data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Set styling parameters\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "from poibin import PoiBin\n",
    "import pytest\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import ML models \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases = pd.read_csv('SCDB_2018_01_caseCentered_Citation.csv', encoding = 'ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>caseId</th>\n",
       "      <th>docketId</th>\n",
       "      <th>caseIssuesId</th>\n",
       "      <th>voteId</th>\n",
       "      <th>dateDecision</th>\n",
       "      <th>decisionType</th>\n",
       "      <th>usCite</th>\n",
       "      <th>sctCite</th>\n",
       "      <th>ledCite</th>\n",
       "      <th>lexisCite</th>\n",
       "      <th>...</th>\n",
       "      <th>authorityDecision1</th>\n",
       "      <th>authorityDecision2</th>\n",
       "      <th>lawType</th>\n",
       "      <th>lawSupp</th>\n",
       "      <th>lawMinor</th>\n",
       "      <th>majOpinWriter</th>\n",
       "      <th>majOpinAssigner</th>\n",
       "      <th>splitVote</th>\n",
       "      <th>majVotes</th>\n",
       "      <th>minVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>1946-001-01</td>\n",
       "      <td>1946-001-01-01</td>\n",
       "      <td>1946-001-01-01-01</td>\n",
       "      <td>11/18/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 1</td>\n",
       "      <td>67 S. Ct. 6</td>\n",
       "      <td>91 L. Ed. 3</td>\n",
       "      <td>1946 U.S. LEXIS 1724</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>35 U.S.C. § 33</td>\n",
       "      <td>78.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1946-002</td>\n",
       "      <td>1946-002-01</td>\n",
       "      <td>1946-002-01-01</td>\n",
       "      <td>1946-002-01-01-01</td>\n",
       "      <td>11/18/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 14</td>\n",
       "      <td>67 S. Ct. 13</td>\n",
       "      <td>91 L. Ed. 12</td>\n",
       "      <td>1946 U.S. LEXIS 1725</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>18 U.S.C. § 398</td>\n",
       "      <td>81.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1946-003</td>\n",
       "      <td>1946-003-01</td>\n",
       "      <td>1946-003-01-01</td>\n",
       "      <td>1946-003-01-01-01</td>\n",
       "      <td>11/18/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 29</td>\n",
       "      <td>67 S. Ct. 1</td>\n",
       "      <td>91 L. Ed. 22</td>\n",
       "      <td>1946 U.S. LEXIS 3037</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1946-004</td>\n",
       "      <td>1946-004-01</td>\n",
       "      <td>1946-004-01-01</td>\n",
       "      <td>1946-004-01-01-01</td>\n",
       "      <td>11/25/1946</td>\n",
       "      <td>7</td>\n",
       "      <td>329 U.S. 40</td>\n",
       "      <td>67 S. Ct. 167</td>\n",
       "      <td>91 L. Ed. 29</td>\n",
       "      <td>1946 U.S. LEXIS 1696</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>49 Stat. 801</td>\n",
       "      <td>87.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1946-005</td>\n",
       "      <td>1946-005-01</td>\n",
       "      <td>1946-005-01-01</td>\n",
       "      <td>1946-005-01-01-01</td>\n",
       "      <td>11/25/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 64</td>\n",
       "      <td>67 S. Ct. 154</td>\n",
       "      <td>91 L. Ed. 44</td>\n",
       "      <td>1946 U.S. LEXIS 2997</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     caseId     docketId    caseIssuesId             voteId dateDecision  \\\n",
       "0  1946-001  1946-001-01  1946-001-01-01  1946-001-01-01-01   11/18/1946   \n",
       "1  1946-002  1946-002-01  1946-002-01-01  1946-002-01-01-01   11/18/1946   \n",
       "2  1946-003  1946-003-01  1946-003-01-01  1946-003-01-01-01   11/18/1946   \n",
       "3  1946-004  1946-004-01  1946-004-01-01  1946-004-01-01-01   11/25/1946   \n",
       "4  1946-005  1946-005-01  1946-005-01-01  1946-005-01-01-01   11/25/1946   \n",
       "\n",
       "   decisionType       usCite        sctCite       ledCite  \\\n",
       "0             1   329 U.S. 1    67 S. Ct. 6   91 L. Ed. 3   \n",
       "1             1  329 U.S. 14   67 S. Ct. 13  91 L. Ed. 12   \n",
       "2             1  329 U.S. 29    67 S. Ct. 1  91 L. Ed. 22   \n",
       "3             7  329 U.S. 40  67 S. Ct. 167  91 L. Ed. 29   \n",
       "4             1  329 U.S. 64  67 S. Ct. 154  91 L. Ed. 44   \n",
       "\n",
       "              lexisCite    ...     authorityDecision1  authorityDecision2  \\\n",
       "0  1946 U.S. LEXIS 1724    ...                    4.0                 NaN   \n",
       "1  1946 U.S. LEXIS 1725    ...                    4.0                 NaN   \n",
       "2  1946 U.S. LEXIS 3037    ...                    1.0                 NaN   \n",
       "3  1946 U.S. LEXIS 1696    ...                    4.0                 NaN   \n",
       "4  1946 U.S. LEXIS 2997    ...                    7.0                 NaN   \n",
       "\n",
       "  lawType lawSupp         lawMinor majOpinWriter majOpinAssigner  splitVote  \\\n",
       "0     6.0   600.0   35 U.S.C. § 33          78.0            78.0          1   \n",
       "1     6.0   600.0  18 U.S.C. § 398          81.0            87.0          1   \n",
       "2     2.0   207.0              NaN          84.0            78.0          1   \n",
       "3     6.0   600.0     49 Stat. 801          87.0            87.0          1   \n",
       "4     NaN     NaN              NaN          78.0            87.0          1   \n",
       "\n",
       "   majVotes  minVotes  \n",
       "0         8         1  \n",
       "1         6         3  \n",
       "2         5         4  \n",
       "3         5         3  \n",
       "4         6         3  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "og_data = pd.read_csv('SCDB_2018_01_justiceCentered_Citation.csv', encoding = 'ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>caseId</th>\n",
       "      <th>docketId</th>\n",
       "      <th>caseIssuesId</th>\n",
       "      <th>voteId</th>\n",
       "      <th>dateDecision</th>\n",
       "      <th>decisionType</th>\n",
       "      <th>usCite</th>\n",
       "      <th>sctCite</th>\n",
       "      <th>ledCite</th>\n",
       "      <th>lexisCite</th>\n",
       "      <th>...</th>\n",
       "      <th>majVotes</th>\n",
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       "      <th>justice</th>\n",
       "      <th>justiceName</th>\n",
       "      <th>vote</th>\n",
       "      <th>opinion</th>\n",
       "      <th>direction</th>\n",
       "      <th>majority</th>\n",
       "      <th>firstAgreement</th>\n",
       "      <th>secondAgreement</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>1946-001-01</td>\n",
       "      <td>1946-001-01-01</td>\n",
       "      <td>1946-001-01-01-01-01</td>\n",
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       "      <td>67 S. Ct. 6</td>\n",
       "      <td>91 L. Ed. 3</td>\n",
       "      <td>1946 U.S. LEXIS 1724</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>HHBurton</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>1946-001-01</td>\n",
       "      <td>1946-001-01-01</td>\n",
       "      <td>1946-001-01-01-01-02</td>\n",
       "      <td>11/18/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 1</td>\n",
       "      <td>67 S. Ct. 6</td>\n",
       "      <td>91 L. Ed. 3</td>\n",
       "      <td>1946 U.S. LEXIS 1724</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>RHJackson</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>1946-001-01</td>\n",
       "      <td>1946-001-01-01</td>\n",
       "      <td>1946-001-01-01-01-03</td>\n",
       "      <td>11/18/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 1</td>\n",
       "      <td>67 S. Ct. 6</td>\n",
       "      <td>91 L. Ed. 3</td>\n",
       "      <td>1946 U.S. LEXIS 1724</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>WODouglas</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>1946-001-01</td>\n",
       "      <td>1946-001-01-01</td>\n",
       "      <td>1946-001-01-01-01-04</td>\n",
       "      <td>11/18/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 1</td>\n",
       "      <td>67 S. Ct. 6</td>\n",
       "      <td>91 L. Ed. 3</td>\n",
       "      <td>1946 U.S. LEXIS 1724</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>FFrankfurter</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>1946-001-01</td>\n",
       "      <td>1946-001-01-01</td>\n",
       "      <td>1946-001-01-01-01-05</td>\n",
       "      <td>11/18/1946</td>\n",
       "      <td>1</td>\n",
       "      <td>329 U.S. 1</td>\n",
       "      <td>67 S. Ct. 6</td>\n",
       "      <td>91 L. Ed. 3</td>\n",
       "      <td>1946 U.S. LEXIS 1724</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>SFReed</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 61 columns</p>\n",
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      ],
      "text/plain": [
       "     caseId     docketId    caseIssuesId                voteId dateDecision  \\\n",
       "0  1946-001  1946-001-01  1946-001-01-01  1946-001-01-01-01-01   11/18/1946   \n",
       "1  1946-001  1946-001-01  1946-001-01-01  1946-001-01-01-01-02   11/18/1946   \n",
       "2  1946-001  1946-001-01  1946-001-01-01  1946-001-01-01-01-03   11/18/1946   \n",
       "3  1946-001  1946-001-01  1946-001-01-01  1946-001-01-01-01-04   11/18/1946   \n",
       "4  1946-001  1946-001-01  1946-001-01-01  1946-001-01-01-01-05   11/18/1946   \n",
       "\n",
       "   decisionType      usCite      sctCite      ledCite             lexisCite  \\\n",
       "0             1  329 U.S. 1  67 S. Ct. 6  91 L. Ed. 3  1946 U.S. LEXIS 1724   \n",
       "1             1  329 U.S. 1  67 S. Ct. 6  91 L. Ed. 3  1946 U.S. LEXIS 1724   \n",
       "2             1  329 U.S. 1  67 S. Ct. 6  91 L. Ed. 3  1946 U.S. LEXIS 1724   \n",
       "3             1  329 U.S. 1  67 S. Ct. 6  91 L. Ed. 3  1946 U.S. LEXIS 1724   \n",
       "4             1  329 U.S. 1  67 S. Ct. 6  91 L. Ed. 3  1946 U.S. LEXIS 1724   \n",
       "\n",
       "        ...         majVotes  minVotes justice   justiceName vote opinion  \\\n",
       "0       ...                8         1      86      HHBurton  2.0     1.0   \n",
       "1       ...                8         1      84     RHJackson  1.0     1.0   \n",
       "2       ...                8         1      81     WODouglas  1.0     1.0   \n",
       "3       ...                8         1      80  FFrankfurter  4.0     2.0   \n",
       "4       ...                8         1      79        SFReed  1.0     1.0   \n",
       "\n",
       "  direction  majority  firstAgreement  secondAgreement  \n",
       "0       1.0       1.0             NaN              NaN  \n",
       "1       2.0       2.0             NaN              NaN  \n",
       "2       2.0       2.0             NaN              NaN  \n",
       "3       2.0       2.0             NaN              NaN  \n",
       "4       2.0       2.0             NaN              NaN  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['caseId', 'docketId', 'caseIssuesId', 'voteId', 'dateDecision',\n",
       "       'decisionType', 'usCite', 'sctCite', 'ledCite', 'lexisCite', 'term',\n",
       "       'naturalCourt', 'chief', 'docket', 'caseName', 'dateArgument',\n",
       "       'dateRearg', 'petitioner', 'petitionerState', 'respondent',\n",
       "       'respondentState', 'jurisdiction', 'adminAction', 'adminActionState',\n",
       "       'threeJudgeFdc', 'caseOrigin', 'caseOriginState', 'caseSource',\n",
       "       'caseSourceState', 'lcDisagreement', 'certReason', 'lcDisposition',\n",
       "       'lcDispositionDirection', 'declarationUncon', 'caseDisposition',\n",
       "       'caseDispositionUnusual', 'partyWinning', 'precedentAlteration',\n",
       "       'voteUnclear', 'issue', 'issueArea', 'decisionDirection',\n",
       "       'decisionDirectionDissent', 'authorityDecision1', 'authorityDecision2',\n",
       "       'lawType', 'lawSupp', 'lawMinor', 'majOpinWriter', 'majOpinAssigner',\n",
       "       'splitVote', 'majVotes', 'minVotes', 'justice', 'justiceName', 'vote',\n",
       "       'opinion', 'direction', 'majority', 'firstAgreement',\n",
       "       'secondAgreement'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data = og_data.drop(columns = ['justice', 'docketId', 'caseIssuesId', 'voteId', 'dateDecision',\n",
    "                                        'usCite', 'sctCite', 'ledCite', 'lexisCite', \n",
    "                                        'docket', 'caseName', 'petitionerState', 'respondentState', \n",
    "                                        'adminActionState', 'caseOriginState',\n",
    "                                        'caseSourceState', 'declarationUncon', \n",
    "                                        'caseDispositionUnusual', 'partyWinning', 'voteUnclear',\n",
    "                                        'decisionDirectionDissent', 'authorityDecision1', 'authorityDecision2',\n",
    "                                        'lawMinor', 'majOpinWriter', 'majOpinAssigner',\n",
    "                                        'splitVote','firstAgreement', 'secondAgreement', \n",
    "                                        'dateArgument', 'dateRearg',\n",
    "                                        'term', 'caseDisposition', 'decisionDirection',\n",
    "                                        'majVotes', 'minVotes', 'majority', 'vote', 'opinion', \n",
    "                                       'precedentAlteration', 'issueArea'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['caseId', 'decisionType', 'naturalCourt', 'chief', 'petitioner',\n",
       "       'respondent', 'jurisdiction', 'adminAction', 'threeJudgeFdc',\n",
       "       'caseOrigin', 'caseSource', 'lcDisagreement', 'certReason',\n",
       "       'lcDisposition', 'lcDispositionDirection', 'issue', 'lawType',\n",
       "       'lawSupp', 'justiceName', 'direction'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data = working_data.dropna(subset=['direction'])\n",
    "working_data = working_data.fillna(int(999))\n",
    "d = {1: 0, 2: 1}\n",
    "working_data['direction'] = working_data['direction'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decisionType',\n",
       " 'naturalCourt',\n",
       " 'chief',\n",
       " 'petitioner',\n",
       " 'respondent',\n",
       " 'jurisdiction',\n",
       " 'adminAction',\n",
       " 'threeJudgeFdc',\n",
       " 'caseOrigin',\n",
       " 'caseSource',\n",
       " 'lcDisagreement',\n",
       " 'certReason',\n",
       " 'lcDisposition',\n",
       " 'lcDispositionDirection',\n",
       " 'issue',\n",
       " 'lawType',\n",
       " 'lawSupp']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_to_dummy = ['caseId', 'justiceName', 'direction']\n",
    "wd_columns_to_dummy = list(working_data.columns)\n",
    "for n in not_to_dummy:\n",
    "    wd_columns_to_dummy.remove(n)\n",
    "wd_columns_to_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_data = pd.get_dummies(working_data, columns = wd_columns_to_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>caseId</th>\n",
       "      <th>justiceName</th>\n",
       "      <th>direction</th>\n",
       "      <th>decisionType_1</th>\n",
       "      <th>decisionType_2</th>\n",
       "      <th>decisionType_4</th>\n",
       "      <th>decisionType_6</th>\n",
       "      <th>decisionType_7</th>\n",
       "      <th>naturalCourt_1301</th>\n",
       "      <th>naturalCourt_1303</th>\n",
       "      <th>...</th>\n",
       "      <th>lawSupp_507.0</th>\n",
       "      <th>lawSupp_508.0</th>\n",
       "      <th>lawSupp_509.0</th>\n",
       "      <th>lawSupp_510.0</th>\n",
       "      <th>lawSupp_511.0</th>\n",
       "      <th>lawSupp_512.0</th>\n",
       "      <th>lawSupp_600.0</th>\n",
       "      <th>lawSupp_800.0</th>\n",
       "      <th>lawSupp_900.0</th>\n",
       "      <th>lawSupp_999.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>HHBurton</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>RHJackson</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>WODouglas</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>FFrankfurter</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1946-001</td>\n",
       "      <td>SFReed</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1399 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     caseId   justiceName  direction  decisionType_1  decisionType_2  \\\n",
       "0  1946-001      HHBurton          0               1               0   \n",
       "1  1946-001     RHJackson          1               1               0   \n",
       "2  1946-001     WODouglas          1               1               0   \n",
       "3  1946-001  FFrankfurter          1               1               0   \n",
       "4  1946-001        SFReed          1               1               0   \n",
       "\n",
       "   decisionType_4  decisionType_6  decisionType_7  naturalCourt_1301  \\\n",
       "0               0               0               0                  1   \n",
       "1               0               0               0                  1   \n",
       "2               0               0               0                  1   \n",
       "3               0               0               0                  1   \n",
       "4               0               0               0                  1   \n",
       "\n",
       "   naturalCourt_1303      ...        lawSupp_507.0  lawSupp_508.0  \\\n",
       "0                  0      ...                    0              0   \n",
       "1                  0      ...                    0              0   \n",
       "2                  0      ...                    0              0   \n",
       "3                  0      ...                    0              0   \n",
       "4                  0      ...                    0              0   \n",
       "\n",
       "   lawSupp_509.0  lawSupp_510.0  lawSupp_511.0  lawSupp_512.0  lawSupp_600.0  \\\n",
       "0              0              0              0              0              1   \n",
       "1              0              0              0              0              1   \n",
       "2              0              0              0              0              1   \n",
       "3              0              0              0              0              1   \n",
       "4              0              0              0              0              1   \n",
       "\n",
       "   lawSupp_800.0  lawSupp_900.0  lawSupp_999.0  \n",
       "0              0              0              0  \n",
       "1              0              0              0  \n",
       "2              0              0              0  \n",
       "3              0              0              0  \n",
       "4              0              0              0  \n",
       "\n",
       "[5 rows x 1399 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WJBrennan       5087\n",
       "BRWhite         4701\n",
       "WHRehnquist     4300\n",
       "JPStevens       4019\n",
       "WODouglas       3707\n",
       "HABlackmun      3561\n",
       "TMarshall       3537\n",
       "PStewart        3429\n",
       "HLBlack         3151\n",
       "SDOConnor       2798\n",
       "AScalia         2773\n",
       "AMKennedy       2736\n",
       "WEBurger        2692\n",
       "LFPowell        2383\n",
       "CThomas         2246\n",
       "JHarlan2        2190\n",
       "TCClark         2132\n",
       "EWarren         2100\n",
       "RBGinsburg      2032\n",
       "SGBreyer        1916\n",
       "FFrankfurter    1721\n",
       "DHSouter        1690\n",
       "HHBurton        1327\n",
       "SFReed          1085\n",
       "JGRoberts        981\n",
       "SAAlito          942\n",
       "RHJackson        804\n",
       "FMVinson         762\n",
       "SSotomayor       668\n",
       "SMinton          658\n",
       "CEWhittaker      641\n",
       "EKagan           551\n",
       "AFortas          525\n",
       "AJGoldberg       440\n",
       "WBRutledge       368\n",
       "FMurphy          361\n",
       "NMGorsuch         82\n",
       "Name: justiceName, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.justiceName.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caseId               0\n",
       "justiceName          0\n",
       "direction            0\n",
       "decisionType_1       0\n",
       "decisionType_2       0\n",
       "decisionType_4       0\n",
       "decisionType_6       0\n",
       "decisionType_7       0\n",
       "naturalCourt_1301    0\n",
       "naturalCourt_1303    0\n",
       "naturalCourt_1401    0\n",
       "naturalCourt_1402    0\n",
       "naturalCourt_1403    0\n",
       "naturalCourt_1404    0\n",
       "naturalCourt_1405    0\n",
       "naturalCourt_1406    0\n",
       "naturalCourt_1407    0\n",
       "naturalCourt_1408    0\n",
       "naturalCourt_1409    0\n",
       "naturalCourt_1410    0\n",
       "naturalCourt_1411    0\n",
       "naturalCourt_1501    0\n",
       "naturalCourt_1502    0\n",
       "naturalCourt_1503    0\n",
       "naturalCourt_1504    0\n",
       "naturalCourt_1505    0\n",
       "naturalCourt_1506    0\n",
       "naturalCourt_1507    0\n",
       "naturalCourt_1601    0\n",
       "naturalCourt_1602    0\n",
       "                    ..\n",
       "lawSupp_377.0        0\n",
       "lawSupp_378.0        0\n",
       "lawSupp_379.0        0\n",
       "lawSupp_381.0        0\n",
       "lawSupp_382.0        0\n",
       "lawSupp_383.0        0\n",
       "lawSupp_384.0        0\n",
       "lawSupp_387.0        0\n",
       "lawSupp_388.0        0\n",
       "lawSupp_400.0        0\n",
       "lawSupp_401.0        0\n",
       "lawSupp_402.0        0\n",
       "lawSupp_403.0        0\n",
       "lawSupp_500.0        0\n",
       "lawSupp_501.0        0\n",
       "lawSupp_502.0        0\n",
       "lawSupp_503.0        0\n",
       "lawSupp_504.0        0\n",
       "lawSupp_505.0        0\n",
       "lawSupp_506.0        0\n",
       "lawSupp_507.0        0\n",
       "lawSupp_508.0        0\n",
       "lawSupp_509.0        0\n",
       "lawSupp_510.0        0\n",
       "lawSupp_511.0        0\n",
       "lawSupp_512.0        0\n",
       "lawSupp_600.0        0\n",
       "lawSupp_800.0        0\n",
       "lawSupp_900.0        0\n",
       "lawSupp_999.0        0\n",
       "Length: 1399, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "5        1\n",
       "6        1\n",
       "7        1\n",
       "8        1\n",
       "9        0\n",
       "10       1\n",
       "11       0\n",
       "12       0\n",
       "13       0\n",
       "14       1\n",
       "15       0\n",
       "16       1\n",
       "17       0\n",
       "18       0\n",
       "19       1\n",
       "20       0\n",
       "21       0\n",
       "22       0\n",
       "23       1\n",
       "24       1\n",
       "25       1\n",
       "26       1\n",
       "27       0\n",
       "29       1\n",
       "30       1\n",
       "        ..\n",
       "79581    0\n",
       "79582    0\n",
       "79583    0\n",
       "79584    0\n",
       "79585    1\n",
       "79586    1\n",
       "79588    1\n",
       "79589    1\n",
       "79590    1\n",
       "79591    1\n",
       "79592    1\n",
       "79593    1\n",
       "79594    1\n",
       "79595    1\n",
       "79596    1\n",
       "79597    1\n",
       "79598    1\n",
       "79599    1\n",
       "79600    1\n",
       "79601    1\n",
       "79602    1\n",
       "79603    0\n",
       "79604    0\n",
       "79605    0\n",
       "79606    0\n",
       "79607    1\n",
       "79608    0\n",
       "79609    0\n",
       "79610    0\n",
       "79611    0\n",
       "Name: direction, Length: 75096, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "working_data['direction']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling \n",
    "\n",
    "### Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just issue areas (for the sake of dimensionality, to see if there's any true value to keeping all those categories). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#no_issue_data = working_data.drop(columns = ['issue'])\n",
    "no_issue_data = working_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ginsburg_df = no_issue_data[no_issue_data['justiceName'] == 'RBGinsburg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if Ginsburg_df[Ginsburg_df['adminAction'].notnull()]:\n",
    "#     Ginsburg_df['lcDisposition'] = 4\n",
    "    \n",
    "Ginsburg_df = Ginsburg_df.dropna(subset=['direction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "case_ids = Ginsburg_df['caseId']\n",
    "Ginsburg_df = Ginsburg_df.drop(columns = ['caseId', 'justiceName'])\n",
    "# df = df[pd.notnull(df['EPS'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ginsburg_target = Ginsburg_df['direction']\n",
    "Ginsburg_df = Ginsburg_df.drop(columns = ['direction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train, data_test, target_train, target_test, case_train, case_test = train_test_split(Ginsburg_df, Ginsburg_target, case_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62697    1995-014\n",
       "72704    2008-010\n",
       "75807    2012-018\n",
       "69881    2004-012\n",
       "71589    2006-035\n",
       "68968    2002-074\n",
       "75654    2012-001\n",
       "73414    2009-006\n",
       "62886    1995-035\n",
       "65001    1997-085\n",
       "66702    1999-082\n",
       "71472    2006-022\n",
       "72019    2007-008\n",
       "62058    1994-035\n",
       "61744    1993-096\n",
       "68203    2001-074\n",
       "65784    1998-068\n",
       "75870    2012-025\n",
       "72695    2008-009\n",
       "76419    2013-007\n",
       "73334    2008-080\n",
       "68896    2002-065\n",
       "63939    1996-062\n",
       "63795    1996-046\n",
       "72091    2007-016\n",
       "66585    1999-069\n",
       "74286    2010-010\n",
       "72416    2007-052\n",
       "77526    2014-060\n",
       "73073    2008-051\n",
       "           ...   \n",
       "69692    2003-082\n",
       "75636    2011-077\n",
       "64083    1996-078\n",
       "67735    2001-021\n",
       "61797    1994-006\n",
       "69058    2002-084\n",
       "68770    2002-051\n",
       "64650    1997-046\n",
       "76473    2013-013\n",
       "78762    2016-063\n",
       "77112    2014-010\n",
       "69496    2003-060\n",
       "70600    2005-012\n",
       "70743    2005-028\n",
       "61546    1993-074\n",
       "65073    1997-093\n",
       "76140    2012-055\n",
       "69076    2003-001\n",
       "72884    2008-030\n",
       "62238    1994-055\n",
       "64524    1997-032\n",
       "75519    2011-064\n",
       "74161    2009-090\n",
       "68428    2002-013\n",
       "73783    2009-047\n",
       "61537    1993-073\n",
       "71680    2006-045\n",
       "72055    2007-012\n",
       "77319    2014-037\n",
       "64335    1997-009\n",
       "Name: caseId, Length: 508, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=30, max_features='auto', 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=200, n_jobs=1,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestClassifier(oob_score = True, n_estimators=200, max_depth = 30)\n",
    "forest.fit(data_train, target_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6587926509186351"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Training, score was 0.9330708661417323\n",
      "On Test, score was 0.6614173228346457\n"
     ]
    }
   ],
   "source": [
    "print(\"On Training, score was {}\".format(forest.score(data_train, target_train)))\n",
    "print(\"On Test, score was {}\".format(forest.score(data_test, target_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6884991438767183"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = forest.predict_proba(data_test)\n",
    "roc_auc_score(target_test, probs[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_series = pd.DataFrame(data = probs[:,1], index = case_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'direction'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3063\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3064\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'direction'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-2c101afdb0a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutcomes_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'direction'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworking_test_cases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2683\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2684\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2685\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2687\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2690\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2691\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2692\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2484\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2485\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2486\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2487\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2488\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   4113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4114\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4115\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4116\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3063\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3064\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3065\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3067\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'direction'"
     ]
    }
   ],
   "source": [
    "outcomes_series = pd.DataFrame(data = data_test['direction'].values, index = working_test_cases.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_with_ids = pd.concat([probs_series, case_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "probs_with_ids.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_with_ids.rename(columns={0:'probability'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_with_ids.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "justices = ['RBG', 'Alito', 'Souter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_probas = pd.DataFrame(columns = justices, index = case_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_probas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ind, row in probs_with_ids.iterrows():\n",
    "    case = row['caseId']\n",
    "    probabil = row['probability']\n",
    "    master_probas['RBG'].loc[case] = probabil "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_probas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_probas = master_probas.fillna(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_probas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = dict.fromkeys(list(master_probas.index.values), 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ind, row in master_probas.iterrows():\n",
    "    lista = []\n",
    "    for c in master_probas.columns:\n",
    "        if row[c] != 2:\n",
    "            lista.append(row[c])\n",
    "    lista = lista + [0.7, 0.5, 0.66, 0.44, 0.9, 0.83, 0.7, 0.71]\n",
    "    ps[ind] = lista "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcomes = {}\n",
    "x = [5, 6, 7, 8, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in ps.keys():\n",
    "    if len(ps[k]) == 9:\n",
    "        pb = PoiBin(ps[k])\n",
    "        outcomes[k] = sum(pb.pmf(x))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cnf_matrix = confusion_matrix(forest.predict(data_test), target_test)\n",
    "print('Confusion Matrix:\\n',cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(cnf_matrix,  cmap=plt.cm.Blues) #Create the basic matrix.\n",
    "\n",
    "#Add title and Axis Labels\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "\n",
    "#Add appropriate Axis Scales\n",
    "class_names = ['conservative', 'liberal'] #Get class labels to add to matrix\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "\n",
    "#Add Labels to Each Cell\n",
    "thresh = cnf_matrix.max() / 2. #Used for text coloring below\n",
    "#Here we iterate through the confusion matrix and append labels to our visualization.\n",
    "for i, j in itertools.product(range(cnf_matrix.shape[0]), range(cnf_matrix.shape[1])):\n",
    "        plt.text(j, i, cnf_matrix[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cnf_matrix[i, j] > thresh else \"black\")\n",
    "\n",
    "#Add a Side Bar Legend Showing Colors\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(oob_score = True, n_estimators=1500, max_depth = 30)\n",
    "forest.fit(data_train, target_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using GridSearchCV to do hyperparameter tuning, note that this takes a while to run and will tax your system\n",
    "# a bit if you're running this on a laptop. \n",
    "\n",
    "# n_estimators = [300, 500, 700]\n",
    "# max_depth = [10,12,14,16]\n",
    "\n",
    "# param_grid_forest = dict(n_estimators=n_estimators, max_depth=max_depth)\n",
    "\n",
    "\n",
    "# kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)\n",
    "\n",
    "# grid_search = GridSearchCV(forest, param_grid_forest, scoring=\"accuracy\", n_jobs=-1, cv=kfold)\n",
    "# grid_result = grid_search.fit(data_train, target_train)\n",
    "\n",
    "# # Interpreting results \n",
    "# print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "\n",
    "# means = grid_result.cv_results_['mean_test_score']\n",
    "# stds = grid_result.cv_results_['std_test_score']\n",
    "# params = grid_result.cv_results_['params']\n",
    "\n",
    "# for mean, stdev, param in zip(means, stds, params):\n",
    "#     print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression(fit_intercept = False, C = 1e12)\n",
    "model_log = logreg.fit(data_train, target_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs = model_log.predict_proba(data_test)\n",
    "roc_auc_score(target_test, probs[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"On Training, score was {}\".format(model_log.score(data_train, target_train)))\n",
    "print(\"On Test, score was {}\".format(model_log.score(data_test, target_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(model_log.predict(data_test), target_test)\n",
    "print('Confusion Matrix:\\n',cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(cnf_matrix,  cmap=plt.cm.Blues) #Create the basic matrix.\n",
    "\n",
    "#Add title and Axis Labels\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "\n",
    "#Add appropriate Axis Scales\n",
    "class_names = ['conservative', 'liberal'] #Get class labels to add to matrix\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names, rotation=45)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "\n",
    "#Add Labels to Each Cell\n",
    "thresh = cnf_matrix.max() / 2. #Used for text coloring below\n",
    "#Here we iterate through the confusion matrix and append labels to our visualization.\n",
    "for i, j in itertools.product(range(cnf_matrix.shape[0]), range(cnf_matrix.shape[1])):\n",
    "        plt.text(j, i, cnf_matrix[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cnf_matrix[i, j] > thresh else \"black\")\n",
    "\n",
    "#Add a Side Bar Legend Showing Colors\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# eval_set = [(data_test, target_test)]\n",
    "# xgb_model = xgb.XGBClassifier()\n",
    "# xgb_model.fit(data_train, target_train, early_stopping_rounds=30, eval_metric=\"error\", eval_set=eval_set, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"On Training, score was {}\".format(xgb_model.score(data_train, target_train)))\n",
    "# print(\"On Test, score was {}\".format(xgb_model.score(data_test, target_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using GridSearchCV to do hyperparameter tuning, note that this takes a while to run and will tax your system\n",
    "# a bit if you're running this on a laptop. \n",
    "\n",
    "# learning_rate = [0.005, 0.01, 0.02]\n",
    "# n_estimators = [150]\n",
    "# max_depth = [2,4,6]\n",
    "\n",
    "# param_grid = dict(learning_rate=learning_rate, n_estimators=n_estimators, max_depth=max_depth)\n",
    "\n",
    "\n",
    "# kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)\n",
    "\n",
    "# grid_search = GridSearchCV(xgb_model, param_grid, scoring=\"accuracy\", n_jobs=-1, cv=kfold)\n",
    "# grid_result = grid_search.fit(data_train, target_train)\n",
    "\n",
    "# # Interpreting results \n",
    "# print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "\n",
    "# means = grid_result.cv_results_['mean_test_score']\n",
    "# stds = grid_result.cv_results_['std_test_score']\n",
    "# params = grid_result.cv_results_['params']\n",
    "\n",
    "# for mean, stdev, param in zip(means, stds, params):\n",
    "#     print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
    "nb = GaussianNB()\n",
    "nbmodel = nb.fit(data_train, target_train)\n",
    "nbpreds = nbmodel.predict(data_test)\n",
    "accuracy = accuracy_score(target_test, nbpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "sv = svm.SVC(kernel='rbf', probability = True, random_state = 124)\n",
    "svmmodel = sv.fit(data_train, target_train)\n",
    "svmpreds = svmmodel.predict(data_test)\n",
    "accuracy_score(target_test, svmpreds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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