build_scdb_model_10_extra_trees.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."
   ]
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     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "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": 2,
   "metadata": {
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   "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": 3,
   "metadata": {
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    {
     "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": 4,
   "metadata": {
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   "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": 5,
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     "name": "stdout",
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     "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py:487: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight(\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn('class_weight presets \"balanced\" or \"balanced_subsample\" are '\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.ExtraTreesClassifier(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": 6,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.90      0.48      0.63      6700\n",
      "          0       0.59      0.35      0.44     24325\n",
      "          1       0.66      0.87      0.75     41142\n",
      "\n",
      "avg / total       0.66      0.66      0.63     72167\n",
      "\n",
      "[[ 3213   470  3017]\n",
      " [  139  8463 15723]\n",
      " [  226  5295 35621]]\n",
      "0.655382654122\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\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(\"\")"
   ]
  },
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tDRMmfJLV9MkreZDRZOhG/u7GTsla/KrknErnZEJqq2j58fj4OHp7e+2ve3p6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\n9f5WDg5jYi29NNDrUMIPLWpDR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a1VH+lrjQqGYSAKHLRjjKJaJlF0w3Qa0ZoGiEoXV/+I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\nnKn1i2dqKT8GAAAAgGYi1M5QT6il/BgAAAA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eZ44cOWJuuukmfvdHWL3rn8lkzCuvvGL2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c3a3b1ed0>"
      ]
     },
     "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8c3a208850>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAABIITADAABACoEZAAAAUgjMAAAAkEJgBgAAgBQCMwAAAKQQmAEAACCFwAwA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\nA6/UlfK6XfxUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c3a30c610>"
      ]
     },
     "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": 9,
   "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>1364</th>\n",
       "      <td>justice_cumulative_lc_direction_diff</td>\n",
       "      <td>0.020287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>justice_previous_lc_direction_diff</td>\n",
       "      <td>0.020257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>justice_cumulative_agreement</td>\n",
       "      <td>0.016502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>justice_previous_agreement</td>\n",
       "      <td>0.016095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>justice_cumulative_direction</td>\n",
       "      <td>0.015733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>justice_previous_court_direction_diff</td>\n",
       "      <td>0.015638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>justice_cumulative_court_direction_diff</td>\n",
       "      <td>0.015582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>justice_previous_direction</td>\n",
       "      <td>0.015507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>issue_area_9</td>\n",
       "      <td>0.014996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>argument_month_-1</td>\n",
       "      <td>0.014665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      feature  importance\n",
       "1364     justice_cumulative_lc_direction_diff    0.020287\n",
       "1363       justice_previous_lc_direction_diff    0.020257\n",
       "1358             justice_cumulative_agreement    0.016502\n",
       "1357               justice_previous_agreement    0.016095\n",
       "1346             justice_cumulative_direction    0.015733\n",
       "1349    justice_previous_court_direction_diff    0.015638\n",
       "1350  justice_cumulative_court_direction_diff    0.015582\n",
       "1345               justice_previous_direction    0.015507\n",
       "1154                             issue_area_9    0.014996\n",
       "104                         argument_month_-1    0.014665"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
      "Ttest_relResult(statistic=15.210306462433861, pvalue=9.1388724464794442e-23)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=7.1459876941793974, pvalue=8.9351064739809073e-13)\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": 11,
   "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1    8013\n",
      " 0    4139\n",
      "-1     513\n",
      "Name: case_outcome_disposition, dtype: int64\n",
      " 1    0.626456\n",
      " 0    0.323587\n",
      "-1    0.040106\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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.86      0.57      0.68      3676\n",
      "        0.0       0.52      0.24      0.33     21597\n",
      "        1.0       0.70      0.89      0.78     45782\n",
      "\n",
      "avg / total       0.65      0.68      0.64     71055\n",
      "\n",
      "[[ 2086    63  1527]\n",
      " [  125  5182 16290]\n",
      " [  207  4689 40886]]\n",
      "0.677700372951\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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8c39f66d90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERERERER1jcGViIiIiIiI6hqDKxEREREREdU1BlciIiIiIiKqawyuREREREREVNcYXImI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\nrrZt6+6779bKlStljNH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s2KC2tjZqn0C11v/hhx/W+vXrdfbZZ8sYI8uytGHDBk2bNq3m\n+tc1uAIAAAAAUG9MRQAAAAAAxBrBFQAAAAAQawRXAAAAAECsEVwBAAAAALFGcAUAAAAAxBrBFQAA\nAAAQawRXAAAAAECsEVwBAAAAALH2/wGgudSmen4vjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c3a33f090>"
      ]
     },
     "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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f8c39e8d250>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAACQQmAGAACAFAIzAAAApBCYAQAAIIXADAAAACkEZgAAAEghMAMAAEAKgRkA\nAABSCMwAAACQQmAGAACAFAIzAAAApBCYAQAAIIXADAAAACkEZgAAAEghMAMAAEAKgRkAAABSCMwA\nAACQQmAGAACAFAIzAAAApBCYAQAAIIXADAAAACkEZgAAAEghMAMAAEAKgRkAAABSCMwAAACQQmAG\nAACAFAIzAAAApBCYAQAAIIXADAAAACkEZgAAAEghMAMAAEAKgRkAAABSCMwAAACQQmAGAACAFAIz\nAAAApMh6YF6yZEkce+yxccwxx8Stt966zf3Lly+P6dOnx3777Rc/+clPWtUXAAAA2iqrgbmhoSHm\nzZsXt912WyxevDjKysri1VdfbdSmf//+cemll8bnPve5VvcFAACAtspqYF66dGnsvvvuMWTIkOjd\nu3dMnjw5nnjiiUZtCgsLY9999428vLxW9wUAAIC2ympgrqqqisGDB2duFxcXx5o1a3Z4XwAAAGhO\nXvNNup9Bg/pluwSyxNj3bMa/ZzP+PVdnG/t33smPvLy86N27+Y9heXl5MXBgfqdbh626wrp01m3H\njmfsaQ9ZDczFxcVRUVGRuV1VVRVFRUU7vO/atetbVyjdwqBB/Yx9D2b8ezbj33N1xrGvrq6N+vr6\n2Ly5vtm29fX1UV1dG336dK512Kqzr0tnHH86hrHv2drzy5KsnpK93377xYoVK2LVqlVRV1cXZWVl\nMX78+CbbJ0nS5r4AAADQGlk9wtyrV6+47LLLYtasWZEkSUydOjWGDRsWd911V+Tk5MS0adPijTfe\niJNPPjk2bNgQubm5sWDBgigrK4tddtkltS8AAAC0h6xfwzx27NgYO3Zso79Nnz498//ddtstysvL\nW9wXAAAA2kNWT8kGAACAzkpgBgAAgBQCMwAAAKQQmAEAACCFwAwAAAApBGYAAABIITADAABACoEZ\nAAAAUgjMAAAAkEJgBgAAgBQCMwAAAKQQmAEAACBFXrYLAAB6noaGhqisXN2itiUlgyM313f8AHQ8\ngRkA6HCVlavj2gXlkV9QuN12tetqYu7McVFaOqSDKgOAfxGYAYCsyC8ojH79i7JdBgA0yflNAAAA\nkEJgBgAAgBQCMwAAAKQQmAEAACCFwAwAAAApBGYAAABIITADAABACoEZAAAAUgjMAAAAkEJgBgAA\ngBQCMwAAAKQQmAEAACCFwAwAAAApBGYAAABIITADAABACoEZAAAAUgjMAAAAkEJgBgAAgBQCMwAA\nAKTIy3betP2lAAAgAElEQVQBAACwPQ0NDVFZubpFbUtKBkdurmNCQPsQmAEA6NQqK1fHtQvKI7+g\ncLvtatfVxNyZ46K0dEgHVQZ0dwIzAACdXn5BYfTrX5TtMoAexvkqAAAAkEJgBgAAgBROyQa6FBO/\nAADQUQRmoEsx8QsAAB1FYAa6HBO/AADQEZyrCAAAACkEZgAAAEghMAMAAEAKgRkAAABSmPQL6PYa\nGhpi5cqVUV1d22xbP0UFAMBWAjPQ7VVWro7rFz4ZO39o1+2281NUAAC8l8AM9Aj5BQOjb/72f7sZ\nAADey3mHAAAAkEJgBgAAgBROyaZLamhoiMrK1S1qaxInAACgLQRmuqTKytVx7YLyyC/Y/jWpJnEC\nAADaSmCmy8ovKIx+/YuyXQYAANBNOU8VAAAAUgjMAAAAkMIp2WSdCbwAaE5r3isivF8A0D4EZrLO\nBF4ANKel7xUR3i8AaD8CM52CCbwAaI73CgA6mnOVAAAAIIUjzAAAQLfR0NAQK1eujOrq2mbbmu+A\n5gjMAABAt1FZuTquX/hk7PyhXbfbznwHtITADAAAdCv5BQOjb37zkwRCc5x/AAAAACkEZgAAAEgh\nMAMAAEAK1zADdHMNDQ1RWbm6RW3NFgoA8C8CM0A3V1m5Oq5dUB75Bduf/MRsodC9+LIM4IMTmAF6\ngPyCwujXvyjbZQAdyJdlAB+cwAwA0E35sgzgg3HuDQAAAKQQmAEAACCFU7IBIAtMyAQAnZ/ADABZ\nYEImAOj8BGYAyBITMgFA5+b8LgAAAEghMAMAAEAKgRkAAABSuIYZgG2YwRkAoJnAXFVVFffff388\n/fTT8frrr0eSJDF06NA49NBD46STToqSkpKOqhOADmQGZwCA7QTmb37zm/GnP/0pJkyYEGeddVYm\nHFdVVcWf/vSn+MIXvhAHHXRQXHHFFR1WLAAdxwzOAEBP12RgHjduXGoYHjZsWBx22GExZ86c+M1v\nfrMjawMAAICsafKisyOPPLLZzi1pAwAAAF1Ri2dp+d3vfhfHH398jB8/PhYtWrQjawIAAICsazIw\nr1u3rtHthQsXxj333BMPPfRQ3HbbbTu8MAAAAMimJgPzueeeGw899FDmdu/eveOFF16IF154IfLy\n/BoVAAAA3VuTgfn222+Pf/zjH3H22WfHihUr4oILLoh77rknFixYEPPmzevIGgEAAKDDNXmoeKed\ndoo5c+bEa6+9FldffXUccMABMX/+fEeXAQAA6BGaTL+1tbXxwAMPxE477RQ33HBDPProo3HmmWfG\n+eefH6NGjerIGuEDa2hoiJUrV0Z1dW2L2peUDI7c3BbPiQcAAHRDTQbmOXPmxMEHHxybNm2KSy65\nJK6//vo48sgj47rrrov77rsvrr766o6sEz6QysrVcf3CJ2PnD+3abNvadTUxd+a4KC0d0gGVAQAA\nnVWTgbm6ujrOO++8SJIkTjrppIiI6N+/f1x11VXx7LPPdliB0F7yCwZG3/zCbJcBAAB0EU0G5hEj\nRsQZZ5wRdXV1ceSRRza6zynZ7auhoSEqK1e3qK1ThYHuwmsfANDZNRmYr7zyynj55ZcjLy8v9thj\nj46sqceprFwd1y4oj/yC7R/9dKow0J147eucfJEBAP/SZGDetGlT7LXXXtvtvGnTpujbt2+7F9UT\n5RcURr/+RdkuA6BDee3rfHyRAQD/0mRg/uxnPxtHH310TJ48eZsjzK+99losXrw4fv3rX8cvf/nL\nHV4kANBxfJEBAO9qMjDfddddcffdd8eXvvSlWLNmTRQVvfvGufX/U6dOjYULF3ZYoQAAdH1O+we6\nkiYD80477RQzZsyIGTNmRE1NTaxcuTIiIoYOHRqFhWYaBgCg9Zz2D3QlTQbm9yosLBSSAQBoF077\nB7qKFgVmoHNxOhsAAOx4AjN0QU5nAwCAHU9gboYjeXRWTmcDAIAdq8nAXF5evt2O48aNa/diOiNH\n8gAAAHqmJgPzj3/844iIqKuri2XLlsXee+8dERF/+9vfYv/99+8xgTnCkTwAAICeqMnAfOedd0ZE\nxAUXXBCXXHJJHHDAARERsXTp0vjpT3/abgUsWbIk5s+fH0mSxMknnxyzZ8/eps1VV10VS5Ysib59\n+8Y111wT++yzT0REHH300ZGfnx+5ubmRl5cX9957b7vVBQAAQM/W7DXML7/8ciYsR0Tsv//+8be/\n/a1dFt7Q0BDz5s2LO+64I4qKimLq1Kkxfvz4GDZsWKZNeXl5rFixIh577LF4/vnn4xvf+Ebcc889\nERGRk5MTd955Z+y6667tUg8AAABs1Wxg7tu3bzzwwAMxZcqUiIh48MEHo2/fvu2y8KVLl8buu+8e\nQ4a8e93v5MmT44knnmgUmJ944ok48cQTIyLigAMOiPXr18cbb7wRu+22WyRJEg0NDe1SC8AHZZJA\nAIDupdnAfM0118TcuXPjsssui4iIvffeO771rW+1y8Krqqpi8ODBmdvFxcWxbNmyRm3WrFkTJSUl\njdpUVVXFbrvtFjk5OTFr1qzIzc2NadOmxX/8x3+0S10AbWGSQACA7qXZwDxs2LBYtGhR1NbWRkRE\nfn7+Di+qpX7+859HUVFR1NTUxJlnnhl77rlnjBo1qtl+gwb1a/Ey3nknP/Ly8qJ37+1vqry8vBg4\nML9Vj92Ry+jM2rL+re2zVXPt37+czqon7zNt3V8imh//D7q93nknP/oXFkXBgO1PEtjR49IRz7Gu\n8NrX2Z4HnfV53FF1teV1vCXt02rrqmMf0fmfY21Zl63/76jXpM42/ux4HfW+T8/Qot9hXr9+ffz9\n73+Pd955J/O3gw8++AMvvLi4OCoqKjK3q6qqoqio8QfNoqKiqKyszNyurKyM4uLizH0REYWFhTFh\nwoRYtmxZiwLz2rXrW1xjdXVt1NfXx+bN9dttV19fH9XVtdGnT8sfuyOX0Zm1Zf1b22er5tq/fzmd\nVU/eZ9q6v0Q0P/4fdHt11nHpiOdYZ3/tGzSoX6te+ztCd9pfOmI5W//f2tfxrjz2EZ3/OdaWddn6\n/454TeqM48+O11Hv+3Re7fklSLMX0D388MNx/PHHx+mnnx6XXnppzJw5M+bPn98uC99vv/1ixYoV\nsWrVqqirq4uysrIYP358ozbjx4+P+++/PyIinnvuuSgoKIjddtstNm3aFBs2bIiIiI0bN8aTTz4Z\ne+21V7vUBQAAAM0eYf7hD38YixYtis997nNx//33x1NPPRWPPvpouyy8V69ecdlll8WsWbMiSZKY\nOnVqDBs2LO66667IycmJadOmxbhx46K8vDwmTJiQ+VmpiIg33ngj5syZEzk5ObFly5b41Kc+FYcf\nfni71AVA99HQ0BArV65sdLZJU0zGBgC8V7OB+d1z+wfGli1bIiJizJgxcd1117VbAWPHjo2xY8c2\n+tv06dMb3b788su36ffhD384HnjggXarg221ZsbfCB80gc6psnJ1XL/wydj5Q9v/CUKTsQEA79ds\nYN5pp50iSZLYfffd484774whQ4bExo0bO6I2sqylM/5G+KAJdG75BQOjb37zr2UAAO/VbGA+//zz\no7a2Ni688ML4xje+EevXr48rrriiI2qjE8gvKIx+/bc/4y8AAEB31GxgPvTQQyMiol+/fnHHHXfs\n6HoAAACgU2g2MFdXV8c111wTq1evjp/97Gfx0ksvxZ///Oc49dRTO6I+oAtpzXXvrnkHAKCzazYw\nX3rppTF27NhYuHBhRETsueeeMXfuXIEZ2EZLr3t3zTsAAF1Bs4G5qqoqTj311Lj77rsj4t1JwBwV\nApriuncAALqLFv2s1HutW7cukiTZYQUBAF2LyzEA6K6aDcwTJkyIyy+/PDZs2BCLFi2KhQsXxskn\nn9wRtQE9QGs/aAOdj8sxAOiumg3MZ511Vjz44IOxbt26KC8vjxkzZsSUKVM6ojagB2jtB22gc3I5\nBgDd0XYD85YtW+IHP/hBfOlLX4oTTjiho2oCehgftAEA6Iy2exFRr169YsmSJR1VCwAAAHQazc66\nceSRR8Ztt90W1dXVsWnTpsw/AAAA6M6avYb5pptuioiIa6+9NvO3nJycePHFF3dcVQAAAJBlzQbm\nl156qSPqAAAAgE6l2cAcEVFTUxPPP/98REQceOCBMWDAgB1aFAAAAGRbs9cwP/bYY3HcccfFnXfe\nGXfeeWdMmjQpHn/88Y6oDQAAALKm2SPM119/fdx1112xxx57RETEa6+9Fuecc0588pOf3OHFAQAA\nQLY0e4S5T58+mbAcEfHRj340dt555x1aFAAAAGRbs4F5/Pjxccstt8TatWtjzZo18cMf/jDGjx8f\nb7/9tp+XAgAAoNtq9pTsH/zgBxERceONNzb6+0033eTnpQAAAOi2/KwUAAAApGjRz0pFRKxbty6e\neeaZGDp0aAwfPnxH1gQApGhoaIjKytUtaltSMjhyc5u98goA2I4mA/OFF14Yn//852P48OHx5ptv\nxpQpUyI/Pz/++c9/xpe//OU45ZRTOrJOAOjxKitXx7ULyiO/oHC77WrX1cTcmeOitHRIB1UGAN1T\nk4H5hRdeyBxJfuCBB2LYsGFx++23R2VlZXzhC18QmAEgC/ILCqNf/6IWtW3NEemId49KAwD/0mRg\n7tOnT+b/f/zjHzO/u1xSUhI5OTk7vjIA4ANp6RHpiH8dlQYA/mW71zBXVVXFrrvuGs8880x86Utf\nyvz9nXfe2eGFAQAfXGuOSAMAjTUZmGfPnh0nnnhi9O7dO0aOHBn/9m//FhERzz33XJSWlnZYgQAA\nAJANTQbm4447LkaNGhVvvPFGo1mxBw8eHPPmzeuQ4gAAACBbtntK9qBBg2LQoEGN/lZcXLxDCwIA\nAIDOwA80AgAAQIrtHmEGoHNpzc8ElZQMjtxc34tCa3iOAfBeAjNAF9LSnwna+hNBpaVDOqiyjiHM\nsKP19OcYAI0JzABdTE/+mSBhho7Qk59jADQmMAPQpQgzAEBHca4aAAAApBCYAQAAIIXADAAAACkE\nZgAAAEghMAMAAEAKgRkAAABSCMwAAACQQmAGAACAFAIzAAAApBCYAQAAIIXADAAAACkEZgAAAEiR\nl+0CAACA7GpoaIjKytUtaltSMjhycx13o2cQmAEAoIerrFwd1y4oj/yCwu22q11XE3NnjovS0iEd\nVBlkl8Dczlrz7VzEu9/QAQBAtuUXFEa//kXZLgM6FYG5nbX027mIf31DBwAAQOcjMO8Avp0DgJ6j\ntdd+AtB1CMwAAB9Aa6/9BKDrEJgBAD4gZ5d1fW2Zh8ZM0dD9CcwAAPR4bZmHxkzR0P0JzAAp/B4l\nQM/jTAHg/QRmgBR+jxIAAIEZoAmONACdhbNeALJDYIYewActgK7NWS8A2SEwQw/ggxZA1+esF4CO\nJzBDD+GDFgAAtI7zLgEAACCFwAwAAAApnJLdg7R24icAAICeTGDuQVo78RMAAEBPJjD3MCZ+AgAA\naBnXMAMAAEAKR5gBsqQ18wpEmFugo7RlXHJzff8MAN2RwAyQJS2dVyDC3AIdqS3jUlo6pAMqAwA6\nmsAMkEXmFeicjAsAEOEaZgAAAEglMAMAAEAKgRkAAABSCMwAAACQQmAGAACAFAIzAAAApBCYAQAA\nIIXADAAAACnysl0AdCcNDQ1RWbm6RW1LSgZHbq7vrAAAoLMSmKEdVVaujmsXlEd+QeF229Wuq4m5\nM8dFaemQDqoM2JF8WQYA3ZPADO0sv6Aw+vUvynYZQAfyZRkAdE8CMwC0A1+WAUD345wwAAAASCEw\nAwAAQAqBGQAAAFIIzAAAAJBCYAYAAIAUAjMAAACkEJgBAAAghcAMAAAAKQRmAAAASCEwAwAAQIq8\nbBdA99LQ0BCVlatb1LakZHDk5vrOBgAA6JwEZtpVZeXquHZBeeQXFG63Xe26mpg7c1yUlg7poMoA\nAABaR2Cm3eUXFEa//kXZLqPLcFQeAAA6J4EZssxReQAA6JwEZugEHJUHAIDOx7mdAAAAkEJgBgAA\ngBQCMwAAAKQQmAEAACCFwAwAAAApBGYAAABIITADAABAiqwH5iVLlsSxxx4bxxxzTNx6662pba66\n6qqYOHFiTJkyJV588cVW9QUAAIC2yGpgbmhoiHnz5sVtt90WixcvjrKysnj11VcbtSkvL48VK1bE\nY489FldeeWVcccUVLe4LAAAAbZXVwLx06dLYfffdY8iQIdG7d++YPHlyPPHEE43aPPHEE3HiiSdG\nRMQBBxwQ69evjzfeeKNFfQEAAKCt8rK58Kqqqhg8eHDmdnFxcSxbtqxRmzVr1kRJSUnmdklJSVRV\nVbWob3upXVfTqjYtad+WPu9t09DQEJWVq1u0nJKSwZGbm9um5XTEunRUn9p11VFfX9/iPh21jTuq\nT1uWUVGxqtk+ERGlpUM+0HI6Yl1aMv6ddVza0qej16U1z5eId58zHVHXv263bvy7y7i0tH1b+nSV\ndVm5cmVUV9c226czvo61pU97jEtrX/s767o0NDS0ePxb85r03nZt/azQlvfX1vTpqNfktqx/W/q0\ndN0j3rtftv59v7Xj0tp1iYg2jUtb9jHaT06SJEm2Fv7oo4/Gk08+GfPmzYuIiAceeCCWLVsWl156\naabN2WefHbNnz46DDjooIiLOOOOMmDt3bqxcubLZvu2hoaEhKioqWtS2tLQ0IqLF7dvSp7S0NHJz\nc2PlypVx6fcfifyCgdttX7uuOq764rExdOjQTrsura2rLX1aU9fWPhUVFTt8G3fG9d+6jIh3P2i2\nxNChQyOi9c+XjlqX7jAubenTkevS0udLxL+eM1v77ci6evq4tKautvTp7Ouy9f2yJTrr61hb+nyQ\ncYlo3Wt/a5axdTkRHbMurf2s1JrXpK3LactnhYjWv7+2tk9L1/29tbXlNbktn0fb2qel2vp5LKL1\n49LadYmIVo9LS/u8fx+j/WT1CHNxcXGjnbmqqiqKiooatSkqKorKysrM7crKyiguLo7Nmzc327cp\na9eub1Wdffrs2qJ21dUbWtW+LX22tq+uro2dP7Rr9M0v3G77+vr6qK6ujT591rdpOR2xLh3VZ+jQ\noS0e++rqDR22jTuqz45cxnu3a2ddl5aOf2cdl7b06ch1aenzJeK9z5kNHbYurR3/7jIurWnflj5d\nYV1aOvad9XWsLX06aly2brPOui7V1bWRXzCwFe/jLX9N2rqcHf1ZoS375dq16zvsNbkt69+WPq0Z\nl63bbEe+72993NauS0S0elxa2uf9+1hPN2hQv3Z7rKwes99vv/1ixYoVsWrVqqirq4uysrIYP358\nozbjx4+P+++/PyIinnvuuSgoKIjddtutRX0BAACgrbJ6hLlXr15x2WWXxaxZsyJJkpg6dWoMGzYs\n7rrrrsjJyYlp06bFuHHjory8PCZMmBB9+/aNa665Zrt9AQAAoD1kNTBHRIwdOzbGjh3b6G/Tp09v\ndPvyyy9vcV8AAABoD6ZRAwAAgBQCMwAAAKQQmAEAACCFwAwAAAApBGYAAABIITADAABACoEZAAAA\nUgjMAAAAkCIv2wUA0D3Urqtp13YAANkmMAPwgZWUDI65M8e1qj0AQGcnMAPwgeXm5kZp6ZBslwEA\n0K5cwwwAAAApBGYAAABIITADAABACoEZAAAAUgjMAAAAkEJgBgAAgBQCMwAAAKQQmAEAACCFwAwA\nAAAp8rJdAAAA0L5q19W0azvoqQRmAADoRkpKBsfcmeNa1R5IJzADAEA3kpubG6WlQ7JdBnQLrmEG\nAACAFAIzAAAApBCYAQAAIIXADAAAACkEZgAAAEghMAP/r727D7K6rP8//tpl0RwVHVjchcWsIEaH\nSc1ovBnFRMK8WWWFHGsKB6msTEPJmcBsSpManVGmacaBibEpU7sRbAxvwcrRTM1M8o7MGVOIewaV\nm4CFz/cPf+5P4sJYYlkO+3j8xZ5zfc5eZ9+7yz53z+ccAACgQDADAABAgWAGAACAAsEMAAAABYIZ\nAAAACgQzAAAAFDR09wZgb7b2zdW7ZQ0AAFB7BDPsQHPzgFw1/tSdXgsAAOxbBDPsQH19fQYObOnu\nbQAAAN3EOcwAAABQIJgBAACgQDADAABAgWAGAACAAsEMAAAABYIZAAAACgQzAAAAFAhmAAAAKBDM\nAAAAUNDQ3RsAAICusPbNVWlvb/8va1bvod0AtUgwAwCwz2luHpDvXfaprFq1dqfW7qqdCW5RDrVL\nMAMAsM+pr6/PoEGDsv/+b3XZ+2huHpCrxp+602uB2iOYAQBgF9TX12fgwJbu3gbQhTzpFwAAABQI\nZgAAACgQzAAAAFDgHGYAAIA9YGefMd0zq+89BDMAAEAX68yzqr+zfunSJV24I3aGYAYAAOhinlW9\nNjmHGQAAAAoEMwAAABQIZgAAACgQzAAAAFAgmAEAAKBAMAMAAECBYAYAAIACwQwAAAAFghkAAAAK\nBDMAAAAUCGYAAAAoEMwAAABQIJgBAACgQDADAABAgWAGAACAAsEMAAAABYIZAAAACgQzAAAAFDR0\n9wYAAICeY+2bq3fLGtgTBDMAALBHNDcPyFXjT93ptdDdBDMAALBH1NfXZ+DAlu7eBuw05zADAABA\ngWAGAACAAsEMAAAABYIZAAAACgQzAAAAFAhmAAAAKBDMAAAAUCCYAQAAoEAwAwAAQIFgBgAAgALB\nDAAAAAWCGQAAAAoEMwAAABQIZgAAACgQzAAAAFAgmAEAAKBAMAMAAECBYAYAAIACwQwAAAAFghkA\nAAAKBDMAAAAUCGYAAAAoEMwAAABQ0NDdG2DXrX1z9W5ZAwAAwPYEc41qbh6Qq8afutNrAQAA6Jxu\nC+Y33ngjV1xxRRYvXpxBgwZl+vTpOfjgg7db98gjj2TatGmpqipjx47Nl770pSTJj370o/zyl79M\nv379kiRXXHFFRowYsUfvQ3eqr6/PwIEt3b0NAACAfVa3ncM8c+bMnHjiiXnggQdy/PHHZ8aMGdut\n2bp1a6677rrMmjUrv/3tbzN37ty88sorHddPmDAhc+bMyZw5c3pULAMAAND1ui2Y58+fn7a2tiRJ\nW1tb5s2bt92aBQsW5IgjjkhLS0t69+6ds88+O/Pnz++4vqqqPbZfAAAAepZuC+bVq1ensbExSdK/\nf/+sXr39k1MtW7YsAwb8//Nvm5qasnz58o63b7vttpx33nm5+uqr89Zbb3X9pgEAAOgxuvQc5gkT\nJmTlypXbXT5p0qTtLqurq+vUbX/2s5/NpZdemrq6utx88835/ve/n2nTpu3Usf37b3+uND2D2fds\n5t+zmX/PZfY9m/l3nY0bD0pDQ0N6937vpGhoaEi/fgft8Vl09fvbE/d/b/8Y9wRdGsy33nrrDq/r\n169fVq5cmcbGxqxYsSJ9+/bdbk1TU1P+9a9/dby9bNmyHHbYYUmyzfoLLrggX/7yl3d6XytW+Gt0\nT9S//8Fm34OZf89m/j2X2fds5t+1Vq1am/b29mze3P6e69rb27Nq1drsv/+em8WemP2euP9788d4\nb7Y7f3HQbQ/JHjlyZGbPnp0kmTNnTk4//fTt1nzkIx/Ja6+9lsWLF2fTpk2ZO3dux7oVK1Z0rHvo\noYcydOjQPbNxAAAAeoRue1mpL37xi5k0aVLuuuuutLS0ZPr06UmS5cuX55prrsmMGTPSq1evXHPN\nNbn44otTVVXGjRuXwYMHJ0luvPHGvPjii6mvr09LS0uuvfba7rorAAAA7IO6LZgPPfTQ/OQnP9nu\n8sMOO2ybl5gaMWJE8SWjbrjhhq7cHgAAAD1ctz0kGwAAAPZmghkAAAAKBDMAAAAUCGYAAAAoEMwA\nAABQIJgBAACgQDADAABAgWAGAACAgobu3gAAAABla99cvVvWsGsEMwAAwF6ouXlArhp/6k6vZfcT\nzAAAAHuh+vr6DBzY0t3b6NGcwwwAAAAFghkAAAAKBDMAAAAUCGYAAAAoEMwAAABQIJgBAACgQDAD\nAABAgWAGAACAAsEMAAAABYIZAAAACgQzAAAAFAhmAAAAKBDMAAAAUCCYAQAAoEAwAwAAQIFgBgAA\ngALBDAAAAAWCGQAAAAoEMwAAABQIZgAAACgQzAAAAFAgmAEAAKBAMAMAAECBYAYAAIACwQwAAAAF\nghkAAAAKBDMAAAAUNHT3BgAAgNq09s3Vu2UN7K0EMwAA0GnNzQNy1fhTd3ot1CLBDAAAdFp9fX0G\nDmzp7m1Al3IOMwAAABQIZgAAACgQzAAAAFAgmAEAAKBAMAMAAECBYAYAAIACwQwAAAAFghkAAAAK\nBDMAAAAUCGYAAAAoEMwAAABQIJgBAACgQDADAABAgWAGAACAAsEMAAAABYIZAAAACgQzAAAAFAhm\nAAAAKBDMAAAAUCCYAQAAoEAwAwAAQIFgBgAAgALBDAAAAAWCGQAAAAoEMwAAABQIZgAAACgQzAAA\nAFAgmAEAAKBAMAMAAECBYAYAAIACwQwAAAAFghkAAAAKBDMAAAAUCGYAAAAoEMwAAABQIJgBAACg\nQDADAABAgWAGAACAAsEMAAAABQ3dvQEAAIBatPbN1btlDXsvwQwAANBJzc0DctX4U3d6LbVJMAMA\nAHRSfX19Bg5s6e5t0MWcwwwAAAAFghkAAAAKBDMAAAAUCGYAAAAoEMwAAABQIJgBAACgQDADAABA\ngWAGAACAAsEMAAAABYIZAAAACgQzAAAAFAhmAAAAKBDMAAAAUCCYAQAAoEAwAwAAQIFgBgAAgALB\nDAAAAAWCGQAAAAoEMwAAABQIZgAAACjotmB+4403cvHFF+eMM87IxIkT89ZbbxXXTZ06NSeddFJa\nW1t36XgAAADYFd0WzDNnzsyJJ56YBx54IMcff3xmzJhRXHf++edn1qxZu3w8AAAA7IpuC+b58+en\nra0tSdLW1pZ58+YV1w0fPjx9+vTZ5eMBAABgV3RbMK9evTqNjY1Jkv79+2f16tV79HgAAAB4Lw1d\nebZV8FAAAAuBSURBVOMTJkzIypUrt7t80qRJ211WV1f3P72vzhzfv//B/9P7onaZfc9m/j2b+fdc\nZt+zmX/PZfbsDl0azLfeeusOr+vXr19WrlyZxsbGrFixIn379u3Ubf+vxwMAAMB76baHZI8cOTKz\nZ89OksyZMyenn376DtdWVfU/HQ8AAACdVVeVanQPWLNmTSZNmpQlS5akpaUl06dPT58+fbJ8+fJc\nc801Hc96PXny5DzxxBNZs2ZNGhsbc9lll2Xs2LE7PB4AAAB2h24LZgAAANibddtDsgEAAGBvJpgB\nAACgQDADAABAQc0H89SpU3PSSSeltbW147KXXnopF154Yc4999x85Stfybp165IkixcvzjHHHJO2\ntra0tbXlO9/5Tscxzz//fFpbW3PGGWfk+uuv39N3g13Umfm/+7pzzjkn5557bjZt2pTE/GtRZ2Z/\nzz33ZMyYMWlra8uYMWNy1FFH5aWXXkqSPPfcc2Zfgzoz/02bNmXy5MlpbW3N2WefnZkzZ3Yc42u/\n9nRm9ps3b86UKVPS2tqaMWPG5Mknn+w4xuxr09KlSzN+/PicffbZaW1tzU9/+tMkyRtvvJGLL744\nZ5xxRiZOnJi33nqr45gZM2Zk9OjROfPMM/Poo492XO5zoLZ0dvZr1qzJ+PHj89GPfjTf+973trkt\ns689nZ3/H//4x5x//vk599xzM3bs2PzpT3/quK1Oz7+qcU899VT1wgsvVOecc07HZWPHjq2eeuqp\nqqqq6q677qqmT59eVVVVLVq0aJt17zZu3Ljq2Wefraqqqr7whS9UjzzySBfvnN2hM/Nvb2+vWltb\nq4ULF1ZVVVVr1qyptm7dWlWV+deizsz+3RYuXFh98pOf7Hjb7GtTZ+Y/e/bs6sorr6yqqqo2bNhQ\nnXbaadXixYurqjL/WtSZ2d92223VlClTqqqqqlWrVlVtbW0dx5h9bVq+fHn1wgsvVFVVVWvXrq1G\njx5d/eMf/6huuOGGaubMmVVVVdWMGTOqG2+8saqqqnr55Zer8847r9q8eXP1+uuvV6NGjfJ/f43q\n7OzXr19fPf3009Wdd95ZXXfdddvcltnXns7O/8UXX6yWL19eVVVV/f3vf69OOeWUjtvq7Pxr/i/M\nw4cP3+7lpP75z39m+PDhSZKTTjopDz744HvexooVK7Ju3bocffTRSZIxY8Zk3rx5XbNhdqvOzP/R\nRx/NkUcemaFDhyZJDjnkkNTV1Zl/jdrVr/25c+fmrLPOSuJrv5Z1Zv6NjY1Zv359tmzZkg0bNmS/\n/fbLQQcdZP41amdm/9BDDyVJXnnllZxwwglJkr59+6ZPnz7529/+ZvY1rH///jnqqKOSJAceeGAG\nDx6cZcuWZf78+Wlra0uStLW1dczz4YcfzllnnZWGhoYMGjQoRxxxRBYsWOBzoAZ1dvYHHHBAjjvu\nuOy3337b3I7Z16bOzv/II49M//79kyQf/vCHs3HjxmzevHmX5l/zwVwyZMiQzJ8/P0ly3333ZenS\npR3XLVq0KG1tbfn85z+fP//5z0mSZcuWpbm5uWNNU1NTli1btmc3zW6zo/m/+uqrSZKJEyfm/PPP\nz49//OMk5r8vea+v/Xfce++9Oeecc5KY/b5mR/M/5ZRTctBBB+Xkk0/OyJEjM3HixPTp08f89yH/\nOfslS5YkefsHpocffjhbtmzJ66+/nueffz5Lly41+33EokWL8tJLL+WYY47JqlWr0tjYmOTtH6xX\nr16d5O3v8wMGDOg45p1Z+xyobTsz+x0x+9rX2fnff//9GTZsWHr37r1L898ng3natGm5/fbbM3bs\n2Kxfvz69e/dO8vYH8fe//33mzJmTb37zm/nGN76xzfmt7Bt2NP8tW7bkL3/5S2666abcfvvtmTdv\n3jbnM1D7djT7dyxYsCAHHHBAhgwZ0k07pCvtaP6/+c1vsnHjxjz22GOZP39+Zs2alUWLFnXzbtmd\ndjT7sWPHpqmpKePGjcsPfvCDHHfccamv3yd/9Olx1q1bl8svvzxTp07NgQcemLq6um2u/8+32XeY\nfc/W2fm//PLLuemmm3Lttdfu8vts2OUj92If/OAHM2vWrCRv/1XxD3/4Q5Jkv/3263hYxrBhw3L4\n4Yfn1VdfTVNTU8dvo5O3f/PU1NS05zfObrGj+Tc3N+fjH/94DjnkkCTJiBEj8sILL6S1tdX89xE7\nmv075s6d2/HX5SS+9vcxO5r/M888k1GjRqW+vj59+/bNcccdl+eeey4f+9jHzH8fsaPZ9+rVK1Om\nTOlYd+GFF+YDH/hA+vTpY/Y1rL29PZdffnnOO++8jBo1KknSr1+/rFy5Mo2NjVmxYkX69u2bZPvv\n80uXLk1TU5Pv/zWqM7PfEbOvXZ2d/9KlS/O1r30tN9xwQwYNGpRk1+a/T/yataqqbd5+50/xW7du\nzS233JILL7yw4/KtW7cmSV5//fW89tprOfzww9O/f/8cfPDBWbBgQaqqyt13353TTz99z94JdtnO\nzv/kk0/OwoULs3HjxrS3t+epp57KkCFDzL+G7ezs31l73333dZy/nMTsa9x/m/9nPvOZJMmHPvSh\nPP7440mS9evX59lnn83gwYPNv4bt7Nf+v//972zYsCFJ8thjj6V3795mvw+YOnVqhgwZkosuuqjj\nspEjR2b27NlJkjlz5nTMc+TIkbn33nuzadOmjp/9jj76aJ8DNaozs3+3d3/PMPva1Zn5v/nmm7nk\nkkty1VVX5dhjj+1Yvyvzr6v+83+dGjN58uQ88cQTWbNmTRobG3PZZZdl3bp1+fnPf566urqMHj06\nV155ZZLkwQcfzA9/+MP07t07dXV1+frXv55TTz01ydsvLTNlypRs3LgxI0aMyLe+9a3uvFvspM7M\nP3n75YVmzJiRurq6fOITn8jkyZOTmH8t6uzsn3zyydx000258847t7kds69NnZn/pk2bMnXq1Cxc\nuDBVVWXs2LGZMGFCEvOvRZ2Z/eLFizNx4sT06tUrTU1Nuf766zvOZzX72vT000/nc5/7XIYOHZq6\nurrU1dXliiuuyNFHH51JkyZlyZIlaWlpyfTp0zueHG7GjBn59a9/nYaGhlx99dU5+eSTk/gcqDW7\nMvuRI0dm3bp12bx5c/r06ZNZs2Zl8ODBZl+DOjv/W265JTNnzswHPvCBVFWVurq6zJo1K3379u30\n/Gs+mAEAAKAr7BMPyQYAAIDdTTADAABAgWAGAACAAsEMAAAABYIZAAAACgQzAAAAFDR09wYAgP/u\nggsuyObNm7Np06a8+uqrGTp0aJLkqKOOyrRp07p5dwCwb/I6zABQQxYvXpxx48bl8ccf7/SxW7du\nTX29B5cBwM7yF2YAqHF33XVX7rzzzmzZsiWHHHJIvvvd7+b9739/fvWrX+X+++/P+973vrz22mu5\n+eab8+1vfzvHHnts/vrXv2bJkiW56KKLcuihh+aOO+7IypUrM2XKlIwaNaq77xIA7BUEMwDUsCef\nfDLz5s3LHXfckYaGhvzud7/L1VdfnZ/97GdJkmeffTb33HNPBgwY0HHMihUrcvvtt2fZsmUZPXp0\nJk6cmF/84hd55plnMnnyZMEMAP+PYAaAGvbwww/nxRdfzKc//elUVZWqqrJhw4aO64cPH75NLCfJ\npz71qSRJU1NTDj744IwePTpJMmzYsCxZsiRbtmxJr1699tydAIC9lGAGgBpWVVUuuOCCfPWrXy1e\nf+CBB2532f7779/x7169enW8/U4kC2YAeJtn/gCAGvPu5+s87bTTcvfdd2f58uVJ3n5ir+eff363\n3DYA9HT+wgwANaaurq7j3yeccEIuvfTSXHLJJamqKu3t7TnzzDMzbNiw/3rszrwNAD2Zl5UCAACA\nAg/JBgAAgALBDAAAAAWCGQAAAAoEMwAAABQIZgAAACgQzAAAAFAgmAEAAKDg/wDw4og/a9Kp3gAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c3a0987d0>"
      ]
     },
     "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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8c3a0ba990>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VAAAkrO6BKb33Ya+8Hpf276yS3WazOhIAYIlQVgEAQEKang2rpS0gSWpq8Csj\n3WVxIgDAUqKsAgCAhGOYpt5o69B0MKzdG0tUVphpdSQAwBKjrAIAgITTdrlPXfcnVVOao7raQqvj\nAAAsQFkFAAAJ5W7vhM5c61e21619OyplY50qAKQkyioAAEgYE9MhHTndIbvdpoO7quVJc1odCQBg\nEcoqAABICFHDUHNbQLOhiPZsKVPRigyrIwEALERZBQAACeEPH/Sqb2hatZV52rTaZ3UcAIDFKKsA\nAMByN7tGdfHmgFZkp2tvXQXrVAEAlFUAAGCt0ck5vXWuS06HXQcb/XK7HFZHAgAkAMoqAACwTDhi\n6PCpuwqFo9pbVyFfrsfqSACABEFZBQAAljlxsUeDY0FtqPHpCf8Kq+MAABIIZRUAAFjiamBYl+8M\nqTAvQ89tK7M6DgAgwVBWAQDAkhscC+rt9m65XQ41NfrldHBLAgD4LK4MAABgSc2Fozp8KqBI1NC+\nHZXKy0qzOhIAIAFRVgEAwJIxTVPH2rs0OjmrbesKtaY81+pIAIAERVkFAABL5tJHQ7rROaoSn1dP\nbyqxOg4AIIFRVgEAwJLoG57Wifd75Elz6kCjXw47tyEAgIfjKgEAAOIuOBdRc2tAhmHqpZ1Vys5w\nWx0JAJDgKKsAACCuTNPU0bOdmpgOqX59kfzF2VZHAgAkAcoqAACIq/brA7pzb1yVRVlqeLLY6jgA\ngCRBWQUAAHHTPTCl9z7sldfj0v6dVbLbbFZHAgAkCcoqAACIi6mZkFraApKkpga/MtJdFicCACQT\nyioAAIg5wzT1b+/c0nQwrKc3laisMNPqSACAJOO0OgAAAEhs9wan1HalX5GoMe/3hCJRjU6Ftaos\nR0+tK4xjOgDAckVZBQAADzUxE9J/vndXwbnIgt9bVZqrF7ZXyMY6VQDAY6CsAgCALxQ1DLW0BhSc\ni+i5beXasqZgQe8vKMjS4OBknNIBAJY71qwCAIAvdPJSr3qHprWuMk+bV/usjgMASDGUVQAA8Dm3\nusd04caA8rLStbeOj/ICAJYeZRUAAHzG6OScjp7tlNNh18FdfqW5HFZHAgCkIMoqAAD4RCRqqLk1\noFA4qufrylWQ67E6EgAgRVFWAQDAJ45f6NHA6Iw21Pi03p9vdRwAQAqjrAIAAEnStcCILt8ZUmGe\nR3u2llkdBwCQ4iirAABAQ2NBHWvvktvlUFNjtVxObhEAANbiSgQAQIoLhaNqbg0oEjW0b0el8rLS\nrI4EAABZJgELAAAgAElEQVRlFQCAVGaapo61d2l4YlZb1xZqTXmu1ZEAAJBEWQUAIKVduj2k652j\nKvZ59czmEqvjAADwCcoqAAApqn9kRicu9ijd7VRTg18OO7cFAIDEwVUJAIAUFJyLqLk1IMMw9dLO\nKmV73VZHAgDgMyirAACkGNM09da5To1Pzan+iSJVl2RbHQkAgM+hrAIAkGLO3xjQ7Z5xVazMUsOG\nYqvjAADwhSirAACkkJ6BKZ281Cuvx6X9DVWy22xWRwIA4AtRVgEASBEzs2E1twUkSQca/PKmuyxO\nBADAw1FWAQBIAYZpqqWtQ9PBsHZtLFZ5YabVkQAA+FKUVQAAUkDblT513Z9UTWmOtteutDoOAACP\nRFkFAGCZC/RN6MzVfmV73dq3o1I21qkCAJIAZRUAgGVsYiakI6c7ZLfb1NTolyfNaXUkAADmhbIK\nAMAyFTUMtbQGFJyLaM+WMhXne62OBADAvFFWAQBYpk5e6lXv0LTWVeZp02qf1XEAAFgQyioAAMvQ\nre4xXbgxoLysdO2tq2CdKgAg6VBWAQBYZkYn53T0bKecDrsO7vIrzeWwOhIAAAtGWQUAYBmJRA01\ntwYUCkf1fF25CnI9VkcCAOCxUFYBAFhGjl/o0cDojDbU+LTen291HAAAHhtlFQCAZeJaYESX7wyp\nMM+jPVvLrI4DAMCiPPJha6FQSH/xF3+hcDiscDisPXv26O///u81Pj6u73//+7p3757Kysr0i1/8\nQllZWUuRGQAA/ImhsaCOtXfJ7XKoqbFaLid/jwYAJLdHXsncbrdef/11HT58WC0tLTp79qwuXryo\n1157TfX19Tp27Ji2b9+uX/3qV0uRFwAA/IlQOKrm1oAiUUP7dlQqLyvN6kgAACzavP7s6vE82Jwh\nFArJMAzl5OToxIkTOnTokCTp0KFDOn78ePxSAgCAL2Sapo61d2l4YlZb1xZqTXmu1ZEAAIiJeZVV\nwzB08OBBNTQ0qK6uTqtWrdLw8LB8vgcPGC8oKNDIyEhcgwIAgM+7dHtI1ztHVezz6pnNJVbHAQAg\nZh65ZlWS7Ha7Dh8+rKmpKX3nO9/RuXPnPvdwcR42DgDA0uofmdGJiz1KdzvV1OCXw846VQDA8jGv\nsvpfMjMztXv3bl29elX5+fkaGhqSz+fT4OCgVqxYMa9jFBSwCVOq4tynNs5/auP8x97MbFi/O3ZT\nTqdD337xK6qpyLM60hfi3Kc2zn/q4twjFh5ZVkdGRuRyuZSVlaXZ2VmdPn1a3/ve9/Tss8/qP//z\nP/Xqq6/qt7/9rfbs2TOvAQcHJxcdGsmnoCCLc5/COP+pjfMfe6Zp6ren7mpgeFr164uU53Em5P/H\nnPvUxvlPXZz71BbLP1Q8sqwODg7qhz/8oUzTlGEYampqUn19vWpra/V3f/d3+o//+A+VlpbqF7/4\nRcxCAQCAhzt/Y0C3e8ZVsTJLDU8WWx0HAIC4eGRZXbt2rX77299+7uu5ubn69a9/HY9MAADgIboH\npnTyUq+8Hpf2N1TJzp4RAIBlip0YAABIEjOzYbW0BSRJBxr88qa7LE4EAED8UFYBAEgChmmqpa1D\n08Gwdm8sUXlhptWRAACIK8oqAABJoO1Kn7ruT6qmNEd1tYVWxwEAIO4oqwAAJLhA34TOXO1Xttet\nfTsqebY5ACAlUFYBAEhgEzMhHTndIbvdpqZGvzxpC3pEOgAASYuyCgBAgooahlpaAwrORbRnS5mK\n871WRwIAYMlQVgEASFAnL/Wqd2ha6yrztGm1z+o4AAAsKcoqAAAJ6Fb3mC7cGFBeVrr21lWwThUA\nkHIoqwAAJJjRyTkdPdspp8Oug7v8SnM5rI4EAMCSo6wCAJBAwhFDza13FQpH9XxduQpyPVZHAgDA\nEpRVAAASyImLPRoYDWpDjU/r/flWxwEAwDKUVQAAEsTVwLAu3xlSYZ5He7aWWR0HAABLUVYBAEgA\nQ2NBvd3eLbfLoabGarmcXKIBAKmNKyEAABYLhaM63BpQJGrohe0VystKszoSAACWo6wCAGAh0zR1\nrL1LIxOz2rq2UGsr8qyOBABAQqCsAgBgoUsfDel656iKfV49s7nE6jgAACQMyioAABbpG57Wifd7\nlO52qqnBL4edyzIAAP+FqyIAABYIzkXU3BqQYZh6aWeVsr1uqyMBAJBQKKsAACwx0zR19GynJqZD\nqn+iSNUl2VZHAgAg4VBWAQBYYu3XB3Tn3rgqVmapYUOx1XEAAEhIlFUAAJZQ98CU3vuwV16PS/sb\nqmS32ayOBABAQqKsAgCwRGZmw2ppC0iSDjT45U13WZwIAIDERVkFAGAJGKaplrYOTQfD2rWxWOWF\nmVZHAgAgoVFWAQBYAm1X+tR1f1I1pTnaXrvS6jgAACQ8yioAAHEW6JvQmav9yva6tW9HpWysUwUA\n4JEoqwAAxNHETEhHTnfIbrepqdEvT5rT6kgAACQFyioAAHESNQy1tAYUnIvo2S1lKs73Wh0JAICk\nQVkFACBOTl7qVe/QtNZV5mnzap/VcQAASCqUVQAA4uBW95gu3BhQXla69tZVsE4VAIAFoqwCABBj\no5NzOnq2U06HXQd3+ZXmclgdCQCApENZBQAghiJRQ82tAYXCUT1fV66CXI/VkQAASEqUVQAAYuj4\nhR4NjM5oQ41P6/35VscBACBpUVYBAIiRa4ERXb4zpMI8j/ZsLbM6DgAASY2yCgBADAyNBXWsvUtu\nl0NNjdVyObnEAgCwGFxJAQBYpFA4qubWgCJRQ/t2VCovK83qSAAAJD3KKgAAi2Capo61d2l4YlZb\n1xZqTXmu1ZEAAFgWKKsAACzCpdtDut45qmKfV89sLrE6DgAAywZlFQCAx9Q/MqMTF3uU7naqqcEv\nh53LKgAAscJVFQCAxxCci6i5NSDDMPXSziple91WRwIAYFmhrAIAsECmaeqtc50an5pT/RNFqi7J\ntjoSAADLDmUVAIAFOn9jQLd7xlWxMksNG4qtjgMAwLJEWQUAYAF6BqZ08lKvvB6X9jdUyW6zWR0J\nAIBlibIKAMA8zcyG1dwWkCQdaPDLm+6yOBEAAMsXZRUAgHkwTFMtbR2aDoa1a2OxygszrY4EAMCy\nRlkFAGAe2q70qev+pGpKc7S9dqXVcQAAWPYoqwAAPEKgb0JnrvYr2+vWvh2VsrFOFQCAuKOsAgDw\nJSZmQjpyukN2u01NjX550pxWRwIAICVQVgEAeIioYailNaDgXER7tpSpON9rdSQAAFIGZRUAgIc4\nealXvUPTWleZp02rfVbHAQAgpVBWAQD4Are6x3ThxoDystK1t66CdaoAACwxyioAAH9idHJOR892\nyumw6+Auv9JcDqsjAQCQciirAAB8SiRqqLk1oFA4qufrylWQ67E6EgAAKYmyCgDApxy/0KOB0Rlt\nqPFpvT/f6jgAAKQsyioAAB+7FhjR5TtDKszzaM/WMqvjAACQ0iirAABIGhoL6lh7l9wuh5oaq+Vy\ncokEAMBKXIkBACkvFI6quTWgSNTQvh2VystKszoSAAApj7IKAEhppmnqWHuXhidmtXVtodaU51od\nCQAAiLIKAEhxl24P6XrnqIp9Xj2zucTqOAAA4GOUVQBAyuofmdGJiz1KdzvV1OCXw85lEQCARMFV\nGQCQkoJzETW3BmQYpl7aWaVsr9vqSAAA4FMoqwCAlGOapt4616nxqTnVP1Gk6pJsqyMBAIA/QVkF\nAKSc8zcGdLtnXBUrs9SwodjqOAAA4AtQVgEAKaVnYEonL/XK63Fpf0OV7Dab1ZEAAMAXoKwCAFLG\nzGxYzW0BSdKBBr+86S6LEwEAgIehrAIAUoJhmmpp69B0MKxdG4tVXphpdSQAAPAlKKsAgJTQdqVP\nXfcnVVOao+21K62OAwAAHoGyCgBY9gJ9EzpztV/ZXrf27aiUjXWqAAAkPMoqAGBZm5gJ6cjpDtnt\nNjU1+uVJc1odCQAAzANlFQCwbEUNQy2tAQXnInp2S5mK871WRwIAAPNEWQUALFsnL/Wqd2ha6yrz\ntHm1z+o4AABgASirAIBl6Vb3mC7cGFBeVrr21lWwThUAgCRDWQUALDujk3M6erZTToddB3f5leZy\nWB0JAAAsEGUVALCsRKKGmlsDCoWjer6uXAW5HqsjAQCAx0BZBQAsK8cv9GhgdEYbanxa78+3Og4A\nAHhMj9y/v7+/Xz/4wQ80PDwsu92ub37zm/r2t7+tX/7yl/r3f/935ec/uBH4/ve/r927d8c9MAAA\nD3MtMKLLd4ZUmOfRnq1lVscBAACL8Miy6nA49KMf/Ui1tbWanp7Wn/3Zn2nnzp2SpJdfflkvv/xy\n3EMCAPAoQ2NBHWvvktvlUFNjtVxOPjwEAEAye2RZLSgoUEFBgSTJ6/WqpqZGAwMDkiTTNOObDgCA\neQiFo2puDSgSNXRwZ7XystKsjgQAABZpQX927unp0Y0bN7RhwwZJ0m9+8xs1NTXpJz/5iSYnJ+MS\nEACAL2Oapo61d2l4YlZb1xZqTXmu1ZEAAEAMzLusTk9P62//9m/14x//WF6vV9/61rd04sQJNTc3\ny+fz6Wc/+1k8cwIA8IUu3R7S9c5RFfu8emZzidVxAABAjNjMeXyWNxKJ6G/+5m+0e/du/dVf/dXn\nvn/v3j1997vf1RtvvBGXkAAAfJF7g1P6l/+4rDS3Q9/7xibl8vFfAACWjUeuWZWkH//4x1q1atVn\niurg4OAna1nfeecdrVmzZl4DDg7yceFUVFCQxblPYZz/1Bav8x+ci+j1Yzc1OxfWS/WVCs+GNDgb\nivk4eHz8209tnP/UxblPbQUFWTE71iPL6sWLF/XGG29ozZo1OnjwoGw2m77//e/ryJEjun79uux2\nu0pLS/XTn/40ZqEAAPgypmnqrXOdGp+aU/0TRaouybY6EgAAiLFHltWtW7fq+vXrn/s6z1QFAFjl\n/I0B3e4ZV8XKLDVsKLY6DgAAiAMeQgcASCrdA1M6ealXXo9L+xuqZLfZrI4EAADigLIKAEgaM7Nh\ntbQFJEkHGvzyprssTgQAAOKFsgoASAqGaaqlrUPTwbB2bSxWeWGm1ZEAAEAcUVYBAEmh7Uqfuu5P\nqqY0R9trV1odBwAAxBllFQCQ8AJ9EzpztV/ZXrf27aiUjXWqAAAse5RVAEBCm5gJ6cjpDtntNjU1\n+uVJm9cjwgEAQJKjrAIAElbUMNTSGlBwLqJnt5SpON9rdSQAALBEKKsAgIR18lKveoemta4yT5tX\n+6yOAwAAlhBlFQCQkG51j+nCjQHlZaVrb10F61QBAEgxlFUAQMIZnZzT0bOdcjrsOrjLrzSXw+pI\nAABgiVFWAQAJJRI11NwaUCgc1fNPlasg12N1JAAAYAHKKgAgoRy/0KOB0Rk9WZOv9dX5VscBAAAW\noawCABLGtcCILt8ZUkGuR89tLbc6DgAAsBBlFQCQEIbGgjrW3iW3y6GmRr9cTi5RAACkMu4EAACW\nC4Wjam4NKBI1tG9HpVZkp1sdCQAAWIyyCgCwlGmaOtbepeGJWW1dW6g15blWRwIAAAmAsgoAsNSl\n20O63jmqYp9Xz2wusToOAABIEJRVAIBl+kdmdOJij9LdTjU1+OWwc1kCAAAPcFcAALBEcC6i5taA\nDMPUSzurlO11Wx0JAAAkEMoqAGDJmaapt851anxqTvVPFKm6JNvqSAAAIMFQVgEAS+78jQHd7hlX\nxcosNWwotjoOAABIQJRVAMCS6h6Y0slLvfJ6XNrfUCW7zWZ1JAAAkIAoqwCAJTMzG1ZLW0CSdKDB\nL2+6y+JEAAAgUVFWAQBLwjBNtbR1aDoY1u6NJSovzLQ6EgAASGCUVQDAkmi70qeu+5OqKc1RXW2h\n1XEAAECCo6wCAOIu0DehM1f7le11a9+OStlYpwoAAB6BsgoAiKvxqTkdOd0hu92mpka/PGlOqyMB\nAIAkQFkFAMRN1DD0/7x9U8G5iPZsKVNxvtfqSAAAIElQVgEAcXPyUq+6+ie1rjJPm1b7rI4DAACS\nCGUVABAXt7rHdOHGgHy56dpbV8E6VQAAsCAsHAIAxNzo5JyOnu2U02HXt/auk8MwrI4EAACSDDOr\nAICYikQNNbcGFApH9XxduYpYpwoAAB4DZRUAEFPHL/RoYHRGG2p8Wu/PtzoOAABIUpRVAEDMXAuM\n6PKdIRXmebRna5nVcQAAQBKjrAIAYmJoLKhj7V1yuxxqaqyWy8klBgAAPD7uJAAAixYKR9XcGlAk\namjfjkrlZaVZHQkAACQ5yioAYFFM09Sx9i4NT8xq27pCrSnPtToSAABYBiirAIBFuXR7SNc7R1Xi\n8+rpTSVWxwEAAMsEZRUA8Nj6R2Z04mKP0t1OHWjwy2HnsgIAAGKDuwoAwGMJzkXU3BqQYZja31Cl\nbK/b6kgAAGAZoawCABbMNE29da5T41Nzql9fJH9xttWRAADAMkNZBQAs2PkbA7rdM66KlVlqeLLY\n6jgAAGAZoqwCABakZ2BKJy/1yutxaX9Dlew2m9WRAADAMkRZBQDM28xsWM1tAUnSgQa/vOkuixMB\nAIDlirIKAJgXwzT1xukOTQfD2r2xROWFmVZHAgAAyxhlFQAwL21X+tTZP6ma0hzV1RZaHQcAACxz\nlFUAwCMF+iZ05mq/sr1u7dtRKRvrVAEAQJxRVgEAX2piJqQjpztkt9vU1OiXJ81pdSQAAJACKKsA\ngIeKGoZaWgMKzkW0Z0uZivO9VkcCAAApgrIKAHiok5d61Ts0rXWVedq02md1HAAAkEIoqwCAL3Sr\ne0wXbgwoLytde+sqWKcKAACWFGUVAPA5o5NzOnq2U06HXQd3+ZXmclgdCQAApBjKKgDgMyJRQ82t\nAYXCUT1fV66CXI/VkQAAQAqirAIAPuP4hR4NjM5oQ41P6/35VscBAAApirIKAPjEtcCILt8ZUmGe\nR3u2llkdBwAApDDKKgBAkjQ0FtSx9i65XQ41NVbL5eQSAQAArMOdCABAoXBUza0BRaKG9u2oVF5W\nmtWRAABAiqOsAkCKM01Tx9q7NDwxq61rC7WmPNfqSAAAAJRVAEh1l24P6XrnqIp9Xj2zucTqOAAA\nAJIoqwCQ0vpHZnTiYo/S3U41NfjlsHNZAAAAiYG7EgBIUcG5iJpbAzIMUy/trFK21211JAAAgE9Q\nVgEgBZmmqbfOdWp8ak71TxSpuiTb6kgAAACfQVkFgAQSnIuo7UqfJqZDcR3n/I0B3e4ZV8XKLDVs\nKI7rWAAAAI+DsgoACcI0Tb1xukNtV/r0HyfvKBwx4jJOz8CUTl7qlTfdpf0NVbLbbHEZBwAAYDEo\nqwCQIM5c61dH34TSXA4NjgV14mJPzMeYmQ2ruS0gSTrQ6Jc33RXzMQAAAGKBsgoACaCzf1Ktl/uU\nleHWKy/WqjAvQ5fvDOlqYDhmYxgfz9xOB8PatbFY5YWZMTs2AABArFFWAcBiU8GwWtoCstttOtDo\nV1aGW02NfrldDr3d3q3BsWBMxmm70qfO/knVlOZoe+3KmBwTAAAgXiirAGAhwzDV0hpQcC6iZzaV\nqtTnlSTlZaVp345KRaKGDp8KaC4cXdQ4gb4Jnbnar2yvW/t2VMrGOlUAAJDgKKsAYKH3PuxVz+CU\n1pTnauvags98b015rp5aV6jRyVkda++SaZqPNcbETEhHTnfIbrepqdEvT5ozFtEBAADiirIKABa5\n3TOu9uv3lZeVphceMtu5e1OJSn2ZutE5qg8+GlrwGFHD+GTm9tktZSrO98YiOgAAQNxRVgHAAmNT\nc3rzTIccDruadlUrzeX4wtc57Hbtb6ySJ82p37/fo77h6QWNc/JSr3qHprWuMk+bV/tikBwAAGBp\nUFYBYIlFog9mO+fCUX1tW7kKcz1f+vrsDLde2lklwzDV/PEs6Xzc6h7ThRsDystK1966CtapAgCA\npEJZBYAl9vv376l/ZEbrq/O1oSZ/Xu/xF2dr5/piTUyHdPRs5yPXr45Ozuno2U45HXYd3OV/6Mwt\nAABAoqKsAsASut4xoksfDcqX69HXtpUv6L07nyxSZVGW7twb17nr9x/6ukjUUHNrQKFwVM8/Va6C\nR8zcAgAAJCLKKgAskaHxoN5q75LL6dDBRr9czoX9CrbbbNq/s0pej0unPuxT98DUF77u+IUeDYzO\n6MmafK2vnt/MLQAAQKJ55J1Sf3+//vIv/1Ivvvii9u/fr9dff12SND4+rldeeUV79+7Vd77zHU1O\nTsY9LAAkq3AkquZTAUUihl7YXqEV2emPdZyMdJeaGvySpJa2gKZnw5/5/rXAiC7fGVJBrkfPbV3Y\nzC0AAEAieWRZdTgc+tGPfqQ333xT//Zv/6Z//dd/1Z07d/Taa6+pvr5ex44d0/bt2/WrX/1qKfIC\nQNIxTVNvn+/W8MSsNq8p0LrKvEUdr6wwU09vKtF0MKw32jpkfLx+dWgsqGPtXXK7HDq4q3rBM7cA\nAACJ5JF3MgUFBaqtrZUkeb1e1dTU6P79+zpx4oQOHTokSTp06JCOHz8e36QAkKQ+vDOsa4ERFeV7\n9dXNpTE55lPrCrWqLEdd9yfVdrlPoXBUza0BRaKG9u2oVF5WWkzGAQAAsMqC/uze09OjGzduaOPG\njRoeHpbP9+CZfQUFBRoZGYlLQABIZvdHZnTiQrfS3U41/f/t3Xlw1Pd9x//XXrrvG90LBGNjYw7Z\ngBC2OWzMIQQm03E7TfKz3brtTOK2cdupSTvTSZq2k048mU5mMvavnsmvTdJMJ7ERBhxjYzsgcWMM\nNgbCsTqRhA50S6s9Pr8/SFQTG9Cx4rvH8/FPotV+v98Xee9uvi9997Nb5ZbTEZqrnTabTRuXlyk9\nJV6Hz7brf9+/pO7+US29J0/zSjJCcgwAAAArTfisaWhoSC+88IJ27Nih5OTkz31fH9/fBwA3847d\nuNoZCBptWlGm9OS4kO7/dwXYbrfpateQZuUk67HFhSE9BgAAgFWcE7mT3+/XCy+8oJqaGq1bt06S\nlJ2dra6uLuXk5Kizs1NZWVkTOmBuburU0yKiMfvYFmvzN8bop2+f15A3oHXLyrR8UfGMHCc3N1V/\nZLPrxPkObV/9JWWE6dt/Y23++D/MPrYx/9jF7BEKEyqrO3bs0Ny5c/W1r31t/LY1a9bo9ddf1/PP\nP6833nhDa9eundABOzv51OBYlJubyuxjWCzO//j5azp94ZqK81K0yJ01o//+wswEbVlRJt/omDpH\nx2bsOFMVi/PHDcw+tjH/2MXsY1so/1Bxx7cBnzx5Um+++aaOHDmirVu3atu2bTpw4ID+9E//VIcO\nHdL69et15MgRPf/88yELBQCRrLVzUB+calVSgktbVt54my4AAAAm545XVpcuXapz58594e9+/OMf\nhzoPAES04VG/ausbZIxRdWW5UhJdVkcCAACISHwJHwCEiDFGuw83aHB4TKsWFqqsgPU6AAAAU0VZ\nBYAQOfRJuxra+uUuTNPyBflWxwEAAIholFUACIHG9gHVf9ym1KQ4bV5Rztd5AQAATBNlFQCmaXDE\np131HtntNm2pcisxfkIftA4AAIDboKwCwDQEg0a76jwa8fq1enGRinKSrY4EAAAQFSirADANB05f\nVUvnoO4pzdSSeblWxwEAAIgalFUAmKJLLX06dq5DmanxenJZKetUAQAAQoiyCgBT0Dvo1Z7DDXI4\n7KpZNVvxLofVkQAAAKIKZRUAJskfCGpXnUdeX0CPV5QoLyPR6kgAAABRh7IKAJP03oetau8Z1v2z\ns7VwTrbVcQAAAKISZRUAJuFcQ48+utipnIxEPV5RYnUcAACAqEVZBYAJ6u4b1VvHmuRyOrS1yi2X\nk5dQAACAmcKZFgBMgM8f0M66K/L7g9qwrFRZaQlWRwIAAIhqlFUAuANjjPYdb1Z336gWz8vV/LJM\nqyMBAABEPcoqANzBmcvdOuvpUUF2slYvLrI6DgAAQEygrALAbXRcH9a7J5qVEOdUTZVbTgcvmwAA\nAHcDZ10AcAvesYBqD3oUCBptWlGm9OQ4qyMBAADEDMoqAHwBY4z2HmlU76BXy+7L15yidKsjAQAA\nxBTKKgB8gRMXOnWxpVfFeSlatbDQ6jgAAAAxh7IKAL+ntXNQH5xqVVKCS1tWumW326yOBAAAEHMo\nqwDwGcOjftXWN8gYo+rKcqUkuqyOBAAAEJMoqwDwW8YY7T7coMHhMa1aWKiyglSrIwEAAMQsyioA\n/NahT9rV0NYvd2Gali/ItzoOAABATKOsAoCkxvYB1X/cptSkOG1eUS6bjXWqAAAAVqKsAoh5gyM+\n7ar3yG63aUuVW4nxTqsjAQAAxDzKKoCYFgwa7arzaMTr12OLilSUk2x1JAAAAIiyCiDGHTh9VS2d\ng5pXkqGl9+RaHQcAAAC/RVkFELMutfTp2LkOZaTGa8OyMtapAgAAhBHKKoCY1Dvo1Z7DDXI47Npa\n5VZ8nMPqSAAAAPgMyiqAmOMPBLWrziOvL6DHK4qVl5lkdSQAAAD8HsoqgJjz3oetau8Z1gJ3lh6Y\nnW11HAAAAHwByiqAmHKuoUcfXexUTkainniolHWqAAAAYYqyCiBmdPeN6q1jTXI5Hdpa5ZbLyUsg\nAABAuOJMDUBM8PkD2ll3RX5/UBuWlSorLcHqSAAAALgNyiqAqGeM0b7jzeruG9XiebmaX5ZpdSQA\nAADcAWUVQNQ7c7lbZz09KshO1urFRVbHAQAAwARQVgFEtY7rw3r3RLMS4pyqqXLL6eBlDwAAIBJw\n1gYgannHAqo96FEgaLRpRZnSk+OsjgQAAIAJoqwCiErGGO090qjeQa+W3ZevOUXpVkcCAADAJFBW\nAUSlkxc6dbGlV8V5KVq1sNDqOAAAAJgkyiqAqNPaOaj3T7UqKcGlLSvdstttVkcCAADAJFFWAUSV\n4VG/ausbZIxRdWW5UhJdVkcCAADAFFBWAUQNY4x2H27Q4PCYVi0sVFlBqtWRAAAAMEWUVQBR4/DZ\ndn+WF5AAACAASURBVDW09ctdmKblC/KtjgMAAIBpoKwCiAqN7QOqO9Om1KQ4bV5RLpuNdaoAAACR\njLIKIOINjvi0q94ju92mLVVuJcY7rY4EAACAaaKsAohowaDRrjqPRrx+PbaoSEU5yVZHAgAAQAhQ\nVgFEtAOnr6qlc1DzSjK09J5cq+MAAAAgRCirACLWpZY+HTvXoYzUeG1YVsY6VQAAgChCWQUQkXoH\nvdpzuEEOh11bq9yKj3NYHQkAAAAhRFkFEHH8gaB21Xnk9QX0eEWJ8jKTrI4EAACAEKOsAog4733Y\nqvaeYd0/O1sL52RbHQcAAAAzgLIKIKKca+jRRxc7lZORqMcrSqyOAwAAgBlCWQUQMbr7RvXWsSa5\nnA5trXLL5eQlDAAAIFpxpgcgIvj8Ae2suyK/P6gNy0qVlZZgdSQAAADMIMoqgLBnjNG+483q7hvV\n4nm5ml+WaXUkAAAAzDDKKoCwd+Zyt856elSQnazVi4usjgMAAIC7gLIKIKx1XB/WuyealRDnVE2V\nW04HL1sAAACxgLM+AGHLOxZQ7UGPAkGjTSvKlJ4cZ3UkAAAA3CWUVQBhyRijvUca1Tvo1bL78jWn\nKN3qSAAAALiLKKsAwtLJC5262NKr4rwUrVpYaHUcAAAA3GWUVQBhp7VzUO+falVSgktbVrplt9us\njgQAAIC7jLIKIKwMj/pVW98gY4yqK8uVkuiyOhIAAAAsQFkFEDaMMdp9uEGDw2NatbBQZQWpVkcC\nAACARSirAMLGoU/a1dDWL3dhmpYvyLc6DgAAACxEWQUQFhrbB1T/cZtSk+K0eUW5bDbWqQIAAMQy\nyioAyw2O+LSr3iO73aYtVW4lxjutjgQAAACLUVYBWCoYNNpV59GI16/HFhWpKCfZ6kgAAAAIA5RV\nAJY6cPqqWjoHNa8kQ0vvybU6DgAAAMIEZRWAZS619OnYuQ5lpMZrw7Iy1qkCAABgHGUVgCV6B73a\nc7hBDoddW6vcio9zWB0JAAAAYYSyCuCu8weC2lXnkdcX0OMVJcrLTLI6EgAAAMIMZRXAXffeh61q\n7xnW/bOztXBOttVxAAAAEIYoqwDuqnMNPfroYqdyMhL1eEWJ1XEAAAAQpiirAO6a7r5RvXWsSS6n\nQ1ur3HI5eQkCAADAF7vjmeKOHTtUWVmp6urq8dt++MMf6pFHHtG2bdu0bds2HThwYEZDAoh8Pn9A\nO+uuyO8PasOyUmWlJVgdCQAAAGHMeac7PPXUU/rKV76iv/u7v7vp9meeeUbPPPPMjAUDED2MMdp3\nvFndfaNaPC9X88syrY4EAACAMHfHK6sVFRVKS0v73O3GmBkJBCD6HD/XobOeHhVkJ2v14iKr4wAA\nACACTHnB2E9+8hPV1NToW9/6lgYGBkKZCUAU6egZ1psHryghzqmaKrecDtapAgAA4M6mdNb4R3/0\nR9q/f79qa2uVk5Ojf/3Xfw11LgBRwDsWUG2dR4GA0aYVZUpPjrM6EgAAACLEHdesfpGsrKzx//4H\nf/AH+vM///MJb5ubmzqVQyIKMPvYYozRT98+ryFvQI8uKdbyRcVWR4KFeP7HLmYf25h/7GL2CIUJ\nldXfX5/a2dmp3NxcSdI777yjefPmTfiAnZ28ZTgW5eamMvsYc/z8NZ2+cE3FeSla93Ap849hPP9j\nF7OPbcw/djH72BbKP1Tcsay++OKLOnr0qHp7e/XYY4/pG9/4ho4ePapz587JbrerqKhI3/72t0MW\nCEDka+0c1AenWpWU4NKWlW457DarIwEAACDC3LGsfv/73//cbdu3b5+RMAAi3/CoX7X1DTLGaMvK\ncqUkuqyOBAAAgAjEx3ICCBljjHYfbtDg8JhWPVio0nzWqwAAAGBqKKsAQubQJ+1qaOuXuzBNy+/L\ntzoOAAAAIhhlFUBINLYPqP7jNqUmxWnzinLZbKxTBQAAwNRRVgFM2+CIT7vqPbLbbaqpcisxfkrf\nigUAAACMo6wCmJZg0GhXnUcjXr9WLy5SYU6y1ZEAAAAQBSirAKblwOmraukc1D2lmVoyL9fqOAAA\nAIgSlFUAU3appU/HznUoMzVeTy4rZZ0qAAAAQoayCmBKege92nO4QQ6HXTWrZive5bA6EgAAAKII\nZRXApPkDQe2q88jrC+jxihLlZSRaHQkAAABRhrIKYNLe+7BV7T3DemB2thbOybY6DgAAAKIQZRXA\npJxr6NFHFzuVk5GodRUlVscBAABAlKKsApiw7r5RvXWsSXEuh7ZWueVy8hICAACAmcGZJoAJ8fkD\n2ll3RX5/UE8+XKqstASrIwEAACCKUVYB3JExRvuON6u7b1RL5uVqflmm1ZEAAAAQ5SirAO7ozOVu\nnfX0qCA7WauXFFkdBwAAADGAsgrgtjquD+vdE81KiHOqpsoth52XDQAAAMw8zjoB3JJ3LKDagx4F\ngkabVpQpPTnO6kgAAACIEZRVAF/IGKO9RxrVO+jV8gUFmlOUbnUkAAAAxBDKKoAvdPJCpy629Ko4\nL0VVD8yyOg4AAABiDGUVwOe0dg7q/VOtSkpwactKt+x2m9WRAAAAEGMoqwBuMjzqV219g4wx2rKy\nXCmJLqsjAQAAIAZRVgGMM8Zo9+EGDQ6PadWDhSrNT7U6EgAAAGIUZRXAuMNn29XQ1i93YZqW35dv\ndRwAAADEMMoqAElSY/uA6s60KTUpTptXlMtmY50qAAAArENZBaDBEZ921Xtkt9tUU+VWYrzT6kgA\nAACIcZRVIMYFg0a76jwa8fq1enGRCnOSrY4EAAAAUFaBWHfg9FW1dA7qntJMLZmXa3UcAAAAQBJl\nFYhpl1r6dOxchzJT4/XkslLWqQIAACBsUFaBGNU36NWeww1yOOyqWTVb8S6H1ZEAAACAcZRVIAb5\nA0HV1nnk9QX0eEWJ8jISrY4EAAAA3ISyCsSg9z5sVXvPsO6fna2Fc7KtjgMAAAB8DmUViDHnGnr0\n0cVO5WQk6vGKEqvjAAAAAF+IsgrEkO6+Ub11rEkup0Nbq9xyOXkJAAAAQHjiTBWIET5/QDvrrsjv\nD2rDslJlpSVYHQkAAAC4JcoqEAOMMdp3vFndfaNaPC9X88syrY4EAAAA3BZlFYgBZy5366ynRwXZ\nyVq9uMjqOAAAAMAdUVaBKNdxfVjvnmhWQpxTNVVuOR087QEAABD+OGsFoph3LKDagx4FgkabVpQp\nPTnO6kgAAADAhFBWgShljNHeI43qHfRq+YICzSlKtzoSAAAAMGGUVSBKnbzQqYstvSrOS1HVA7Os\njgMAAABMCmUViEKtnYN6/1SrkhJc2rLSLbvdZnUkAAAAYFIoq0CUGR71q7a+QcYYbVlZrpREl9WR\nAAAAgEmjrAJRxBij3YcbNDg8plUPFqo0P9XqSAAAAMCUUFaBKHL4bLsa2vrlLkzT8vvyrY4DAAAA\nTBllFYgSje0DqjvTptSkOG1eUS6bjXWqAAAAiFyUVSAKDI74tKveI7vdppoqtxLjnVZHAgAAAKaF\nsgpEuGDQaFedRyNev1YvLlJhTrLVkQAAAIBpo6wCEe7Amatq6RzUPaWZWjIv1+o4AAAAQEhQVoEI\ndqmlT8c+7VBmaryeXFbKOlUAAABEDcoqEKF6B73ac7hBDoddNatmK97lsDoSAAAAEDKUVSAC+QNB\n7arzyOsL6PGKEuVlJFodCQAAAAgpyioQgd77sFXtPcO6f3a2Fs7JtjoOAAAAEHKUVSDCnGvo0UcX\nO5WTkajHK0qsjgMAAADMCMoqEEG6+0b11rEmuZwOba1yy+XkKQwAAIDoxJkuECF8/oB21l2R3x/U\nhmWlykpLsDoSAAAAMGMoq0AEMMZo3/FmdfeNavG8XM0vy7Q6EgAAADCjKKtABDhzuVtnPT0qyE7W\n6sVFVscBAAAAZhxlFQhzHdeH9e6JZiXEOVVT5ZbTwdMWAAAA0Y+zXiCMeccCqj3oUSBotGlFmdKT\n46yOBAAAANwVlFUgTBljtPdIo3oHvVp2X77mFKVbHQkAAAC4ayirQJg6eaFTF1t6VZyXolULC62O\nAwAAANxVTqsDANHOGKOO6yMKBs2EtxkYHtP7p1qVlODSlpVu2e22GUwIAAAAhB/KKjCDjDH65a+v\n6MrVviltX11ZrpREV4hTAQAAAOGPsgrMoCNnO3Tlap9mZSerJC9lUtuWFaSqrCB1hpIBAAAA4Y2y\nCsyQxvYBHTxzVSlJcdr+6BwlJfB0AwAAACaKD1gCZsDgiE+76j2y2WyqWVlOUQUAAAAmibIKhFgw\naLSrzqMRr1+rFxepKHdyb/8FAAAAQFkFQu7Amatq6RzUvJIMLb0n1+o4AAAAQESirAIhdKm1T8c+\n7VBGarw2LCuTzcZXzgAAAABTQVkFQqRv0Ku9hxvlsNtUU+VWfJzD6kgAAABAxKKsAiHgDwRVW+fR\n6Jhf6ypKlJ+ZZHUkAAAAIKJRVoEQeP/DVrX3DGuBO0sL52RbHQcAAACIeJRVYJrONV7XqYudyk5P\n0BMPlbBOFQAAAAgByiowDT39o/rV0Sa5nA5trZotl5N1qgAAAEAo3LGs7tixQ5WVlaqurh6/ra+v\nT88++6zWr1+v5557TgMDAzMaEghHPn9QOw965PMH9OTDJcpOT7A6EgAAABA17lhWn3rqKb322ms3\n3fbqq69qxYoVevvtt7Vs2TK98sorMxYQCEfGGO073qSuvhEt/lKu7i3PsjoSAAAAEFXuWFYrKiqU\nlpZ202379+/Xtm3bJEnbtm3Tu+++OzPpgDD18ZVunfX0qCA7WauXFFkdBwAAAIg6U1qz2tPTo5yc\nHElSbm6uenp6QhoKCGfXrg/rnRMtSohzqmZluZwOln4DAAAAoRaSs2w+/RSxwusLaGedR4FAUBtX\nlCk9Jd7qSAAAAEBUck5lo+zsbHV1dSknJ0ednZ3Kypr4er3c3NSpHBJRINJnb4zRz96+oKHRgNY+\nXKYVi4qtjhRRIn3+mB7mH7uYfWxj/rGL2SMUJlRWjTE3/bxmzRq9/vrrev755/XGG29o7dq1Ez5g\nZyefHByLcnNTI372J85f00cXOlScm6LFs7Mi/t9zN0XD/DF1zD92MfvYxvxjF7OPbaH8Q8Ud3wb8\n4osv6umnn5bH49Fjjz2mX/7yl3r++ed16NAhrV+/XkeOHNHzzz8fskBAOGrtGtL7p1qVGO/Uliq3\n7Hbe+g4AAADMpDteWf3+97//hbf/+Mc/DnUWICyNeP3aVeeRMUZbVrqVkuiyOhIAAAAQ9fgYU+A2\njDHafbhBA8Njqlo4S2UFrL8AAAAA7gbKKnAbR852yHO1X+5ZaVqxoMDqOAAAAEDMoKwCt9DYPqCD\nZ64qJSlOm1aU8xVNAAAAwF1EWQW+wOCIT28eapDNZlPNynIlJUzpW54AAAAATBFlFfg9waDRrjqP\nhkd9Wr24SEW5KVZHAgAAAGIOZRX4PQfPXFVL56DmlWRo6T25VscBAAAAYhJlFfiMS619OvpphzJS\n47VhWRnrVAEAAACLUFaB3+ob9Grv4UY57DbVVLkVH+ewOhIAAAAQsyirgCR/IKja+gaNjvm1rqJE\n+ZlJVkcCAAAAYhplFZD0/qlWtXcPaYE7SwvnZFsdBwAAAIh5lFXEvHON13XqN53KTk/QEw+VsE4V\nAAAACAOUVcS0nv5R/epok1xOh7ZWzZbLyTpVAAAAIBxQVhGzfP6gdh70yOcP6MmHS5SdnmB1JAAA\nAAC/RVlFTDLGaN/xJnX1jWjRl3J1b3mW1ZEAAAAAfAZlFTHp4yvdOuvpUUFWktYsKbI6DgAAAIDf\nQ1lFzLl2fVjvnGhRvMuhLVVuOR08DQAAAIBww1k6Yop3LKCddR4FAkFtWlGujJR4qyMBAAAA+AKU\nVcQMY4zeOtqo3gGvHr43X3OL062OBAAAAOAWKKuIGScvdOo3zb0qzk3RIw8WWh0HAAAAwG1QVhET\nWruG9P6pViXGO7Wlyi273WZ1JAAAAAC3QVlF1Bvx+rWrziNjjLasdCsl0WV1JAAAAAB3QFlFVDPG\naPfhBg0Mj2nlA7NUVpBqdSQAAAAAE0BZRVQ7crZDnqv9Kp+Vpsr7C6yOAwAAAGCCKKuIWo3tAzp4\n5qpSkuK0eUW5bDbWqQIAAACRgrKKqDQ44tObhxpks9lUs7JcSQlOqyMBAAAAmATKKqJOMGi0q86j\n4VGfHltcpKLcFKsjAQAAAJgkyiqizsEzV9XSOagvFWeo4p5cq+MAAAAAmALKKqLKpdY+Hf20Qxkp\n8dq4vIx1qgAAAECEoqwiavQNerX3cKMcdptqVrkVH+ewOhIAAACAKaKsYsZdaunTv//3CV1u7Zux\nY/gDQdXWN2h0zK91FSXKz0yasWMBAAAAmHmUVcyo6wNe7T7ccOM/DzWod9A7I8d5/1Sr2ruHtMCd\npYVzsmfkGAAAAADuHsoqZozPH9TOg1c05gvogbk58voCqq3zyB8IhvQ45xqv69RvOpWdnqAnHiph\nnSoAAAAQBSirmDH7T7aos3dEC+fk6A+fuEcPzM5WR8+w3vuwNWTH6Okf1a+ONsnldGhr1Wy5nKxT\nBQAAAKIBZRUz4hNPt85c7lJeZpLWVRRLktZVlCgnI1EfXezUpw090z7GjSu3Hvn8AT35cImy0xOm\nvU8AAAAA4YGyipDr7B3RvmPNinM5VFPlltNx42Hmctq1tcqtOJdDvzrWpK6+kWkd550TzerqG9Gi\nL+Xq3vKsUEQHAAAAECYoqwgpry+gnQdvrEvduLxMmanxN/0+Ky1BG5aVyu8Pqva3V0Wn4szlbn1y\npVsFWUlas6QoFNEBAAAAhBHKKkLGGKO3jzXp+sCoHpqfp3klGV94v3tKM7VkXq66+0f19rFmGWMm\ndZxr14f1zolmxbsc2vKZK7cAAAAAogdn+QiZjy526XzjdRXlpOiRRYW3ve/qJUWalZ2sTxt6dPpy\n94SP4R0LaGedR4FAUJtWlCsjJf7OGwEAAACIOJRVhERb95D2f9iixHinqqvK5bDf/qHlsNtVU+VW\nQpxT+080q6Nn+I7HMMboraON6h3w6uH78jW3OD1U8QEAAACEGcoqpm3E61dtnUfBoNHmynKlJcVN\naLu05DhtqixTIGi0s86j0TH/be9/8kKnftPcq+LcFD2y8PZXbgEAAABENsoqpsUYo71HGtU/NKbK\n+2fJPSttUtvPKUzX8gUF6hv0au+RpluuX23tGtL7p1qVGO/Uliq37HZbKOIDAAAACFOUVUzL0XMd\nutzap7KCVFU+UDClfVQ9MEsleam61NKrExc6P/f7Ea9fu+o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AAGZIxJTVjp5hvXuyWQlx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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c39cee6d0>"
      ]
     },
     "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
      "Ttest_relResult(statistic=5.3090658237059012, pvalue=1.5243965145890786e-06)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=2.3351343582180091, pvalue=0.019536402944206405)\n",
      "================\n",
      "Binomial:\n",
      "1.77322248234e-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": 18,
   "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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
       "      <td>0.525</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.58</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",
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       "      <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",
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       "      <td>1</td>\n",
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       "    </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                 0.525                0.22   \n",
       "rf_predicted_score_reverse                 0.47               0.765   \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                 0.095               0.565   \n",
       "rf_predicted_score_reverse                  0.9               0.425   \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                   0   \n",
       "rf_predicted_score_affirm                  0.27                0.49   \n",
       "rf_predicted_score_reverse                0.725                0.47   \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                   0   \n",
       "rf_predicted_score_affirm                 0.175               0.495   \n",
       "rf_predicted_score_reverse                0.745               0.485   \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                 0.345  \n",
       "rf_predicted_score_reverse                 0.58  \n",
       "rf_correct_case                               1  \n",
       "dummy_correct_case                            1  "
      ]
     },
     "execution_count": 18,
     "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": 19,
   "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",
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      "text/plain": [
       "                                            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               0.702899              0.647826   \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                0.278261              0.330435   \n",
       "rf_predicted_score_reverse               0.701449              0.655072   \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                0.256522               0.26087   \n",
       "rf_predicted_score_reverse               0.733333              0.730435   \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",
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       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                0.295652              0.266667   \n",
       "rf_predicted_score_reverse               0.689855              0.715942   \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                0.253623  \n",
       "rf_predicted_score_reverse               0.736232  \n",
       "rf_correct_case                                 1  \n",
       "dummy_correct_case                              1  "
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                       77126            77127  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
       "justiceName                        JGRoberts          AScalia   \n",
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       "rf_correct_case                            1                1   \n",
       "dummy_correct_case                         0                0   \n",
       "\n",
       "                                       77128            77129  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
       "justiceName                        AMKennedy          CThomas   \n",
       "case_outcome_disposition                   0                0   \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",
       "justiceName                       RBGinsburg         SGBreyer   \n",
       "case_outcome_disposition                   0                0   \n",
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       "rf_predicted                               0                0   \n",
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       "                                       77132            77133            77134  \n",
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       "rf_predicted_score_reverse          0.391304         0.323188         0.344928  \n",
       "rf_correct_case                            1                1                1  \n",
       "dummy_correct_case                         0                0                0  "
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caseName</th>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "      <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justiceName</th>\n",
       "      <td>JGRoberts</td>\n",
       "      <td>JPStevens</td>\n",
       "      <td>AScalia</td>\n",
       "      <td>AMKennedy</td>\n",
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       "    <tr>\n",
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       "      <th>rf_predicted</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
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       "      <td>0.60625</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.357812</td>\n",
       "      <td>0.688087</td>\n",
       "      <td>0.657813</td>\n",
       "      <td>0.678712</td>\n",
       "      <td>0.407813</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.661525</td>\n",
       "      <td>0.472462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "</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                                          0.333788   \n",
       "rf_predicted_score_reverse                                          0.63965   \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                                           0.60625   \n",
       "rf_predicted_score_reverse                                         0.357812   \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                                          0.293163   \n",
       "rf_predicted_score_reverse                                         0.688087   \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                                          0.314063   \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                                          0.300975   \n",
       "rf_predicted_score_reverse                                         0.678712   \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                                          0.559375   \n",
       "rf_predicted_score_reverse                                         0.407813   \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                                           0.53125   \n",
       "rf_predicted_score_reverse                                             0.45   \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                                            0.3166   \n",
       "rf_predicted_score_reverse                                         0.661525   \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                                          0.365038  \n",
       "rf_predicted_score_reverse                                         0.472462  \n",
       "rf_correct_case                                                           1  \n",
       "dummy_correct_case                                                        1  "
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\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",
       "    </tr>\n",
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       "      <th>justiceName</th>\n",
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       "    <tr>\n",
       "      <th>rf_predicted</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
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       "      <td>0.329032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
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       "      <td>0.593548</td>\n",
       "      <td>0.633871</td>\n",
       "      <td>0.648387</td>\n",
       "      <td>0.637097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "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                          0.380645   \n",
       "rf_predicted_score_reverse                         0.595161   \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                          0.369355   \n",
       "rf_predicted_score_reverse                         0.619355   \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                          0.325806   \n",
       "rf_predicted_score_reverse                         0.656452   \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                          0.383871   \n",
       "rf_predicted_score_reverse                         0.603226   \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                          0.366129   \n",
       "rf_predicted_score_reverse                         0.624194   \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                           0.38871   \n",
       "rf_predicted_score_reverse                         0.593548   \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                         0.633871   \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                          0.327419   \n",
       "rf_predicted_score_reverse                         0.648387   \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                          0.329032  \n",
       "rf_predicted_score_reverse                         0.637097  \n",
       "rf_correct_case                                           0  \n",
       "dummy_correct_case                                        0  "
      ]
     },
     "execution_count": 22,
     "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": 23,
   "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}_extra_trees_{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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