build_scdb_model_10.ipynb

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    "Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "==================\n",
    "  *  __Title__: Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "  *  __Authors__: [Daniel Martin Katz](http://www.law.msu.edu/faculty_staff/profile.php?prof=780), [Michael J Bommarito II](http://bommaritollc.com/), [Josh Blackman](http://joshblackman.com)\n",
    "  *  __Paper URL__: [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244)\n",
    "  *  __Blog URL__: [http://lexpredict.com/portfolio/predicting-the-supreme-court/](http://lexpredict.com/portfolio/predicting-the-supreme-court/)\n",
    "\n",
    "## Paper Abstract\n",
    "Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized,and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).\n",
    "\n",
    "## Source Description\n",
    "The source and data in this repository allow for the reproduction of the results in this paper.  \n",
    "\n",
    "## Data Description\n",
    "The data used in this paper is available from the [Supreme Court Database (SCDB)](http://scdb.wustl.edu/).\n",
    "\n",
    "## Version\n",
    "The latest version of this model was relesed in October 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Imports\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.stats.proportion\n",
    "\n",
    "# seaborn\n",
    "import seaborn\n",
    "seaborn.set()\n",
    "seaborn.set_style(\"darkgrid\")\n",
    "\n",
    "# Project imports\n",
    "from model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get raw data\n",
    "raw_data = get_raw_scdb_data(\"data/input/SCDB_2015_01_justiceCentered_Citation.csv\")\n",
    "\n",
    "# Get feature data\n",
    "feature_df = preprocess_raw_data(raw_data, include_direction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77342, 82)\n",
      "(77342, 1365)\n"
     ]
    }
   ],
   "source": [
    "# Output some diagnostics on features\n",
    "print(raw_data.shape)\n",
    "print(feature_df.shape)\n",
    "assert(raw_data.shape[0] == feature_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    justice_outcome_disposition\n",
      " 1                        43951\n",
      " 0                        26293\n",
      "-1                         7098\n",
      "    justice_outcome_disposition\n",
      " 1                     0.568268\n",
      " 0                     0.339958\n",
      "-1                     0.091774\n"
     ]
    }
   ],
   "source": [
    "# Output basic quantities for sample\n",
    "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts()))\n",
    "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts(normalize=True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term: 1951\n",
      "Term: 1952\n",
      "Term: 1953\n",
      "Natural court change; rebuilding with 80 trees\n",
      "Term: 1954\n",
      "Natural court change; rebuilding with 90 trees\n",
      "Term: 1955\n",
      "Term: 1956\n",
      "Natural court change; rebuilding with 110 trees\n",
      "Term: 1957\n",
      "Natural court change; rebuilding with 120 trees\n",
      "Term: 1958\n",
      "Natural court change; rebuilding with 130 trees\n",
      "Term: 1959\n",
      "Natural court change; rebuilding with 140 trees\n",
      "Term: 1960\n",
      "Term: 1961\n",
      "Natural court change; rebuilding with 160 trees\n",
      "Term: 1962\n",
      "Natural court change; rebuilding with 170 trees\n",
      "Term: 1963\n",
      "Term: 1964\n",
      "Term: 1965\n",
      "Natural court change; rebuilding with 200 trees\n",
      "Term: 1966\n",
      "Term: 1967\n",
      "Natural court change; rebuilding with 220 trees\n",
      "Term: 1968\n",
      "Term: 1969\n",
      "Natural court change; rebuilding with 240 trees\n",
      "Term: 1970\n",
      "Term: 1971\n",
      "Natural court change; rebuilding with 260 trees\n",
      "Term: 1972\n",
      "Term: 1973\n",
      "Term: 1974\n",
      "Term: 1975\n",
      "Natural court change; rebuilding with 300 trees\n",
      "Term: 1976\n",
      "Natural court change; rebuilding with 310 trees\n",
      "Term: 1977\n",
      "Term: 1978\n",
      "Term: 1979\n",
      "Term: 1980\n",
      "Term: 1981\n",
      "Natural court change; rebuilding with 360 trees\n",
      "Term: 1982\n",
      "Term: 1983\n",
      "Term: 1984\n",
      "Term: 1985\n",
      "Term: 1986\n",
      "Natural court change; rebuilding with 410 trees\n",
      "Term: 1987\n",
      "Natural court change; rebuilding with 420 trees\n",
      "Term: 1988\n",
      "Term: 1989\n",
      "Term: 1990\n",
      "Natural court change; rebuilding with 450 trees\n",
      "Term: 1991\n",
      "Natural court change; rebuilding with 460 trees\n",
      "Term: 1992\n",
      "Term: 1993\n",
      "Natural court change; rebuilding with 480 trees\n",
      "Term: 1994\n",
      "Natural court change; rebuilding with 490 trees\n",
      "Term: 1995\n",
      "Term: 1996\n",
      "Term: 1997\n",
      "Term: 1998\n",
      "Term: 1999\n",
      "Term: 2000\n",
      "Term: 2001\n",
      "Term: 2002\n",
      "Term: 2003\n",
      "Term: 2004\n",
      "Term: 2005\n",
      "Natural court change; rebuilding with 600 trees\n",
      "Term: 2006\n",
      "Natural court change; rebuilding with 610 trees\n",
      "Term: 2007\n",
      "Term: 2008\n",
      "Term: 2009\n",
      "Natural court change; rebuilding with 640 trees\n",
      "Term: 2010\n",
      "Natural court change; rebuilding with 650 trees\n",
      "Term: 2011\n",
      "Term: 2012\n",
      "Term: 2013\n",
      "Term: 2014\n"
     ]
    }
   ],
   "source": [
    "# Setup training time period\n",
    "min_training_years = 5\n",
    "term_range = range(raw_data[\"term\"].min() + min_training_years,\n",
    "                   raw_data[\"term\"].max()+1)\n",
    "\n",
    "# Setting growing random forest parameters\n",
    "# Number of trees to grow per term\n",
    "trees_per_term = 10\n",
    "\n",
    "# Number of trees to begin with\n",
    "initial_trees = min_training_years * trees_per_term\n",
    "\n",
    "# Number of years between \"forest fires\"\n",
    "reset_interval = 9999\n",
    "\n",
    "# Setup model\n",
    "m = None\n",
    "term_count = 0\n",
    "\n",
    "for term in term_range:\n",
    "    # Diagnostic output\n",
    "    print(\"Term: {0}\".format(term))\n",
    "    term_count += 1\n",
    "    \n",
    "    # Setup train and test periods\n",
    "    train_index = (raw_data.loc[:, \"term\"] < term).values\n",
    "    test_index = (raw_data.loc[:, \"term\"] == term).values\n",
    "    \n",
    "    # Setup train data\n",
    "    feature_data_train = feature_df.loc[train_index, :]\n",
    "    target_data_train = raw_data.loc[train_index, \"justice_outcome_disposition\"].astype(int).values\n",
    "\n",
    "    # Setup test data\n",
    "    feature_data_test = feature_df.loc[test_index, :]\n",
    "    target_data_test = raw_data.loc[test_index, \"justice_outcome_disposition\"].astype(int).values\n",
    "    \n",
    "    # Check if we should rebuild the model based on changing natural court\n",
    "    if term_count % reset_interval == 0:\n",
    "        # \"Forest fire;\" grow a new forest from scratch\n",
    "        print(\"Reset interval hit; rebuilding with {0} trees\".format(initial_trees + (term_count * trees_per_term)))\n",
    "        m = None\n",
    "    else:\n",
    "        # Check if the justice set has changed\n",
    "        if set(raw_data.loc[raw_data.loc[:, \"term\"] == (term-1), \"justice\"].unique()) != \\\n",
    "            set(raw_data.loc[raw_data.loc[:, \"term\"] == (term), \"justice\"].unique()):\n",
    "            # natural Court change; trigger forest fire\n",
    "            print(\"Natural court change; rebuilding with {0} trees\".format(initial_trees + (term_count * trees_per_term)))\n",
    "        \n",
    "            m = None\n",
    "                                              \n",
    "    # Build or grow a model depending on initial/reset condition\n",
    "    if not m:\n",
    "        # Grow an initial forest\n",
    "        m = sklearn.ensemble.RandomForestClassifier(n_estimators=initial_trees + (term_count * trees_per_term), \n",
    "                                                    class_weight=\"balanced_subsample\",\n",
    "                                                    warm_start=True,\n",
    "                                                    n_jobs=-1)\n",
    "    else:\n",
    "        # Grow the forest by increasing the number of trees (requires warm_start=True)\n",
    "        m.set_params(n_estimators=initial_trees + (term_count * trees_per_term))\n",
    "\n",
    "    # Fit the forest model\n",
    "    m.fit(feature_data_train,\n",
    "          target_data_train)\n",
    "\n",
    "    # Fit the \"dummy\" model\n",
    "    d = sklearn.dummy.DummyClassifier(strategy=\"most_frequent\")\n",
    "    d.fit(feature_data_train, target_data_train)\n",
    "    \n",
    "    # Perform forest predictions\n",
    "    raw_data.loc[test_index, \"rf_predicted\"] = m.predict(feature_data_test)\n",
    "    \n",
    "    # Store scores per class\n",
    "    scores = m.predict_proba(feature_data_test)\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_other\"] = scores[:, 0]\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_affirm\"] = scores[:, 1]\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_reverse\"] = scores[:, 2]\n",
    "    \n",
    "    # Store dummy predictions\n",
    "    raw_data.loc[test_index, \"dummy_predicted\"] = d.predict(feature_data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.92      0.44      0.59      6700\n",
      "          0       0.60      0.34      0.44     24325\n",
      "          1       0.65      0.88      0.75     41142\n",
      "\n",
      "avg / total       0.66      0.65      0.63     72167\n",
      "\n",
      "[[ 2937   450  3313]\n",
      " [   98  8265 15962]\n",
      " [  174  4959 36009]]\n",
      "0.654190973714\n",
      "================================\n",
      "\n",
      "Dummy model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.00      0.00      0.00      6700\n",
      "          0       0.00      0.00      0.00     24325\n",
      "          1       0.57      1.00      0.73     41142\n",
      "\n",
      "avg / total       0.33      0.57      0.41     72167\n",
      "\n",
      "[[    0     0  6700]\n",
      " [    0     0 24325]\n",
      " [    0     0 41142]]\n",
      "0.57009436446\n",
      "================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation range\n",
    "evaluation_index = raw_data.loc[:, \"term\"].isin(term_range)\n",
    "target_actual = raw_data.loc[evaluation_index, \"justice_outcome_disposition\"]\n",
    "target_predicted = raw_data.loc[evaluation_index, \"rf_predicted\"]\n",
    "target_dummy = raw_data.loc[evaluation_index, \"dummy_predicted\"]\n",
    "raw_data.loc[:, \"rf_correct\"] = numpy.nan\n",
    "raw_data.loc[:, \"dummy_correct\"] = numpy.nan\n",
    "raw_data.loc[evaluation_index, \"rf_correct\"] = (target_actual == target_predicted).astype(float)\n",
    "raw_data.loc[evaluation_index, \"dummy_correct\"] = (target_actual == target_dummy).astype(float)\n",
    "\n",
    "# Compare model\n",
    "print(\"RF model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_predicted))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_predicted))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_predicted))\n",
    "print(\"=\"*32)\n",
    "print(\"\")\n",
    "\n",
    "# Dummy model\n",
    "print(\"Dummy model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_dummy))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_dummy))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_dummy))\n",
    "print(\"=\"*32)\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
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     "output_type": "execute_result"
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X9T1vdqwPm0URVZ5YUoFNFOBy2BDLNony5WRqASChlKdyK+hzotbvwrmhKBSV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\nvC8Ii8nSApNB7XJmNiYztUufGb5kYx0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jZTW/W4aAciycgqbpaK6bWiZpvset1n21aUXD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\nlb1X6LKll+rS7ovl2E6T7wwAAAAAIBFq5+X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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89385baf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_correct_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"rf_correct\"].mean()\n",
    "dummy_correct_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"dummy_correct\"].mean()\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.plot(rf_correct_ts.index, rf_correct_ts,\n",
    "         marker='o', alpha=0.75)\n",
    "plt.plot(dummy_correct_ts.index, dummy_correct_ts,\n",
    "         marker='>', alpha=0.75)\n",
    "plt.legend(('Random forest', 'Dummy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8939b8a290>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAABMEJgBAAAAADBBYAYAAAAAwASBGQAAAAAAEwRmAAAAAABMEJgBAAAAADBB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f893767d190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_spread_ts = rf_correct_ts - dummy_correct_ts\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.bar(rf_spread_ts.index, rf_spread_ts,\n",
    "        alpha=0.75)\n",
    "plt.xlabel(\"Term\")\n",
    "plt.ylabel(\"Spread (%)\")\n",
    "plt.title(\"Spread over dummy model for justice accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>decision_delay</td>\n",
       "      <td>0.031795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>justice_previous_lc_direction_diff</td>\n",
       "      <td>0.026804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>justice_cumulative_lc_direction_diff</td>\n",
       "      <td>0.026561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>justice_cumulative_action</td>\n",
       "      <td>0.015562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>justice_previous_action</td>\n",
       "      <td>0.015302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>justice_previous_agreement</td>\n",
       "      <td>0.015215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>justice_cumulative_agreement</td>\n",
       "      <td>0.015215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>justice_previous_court_direction_diff</td>\n",
       "      <td>0.014666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>justice_cumulative_direction</td>\n",
       "      <td>0.014516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>justice_previous_direction</td>\n",
       "      <td>0.014495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    feature  importance\n",
       "130                          decision_delay    0.031795\n",
       "1363     justice_previous_lc_direction_diff    0.026804\n",
       "1364   justice_cumulative_lc_direction_diff    0.026561\n",
       "1352              justice_cumulative_action    0.015562\n",
       "1351                justice_previous_action    0.015302\n",
       "1357             justice_previous_agreement    0.015215\n",
       "1358           justice_cumulative_agreement    0.015215\n",
       "1349  justice_previous_court_direction_diff    0.014666\n",
       "1346           justice_cumulative_direction    0.014516\n",
       "1345             justice_previous_direction    0.014495"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importance\n",
    "feature_importance_df = pandas.DataFrame(zip(feature_df.columns, m.feature_importances_),\n",
    "                                         columns=[\"feature\", \"importance\"])\n",
    "feature_importance_df.sort_values([\"importance\"], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
      "Ttest_relResult(statistic=14.97997134678951, pvalue=1.9468743762289205e-22)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=7.0387621369142845, pvalue=1.9395513815324922e-12)\n",
      "================\n",
      "Binomial:\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Output stats\n",
    "print(\"t-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ttest_rel(rf_correct_ts.values,\n",
    "                   dummy_correct_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"ranksum-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ranksums(rf_correct_ts.values,\n",
    "                   dummy_correct_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"Binomial:\")\n",
    "print(statsmodels.stats.proportion.binom_test(raw_data.loc[evaluation_index, \"rf_correct\"].sum(),\n",
    "                                              raw_data.loc[evaluation_index, \"rf_correct\"].shape[0],\n",
    "                                              raw_data.loc[evaluation_index, \"dummy_correct\"].mean(),\n",
    "                                              alternative=\"larger\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get case-level prediction\n",
    "#scdb_data.loc[evaluation_index, \"rf_predicted_case\"] = \n",
    "rf_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n",
    "    .groupby([\"docketId\"])[\"rf_predicted\"]\\\n",
    "    .agg(lambda x: x.value_counts().index[0]))\n",
    "rf_predicted_case.columns = [\"rf_predicted_case\"]\n",
    "\n",
    "dummy_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n",
    "    .groupby([\"docketId\"])[\"dummy_predicted\"]\\\n",
    "    .agg(lambda x: x.value_counts().index[0]))\n",
    "dummy_predicted_case.columns = [\"dummy_predicted_case\"]\n",
    "\n",
    "# Set DFs\n",
    "rf_predicted_case = raw_data[[\"docketId\", \"case_outcome_disposition\", \"rf_predicted\"]].join(rf_predicted_case, on=\"docketId\")\n",
    "dumy_predicted_case = raw_data[[\"docketId\", \"dummy_predicted\"]].join(dummy_predicted_case, on=\"docketId\")\n",
    "\n",
    "raw_data.loc[:, \"rf_predicted_case\"] = rf_predicted_case\n",
    "raw_data.loc[:, \"dummy_predicted_case\"] = dumy_predicted_case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1    5435\n",
      " 0    2639\n",
      "-1     433\n",
      "Name: case_outcome_disposition, dtype: int64\n",
      " 1    0.629707\n",
      " 0    0.305758\n",
      "-1    0.050168\n",
      "Name: case_outcome_disposition, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Output case distribution\n",
    "case_outcomes = raw_data.groupby([\"docketId\"])[\"case_outcome_disposition\"].agg(lambda x: x.mode())\n",
    "case_outcomes = case_outcomes.apply(lambda x: int(x) if type(x) in [numpy.float64] else None)\n",
    "print(case_outcomes.value_counts())\n",
    "print(case_outcomes.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.89      0.49      0.63      3676\n",
      "        0.0       0.54      0.20      0.29     21597\n",
      "        1.0       0.69      0.92      0.79     45782\n",
      "\n",
      "avg / total       0.65      0.68      0.63     71055\n",
      "\n",
      "[[ 1803    72  1801]\n",
      " [   63  4307 17227]\n",
      " [  153  3650 41979]]\n",
      "0.676785588629\n",
      "================================\n",
      "\n",
      "Dummy model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.00      0.00      0.00      3676\n",
      "        0.0       0.00      0.00      0.00     21597\n",
      "        1.0       0.64      1.00      0.78     45782\n",
      "\n",
      "avg / total       0.42      0.64      0.50     71055\n",
      "\n",
      "[[    0     0  3676]\n",
      " [    0     0 21597]\n",
      " [    0     0 45782]]\n",
      "0.644317782\n",
      "================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Output comparison\n",
    "# Evaluation range\n",
    "evaluation_index = raw_data.loc[:, \"term\"].isin(term_range) & -raw_data.loc[:, \"case_outcome_disposition\"].isnull()\n",
    "target_actual = raw_data.loc[evaluation_index, \"case_outcome_disposition\"]\n",
    "target_predicted = raw_data.loc[evaluation_index, \"rf_predicted_case\"]\n",
    "target_dummy = raw_data.loc[evaluation_index, \"dummy_predicted_case\"]\n",
    "\n",
    "raw_data.loc[:, \"rf_correct_case\"] = numpy.nan\n",
    "raw_data.loc[:, \"dummy_correct_case\"] = numpy.nan\n",
    "raw_data.loc[evaluation_index, \"rf_correct_case\"] = (target_actual == target_predicted).astype(float)\n",
    "raw_data.loc[evaluation_index, \"dummy_correct_case\"] = (target_actual == target_dummy).astype(float)\n",
    "\n",
    "# Compare model\n",
    "print(\"RF model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_predicted))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_predicted))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_predicted))\n",
    "print(\"=\"*32)\n",
    "print(\"\")\n",
    "\n",
    "# Dummy model\n",
    "print(\"Dummy model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_dummy))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_dummy))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_dummy))\n",
    "print(\"=\"*32)\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8937704050>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GrkRERERERFTTGLgSERERERFRTWPgSkRERERERDWNgSsRERERERHVNAauREREREREVNMY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\nI0tSYpSGJnmThU/kuiR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lSR/60If0/PPPS5K6u7v18ssvq7GxkbUPuaH+289kMkqn05Kk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f893a3fe090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_correct_case_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"rf_correct_case\"].mean()\n",
    "dummy_correct_case_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"dummy_correct_case\"].mean()\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.plot(rf_correct_case_ts.index, rf_correct_case_ts,\n",
    "         marker='o', alpha=0.75)\n",
    "plt.plot(dummy_correct_case_ts.index, dummy_correct_case_ts,\n",
    "         marker='>', alpha=0.75)\n",
    "plt.legend(('Random forest', 'Dummy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f893a5f1e50>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DAABACoEZAAAAUgjMAAAAkEJgBgAAgBQCMwAAAKQQmAEAACCFwAwAAAApBGYA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\nFAhmAAAAKBDMAAAAUPD/AF1riT1XfOBMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f893767ef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_spread_case_ts = rf_correct_case_ts - dummy_correct_case_ts\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.bar(rf_spread_case_ts.index, rf_spread_case_ts,\n",
    "        alpha=0.75)\n",
    "plt.xlabel(\"Term\")\n",
    "plt.ylabel(\"Spread (%)\")\n",
    "plt.title(\"Spread over dummy model for case accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8939b3c850>]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pwgrt3Vah57eUamouqJOdXgWXCVYkB2IVAAAgxc3/2ctImxor1bi5xOpJeASGYehAY6W+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\nAMCUUFaTVCAY0f4jHvUP+7VqaYnW1y4wOxIAAAAATBllNQm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8ggULbi3zm1nzHEhsU5n9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\nAAAg7lBWAQAAAABx5/8BfUZfKNC/oEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f893782bd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_spread_case_dir_ts = pandas.expanding_sum(numpy.sign(rf_spread_case_ts))\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.plot(rf_spread_case_dir_ts.index, rf_spread_case_dir_ts,\n",
    "        alpha=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
      "Ttest_relResult(statistic=5.8174990916271012, pvalue=2.1637432938563989e-07)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=2.4423599154831219, pvalue=0.014591591306004263)\n",
      "================\n",
      "Binomial:\n",
      "5.92494992988e-10\n"
     ]
    }
   ],
   "source": [
    "# Output stats\n",
    "print(\"t-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ttest_rel(rf_correct_case_ts.values,\n",
    "                   dummy_correct_case_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"ranksum-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ranksums(rf_correct_case_ts.values,\n",
    "                   dummy_correct_case_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"Binomial:\")\n",
    "case_accuracy_data = raw_data.loc[evaluation_index, [\"docketId\", \"rf_correct_case\", \"dummy_correct_case\"]].drop_duplicates()\n",
    "print(statsmodels.stats.proportion.binom_test(case_accuracy_data[\"rf_correct_case\"].sum(),\n",
    "                                              case_accuracy_data[\"rf_correct_case\"].shape[0],\n",
    "                                              case_accuracy_data[\"dummy_correct_case\"].mean(),\n",
    "                                              alternative=\"larger\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>22281</th>\n",
       "      <th>22282</th>\n",
       "      <th>22283</th>\n",
       "      <th>22284</th>\n",
       "      <th>22285</th>\n",
       "      <th>22286</th>\n",
       "      <th>22287</th>\n",
       "      <th>22288</th>\n",
       "      <th>22289</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caseName</th>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justiceName</th>\n",
       "      <td>JHarlan2</td>\n",
       "      <td>HLBlack</td>\n",
       "      <td>WODouglas</td>\n",
       "      <td>PStewart</td>\n",
       "      <td>WJBrennan</td>\n",
       "      <td>BRWhite</td>\n",
       "      <td>EWarren</td>\n",
       "      <td>TCClark</td>\n",
       "      <td>AFortas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case_outcome_disposition</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justice_outcome_disposition</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
       "      <td>0.535</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.445</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          22281               22282  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                            JHarlan2             HLBlack   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   0                   1   \n",
       "rf_predicted                                  0                   1   \n",
       "rf_predicted_score_affirm                 0.535               0.275   \n",
       "rf_predicted_score_reverse                0.445               0.665   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22283               22284  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                           WODouglas            PStewart   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   0   \n",
       "rf_predicted_score_affirm                   0.2               0.525   \n",
       "rf_predicted_score_reverse                 0.78                0.46   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22285               22286  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                           WJBrennan             BRWhite   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   1   \n",
       "rf_predicted_score_affirm                 0.335                0.48   \n",
       "rf_predicted_score_reverse                 0.64                0.49   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22287               22288  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                             EWarren             TCClark   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   1   \n",
       "rf_predicted_score_affirm                 0.185                0.44   \n",
       "rf_predicted_score_reverse                0.755               0.535   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22289  \n",
       "caseName                     MIRANDA v. ARIZONA  \n",
       "justiceName                             AFortas  \n",
       "case_outcome_disposition                      1  \n",
       "justice_outcome_disposition                   1  \n",
       "rf_predicted                                  1  \n",
       "rf_predicted_score_affirm                  0.24  \n",
       "rf_predicted_score_reverse                 0.65  \n",
       "rf_correct_case                               1  \n",
       "dummy_correct_case                            1  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"MIRANDA v. ARIZONA\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>caseName</th>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
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       "                                            77288                 77289  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                             JGRoberts               AScalia   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     0                     0   \n",
       "rf_predicted                                    1                     1   \n",
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       "rf_predicted_score_reverse               0.688406              0.652174   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77290                 77291  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                             AMKennedy               CThomas   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     1                     0   \n",
       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                0.250725              0.355072   \n",
       "rf_predicted_score_reverse               0.727536              0.626087   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77292                 77293  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                            RBGinsburg              SGBreyer   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     1                     1   \n",
       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                 0.25942              0.255072   \n",
       "rf_predicted_score_reverse               0.723188              0.726087   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77294                 77295  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                               SAAlito            SSotomayor   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     0                     1   \n",
       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                0.326087              0.247826   \n",
       "rf_predicted_score_reverse               0.655072              0.731884   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77296  \n",
       "caseName                     OBERGEFELL v. HODGES  \n",
       "justiceName                                EKagan  \n",
       "case_outcome_disposition                        1  \n",
       "justice_outcome_disposition                     1  \n",
       "rf_predicted                                    1  \n",
       "rf_predicted_score_affirm                0.237681  \n",
       "rf_predicted_score_reverse               0.742029  \n",
       "rf_correct_case                                 1  \n",
       "dummy_correct_case                              1  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"OBERGEFELL v. HODGES\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "                                       77126            77127  \\\n",
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       "rf_correct_case                            1                1   \n",
       "dummy_correct_case                         0                0   \n",
       "\n",
       "                                       77130            77131  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
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       "\n",
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       "rf_correct_case                            1                1                1  \n",
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      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"KING v. BURWELL\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>0.30625</td>\n",
       "      <td>0.58125</td>\n",
       "      <td>0.271875</td>\n",
       "      <td>0.292187</td>\n",
       "      <td>0.26875</td>\n",
       "      <td>0.548438</td>\n",
       "      <td>0.50625</td>\n",
       "      <td>0.29375</td>\n",
       "      <td>0.31875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.654687</td>\n",
       "      <td>0.385937</td>\n",
       "      <td>0.689063</td>\n",
       "      <td>0.657813</td>\n",
       "      <td>0.692187</td>\n",
       "      <td>0.420312</td>\n",
       "      <td>0.454688</td>\n",
       "      <td>0.671875</td>\n",
       "      <td>0.509375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      73130  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       JGRoberts   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
       "rf_predicted_score_affirm                                           0.30625   \n",
       "rf_predicted_score_reverse                                         0.654687   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73131  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       JPStevens   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               0   \n",
       "rf_predicted                                                              0   \n",
       "rf_predicted_score_affirm                                           0.58125   \n",
       "rf_predicted_score_reverse                                         0.385937   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73132  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         AScalia   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
       "rf_predicted_score_affirm                                          0.271875   \n",
       "rf_predicted_score_reverse                                         0.689063   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73133  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       AMKennedy   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
       "rf_predicted_score_affirm                                          0.292187   \n",
       "rf_predicted_score_reverse                                         0.657813   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73134  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         CThomas   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
       "rf_predicted_score_affirm                                           0.26875   \n",
       "rf_predicted_score_reverse                                         0.692187   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73135  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                      RBGinsburg   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               0   \n",
       "rf_predicted                                                              0   \n",
       "rf_predicted_score_affirm                                          0.548438   \n",
       "rf_predicted_score_reverse                                         0.420312   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73136  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                        SGBreyer   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               0   \n",
       "rf_predicted                                                              0   \n",
       "rf_predicted_score_affirm                                           0.50625   \n",
       "rf_predicted_score_reverse                                         0.454688   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73137  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         SAAlito   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
       "rf_predicted_score_affirm                                           0.29375   \n",
       "rf_predicted_score_reverse                                         0.671875   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73138  \n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION  \n",
       "justiceName                                                      SSotomayor  \n",
       "case_outcome_disposition                                                  1  \n",
       "justice_outcome_disposition                                               0  \n",
       "rf_predicted                                                              1  \n",
       "rf_predicted_score_affirm                                           0.31875  \n",
       "rf_predicted_score_reverse                                         0.509375  \n",
       "rf_correct_case                                                           1  \n",
       "dummy_correct_case                                                        1  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == 'CITIZENS UNITED v. FEDERAL ELECTION COMMISSION',\n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>72257</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caseName</th>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
       "      <td>DISTRICT OF COLUMBIA v. HELLER</td>\n",
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       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
       "      <td>0.319355</td>\n",
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       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.651613</td>\n",
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       "      <td>0.66129</td>\n",
       "      <td>0.646774</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                                                      72257  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                       JPStevens   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               1   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.319355   \n",
       "rf_predicted_score_reverse                         0.651613   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72258  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                         AScalia   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               0   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.377419   \n",
       "rf_predicted_score_reverse                         0.606452   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72259  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                       AMKennedy   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               0   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.272581   \n",
       "rf_predicted_score_reverse                         0.701613   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72260  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                        DHSouter   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               1   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.327419   \n",
       "rf_predicted_score_reverse                         0.654839   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72261  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                         CThomas   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               0   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.383871   \n",
       "rf_predicted_score_reverse                          0.58871   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72262  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                      RBGinsburg   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               1   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.358065   \n",
       "rf_predicted_score_reverse                         0.619355   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72263  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                        SGBreyer   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               1   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.351613   \n",
       "rf_predicted_score_reverse                         0.629032   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72264  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                       JGRoberts   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               0   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.316129   \n",
       "rf_predicted_score_reverse                          0.66129   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72265  \n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER  \n",
       "justiceName                                         SAAlito  \n",
       "case_outcome_disposition                                  0  \n",
       "justice_outcome_disposition                               0  \n",
       "rf_predicted                                              1  \n",
       "rf_predicted_score_affirm                          0.327419  \n",
       "rf_predicted_score_reverse                         0.646774  \n",
       "rf_correct_case                                           0  \n",
       "dummy_correct_case                                        0  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == 'DISTRICT OF COLUMBIA v. HELLER',\n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Write output\n",
    "import cPickle as pickle\n",
    "import time\n",
    "\n",
    "timestamp_suffix = time.strftime(\"%Y%m%d%H%M%S\")\n",
    "raw_data.to_csv(\"data/output/model_output_{0}_{1}.csv\".format(trees_per_term, timestamp_suffix),\n",
    "                 index=False)\n",
    "# You probably don't want to do this, as the object can be tens of GB\n",
    "#pickle.dump(m, open(\"scotus_scdb_model_{0}.pickle\".format(timestamp_suffix), \"w\"))"
   ]
  }
 ],
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