\n",
    "ax =plt.plot(fpr, tpr)\n",
    "#leg = zip(alphas,AUC)\n",
    "plt.title('ROC curves of different alphas')\n",
    "#l = plt.legend(map(lambda x: 'alpha: '+str(x[0])+' AUC:'+str(round(x[1],3)),leg), loc = 2)\n",
    "print AUC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minutes_remaining</th>\n",
       "      <th>period</th>\n",
       "      <th>seconds_remaining</th>\n",
       "      <th>shot_distance</th>\n",
       "      <th>label</th>\n",
       "      <th>loc_x</th>\n",
       "      <th>loc_y</th>\n",
       "      <th>season</th>\n",
       "      <th>is_home</th>\n",
       "      <th>numpos</th>\n",
       "      <th>...</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Reverse</th>\n",
       "      <th>Fadeaway</th>\n",
       "      <th>Back</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Jump</th>\n",
       "      <th>Hook</th>\n",
       "      <th>Slam</th>\n",
       "      <th>Floating</th>\n",
       "      <th>Oop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "      <td>40663.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.447370</td>\n",
       "      <td>0.485913</td>\n",
       "      <td>0.487614</td>\n",
       "      <td>0.143143</td>\n",
       "      <td>0.450778</td>\n",
       "      <td>0.498159</td>\n",
       "      <td>0.145077</td>\n",
       "      <td>0.511325</td>\n",
       "      <td>0.500209</td>\n",
       "      <td>0.432111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019182</td>\n",
       "      <td>0.018272</td>\n",
       "      <td>0.021494</td>\n",
       "      <td>0.019797</td>\n",
       "      <td>0.011239</td>\n",
       "      <td>0.649165</td>\n",
       "      <td>0.036151</td>\n",
       "      <td>0.006738</td>\n",
       "      <td>0.027839</td>\n",
       "      <td>0.011239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.288079</td>\n",
       "      <td>0.371546</td>\n",
       "      <td>0.295789</td>\n",
       "      <td>0.115511</td>\n",
       "      <td>0.497577</td>\n",
       "      <td>0.218554</td>\n",
       "      <td>0.097396</td>\n",
       "      <td>0.499878</td>\n",
       "      <td>0.500006</td>\n",
       "      <td>0.389428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137166</td>\n",
       "      <td>0.133935</td>\n",
       "      <td>0.145025</td>\n",
       "      <td>0.139303</td>\n",
       "      <td>0.105417</td>\n",
       "      <td>0.477237</td>\n",
       "      <td>0.186668</td>\n",
       "      <td>0.081811</td>\n",
       "      <td>0.164512</td>\n",
       "      <td>0.105417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237288</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.396000</td>\n",
       "      <td>0.063947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.101433</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.267442</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.226020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.941860</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.944873</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       minutes_remaining        period  seconds_remaining  shot_distance  \\\n",
       "count       40663.000000  40663.000000       40663.000000   40663.000000   \n",
       "mean            0.447370      0.485913           0.487614       0.143143   \n",
       "std             0.288079      0.371546           0.295789       0.115511   \n",
       "min             0.000000      0.000000           0.000000       0.000000   \n",
       "25%             0.166667      0.000000           0.237288       0.023256   \n",
       "50%             0.416667      0.333333           0.491525       0.139535   \n",
       "75%             0.666667      0.666667           0.745763       0.267442   \n",
       "max             1.000000      1.000000           1.000000       0.941860   \n",
       "\n",
       "              label         loc_x         loc_y        season       is_home  \\\n",
       "count  40663.000000  40663.000000  40663.000000  40663.000000  40663.000000   \n",
       "mean       0.450778      0.498159      0.145077      0.511325      0.500209   \n",
       "std        0.497577      0.218554      0.097396      0.499878      0.500006   \n",
       "min        0.000000      0.000000      0.008820      0.000000      0.000000   \n",
       "25%        0.000000      0.396000      0.063947      0.000000      0.000000   \n",
       "50%        0.000000      0.500000      0.101433      1.000000      1.000000   \n",
       "75%        1.000000      0.600000      0.226020      1.000000      1.000000   \n",
       "max        1.000000      1.000000      0.944873      1.000000      1.000000   \n",
       "\n",
       "             numpos      ...               Bank       Reverse      Fadeaway  \\\n",
       "count  40663.000000      ...       40663.000000  40663.000000  40663.000000   \n",
       "mean       0.432111      ...           0.019182      0.018272      0.021494   \n",
       "std        0.389428      ...           0.137166      0.133935      0.145025   \n",
       "min        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%        0.000000      ...           0.000000      0.000000      0.000000   \n",
       "50%        0.714286      ...           0.000000      0.000000      0.000000   \n",
       "75%        0.714286      ...           0.000000      0.000000      0.000000   \n",
       "max        1.000000      ...           1.000000      1.000000      1.000000   \n",
       "\n",
       "               Back         Alley          Jump          Hook          Slam  \\\n",
       "count  40663.000000  40663.000000  40663.000000  40663.000000  40663.000000   \n",
       "mean       0.019797      0.011239      0.649165      0.036151      0.006738   \n",
       "std        0.139303      0.105417      0.477237      0.186668      0.081811   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      1.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      1.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "           Floating           Oop  \n",
       "count  40663.000000  40663.000000  \n",
       "mean       0.027839      0.011239  \n",
       "std        0.164512      0.105417  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max        1.000000      1.000000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "minmax_new_dset\n",
    "subset = minmax_new_dset[[u'minutes_remaining', u'period', u'seconds_remaining', u'shot_distance', u'loc_x', u'loc_y', u'season', u'is_home', u'numpos',\n",
    "       u'angle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.22323649  0.22304533]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(subset)\n",
    "PCA(copy=True, n_components=2, whiten=False)\n",
    "print(pca.explained_variance_ratio_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new = pca.transform(subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = minmax_new_dset.label.apply(lambda x: 'r' if x==0 else 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x65867cf8>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x81b7cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(new[:,0],new[:,1],c=cols)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}