fantayscotus_2015_build_scdb_model_40.ipynb

{
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    "Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "==================\n",
    "  *  __Title__: Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "  *  __Authors__: [Daniel Martin Katz](http://www.law.msu.edu/faculty_staff/profile.php?prof=780), [Michael J Bommarito II](http://bommaritollc.com/), [Josh Blackman](http://joshblackman.com)\n",
    "  *  __Paper URL__: [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244)\n",
    "  *  __Blog URL__: [http://lexpredict.com/portfolio/predicting-the-supreme-court/](http://lexpredict.com/portfolio/predicting-the-supreme-court/)\n",
    "\n",
    "## Paper Abstract\n",
    "Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized,and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).\n",
    "\n",
    "## Source Description\n",
    "The source and data in this repository allow for the reproduction of the results in this paper.  \n",
    "\n",
    "## Data Description\n",
    "The data used in this paper is available from the [Supreme Court Database (SCDB)](http://scdb.wustl.edu/).\n",
    "\n",
    "## Version\n",
    "The latest version of this model was relesed in October 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Imports\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats\n",
    "import statsmodels.stats\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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Pre-process old and new data to merge\n",
    "old_data = pandas.read_csv(\"../SCDB_2015_01_justiceCentered_Citation.csv\")\n",
<<<<<<< HEAD
    "new_data = pandas.read_table(\"data/input/OT2015_20160416.csv\")"
=======
    "new_data = pandas.read_table(\"data/input/OT2015_20160228.csv\")"
>>>>>>> parent of ebbaa95... mid-March update
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>justice</th>\n",
       "      <th>justiceName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76703</th>\n",
       "      <td>111</td>\n",
       "      <td>JGRoberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76704</th>\n",
       "      <td>105</td>\n",
       "      <td>AScalia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76705</th>\n",
       "      <td>106</td>\n",
       "      <td>AMKennedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76706</th>\n",
       "      <td>108</td>\n",
       "      <td>CThomas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76707</th>\n",
       "      <td>109</td>\n",
       "      <td>RBGinsburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76708</th>\n",
       "      <td>110</td>\n",
       "      <td>SGBreyer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76709</th>\n",
       "      <td>112</td>\n",
       "      <td>SAAlito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76710</th>\n",
       "      <td>113</td>\n",
       "      <td>SSotomayor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76711</th>\n",
       "      <td>114</td>\n",
       "      <td>EKagan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       justice justiceName\n",
       "76703      111   JGRoberts\n",
       "76704      105     AScalia\n",
       "76705      106   AMKennedy\n",
       "76706      108     CThomas\n",
       "76707      109  RBGinsburg\n",
       "76708      110    SGBreyer\n",
       "76709      112     SAAlito\n",
       "76710      113  SSotomayor\n",
       "76711      114      EKagan"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get current justices\n",
    "justice_df = old_data.loc[old_data.loc[:, \"term\"] == 2014,\n",
    "             [\"justice\", \"justiceName\"]]\\\n",
    "    .drop_duplicates()\n",
    "justice_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['direction',\n",
       " 'secondAgreement',\n",
       " 'firstAgreement',\n",
       " 'majority',\n",
       " 'vote',\n",
       " 'opinion']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get columns to add\n",
    "columns_to_add = list(set(old_data.columns) - set(new_data.columns))\n",
    "columns_to_add.remove(\"justice\")\n",
    "columns_to_add.remove(\"justiceName\")\n",
    "columns_to_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Setup new DF\n",
    "new_row_df = pandas.DataFrame()\n",
    "\n",
    "# Iterate over case-rows in new data\n",
    "for row_id, row in new_data.iterrows():\n",
    "    # Iterate over justices in last term\n",
    "    for justice_id, justice_row in justice_df.iterrows():\n",
    "        new_row = row.copy()\n",
    "        new_row[\"justice\"] = justice_row[\"justice\"]\n",
    "        new_row[\"justiceName\"] = justice_row[\"justiceName\"]\n",
    "        new_row[\"dateDecision\"] = \"4/20/2016\"\n",
    "        \n",
    "        # Iterate over missing columns\n",
    "        for column_name in columns_to_add:\n",
    "            new_row[column_name] = numpy.nan\n",
    "        \n",
    "        new_row_df = new_row_df.append(new_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77342, 61)\n",
      "(78107, 61)\n"
     ]
    }
   ],
   "source": [
    "# Merge DFs\n",
    "scdb_data = old_data.append(new_row_df)\n",
    "print(old_data.shape)\n",
    "print(scdb_data.shape)\n",
    "\n",
    "# Write out\n",
<<<<<<< HEAD
    "scdb_data.to_csv(\"data/input/SCDB_OT2015_201604_input.csv\", index=False)"
=======
    "scdb_data.to_csv(\"data/input/SCDB_OT2015_201602_input.csv\", index=False)"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get raw data\n",
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    "raw_data = get_raw_scdb_data(\"data/input/SCDB_OT2015_201604_input.csv\")"
=======
    "raw_data = get_raw_scdb_data(\"data/input/SCDB_OT2015_201602_input.csv\")"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get feature data\n",
    "feature_df = preprocess_raw_data(raw_data, include_direction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "(78107, 82)\n",
      "(78107, 1371)\n"
=======
      "(78035, 82)\n",
      "(78035, 1368)\n"
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     ]
    }
   ],
   "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term: 1951\n",
      "Term: 1952\n",
      "Term: 1953\n",
      "Term: 1954\n",
      "Term: 1955\n",
      "Term: 1956\n",
      "Term: 1957\n",
      "Term: 1958\n",
      "Term: 1959\n",
      "Term: 1960\n",
      "Reset interval hit; rebuilding with 300 trees\n",
      "Term: 1961\n",
      "Term: 1962\n",
      "Term: 1963\n",
      "Term: 1964\n",
      "Term: 1965\n",
      "Term: 1966\n",
      "Term: 1967\n",
      "Term: 1968\n",
      "Term: 1969\n",
      "Term: 1970\n",
      "Reset interval hit; rebuilding with 500 trees\n",
      "Term: 1971\n",
      "Term: 1972\n",
      "Term: 1973\n",
      "Term: 1974\n",
      "Term: 1975\n",
      "Term: 1976\n",
      "Term: 1977\n",
      "Term: 1978\n",
      "Term: 1979\n",
      "Term: 1980\n",
      "Reset interval hit; rebuilding with 700 trees\n",
      "Term: 1981\n",
      "Term: 1982\n",
      "Term: 1983\n",
      "Term: 1984\n",
      "Term: 1985\n",
      "Term: 1986\n",
      "Term: 1987\n",
      "Term: 1988\n",
      "Term: 1989\n",
      "Term: 1990\n",
      "Reset interval hit; rebuilding with 900 trees\n",
      "Term: 1991\n",
      "Term: 1992\n",
      "Term: 1993\n",
      "Term: 1994\n",
      "Term: 1995\n",
      "Term: 1996\n",
      "Term: 1997\n",
      "Term: 1998\n",
      "Term: 1999\n",
      "Term: 2000\n",
      "Reset interval hit; rebuilding with 1100 trees\n",
      "Term: 2001\n",
      "Term: 2002\n",
      "Term: 2003\n",
      "Term: 2004\n",
      "Term: 2005\n",
      "Term: 2006\n",
      "Term: 2007\n",
      "Term: 2008\n",
      "Term: 2009\n",
      "Term: 2010\n",
      "Reset interval hit; rebuilding with 1300 trees\n",
      "Term: 2011\n",
      "Term: 2012\n",
      "Term: 2013\n",
      "Term: 2014\n",
      "Term: 2015\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/workspace/env/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\"].astype(int).min() + min_training_years,\n",
    "                   raw_data[\"term\"].astype(int).max()+1)\n",
    "#term_range = range(raw_data[\"term\"].astype(int).min() + min_training_years,\n",
    "#                   1980)\n",
    "\n",
    "# Setting growing random forest parameters\n",
    "# Number of trees to grow per term\n",
    "trees_per_term = 20\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 = 10\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",
    "    # 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",
    "                                                    min_samples_leaf=1,\n",
    "                                                    n_jobs=1)\n",
    "    elif 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 = sklearn.ensemble.ExtraTreesClassifier(n_estimators=initial_trees + (term_count * trees_per_term),\n",
    "                                                    class_weight=\"balanced_subsample\",\n",
    "                                                    warm_start=True,\n",
    "                                                    min_samples_leaf=1,\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",
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   "execution_count": 13,
=======
   "execution_count": 34,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
<<<<<<< HEAD
      "         -1       0.93      0.39      0.55      7465\n",
      "          0       0.65      0.13      0.22     24325\n",
      "          1       0.61      0.96      0.74     41142\n",
=======
      "         -1       0.93      0.40      0.55      7393\n",
      "          0       0.63      0.13      0.21     24325\n",
      "          1       0.61      0.96      0.75     41142\n",
>>>>>>> parent of ebbaa95... mid-March update
      "\n",
      "avg / total       0.65      0.62      0.55     72932\n",
      "\n",
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      "[[ 2891   180  4394]\n",
      " [   89  3194 21042]\n",
      " [  118  1576 39448]]\n",
      "0.624321285581\n",
=======
      "[[ 2921   361  4111]\n",
      " [   90  3113 21122]\n",
      " [  133  1486 39523]]\n",
      "0.625267636563\n",
>>>>>>> parent of ebbaa95... mid-March update
      "================================\n",
      "\n",
      "Dummy model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.00      0.00      0.00      7465\n",
      "          0       0.00      0.00      0.00     24325\n",
      "          1       0.56      1.00      0.72     41142\n",
      "\n",
      "avg / total       0.32      0.56      0.41     72932\n",
      "\n",
      "[[    0     0  7465]\n",
      " [    0     0 24325]\n",
      " [    0     0 41142]]\n",
      "0.564114517633\n",
      "================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluation range\n",
    "evaluation_index = raw_data.loc[:, \"term\"].isin(term_range)\n",
    "target_actual = raw_data.loc[evaluation_index, \"justice_outcome_disposition\"]\n",
    "target_predicted = raw_data.loc[evaluation_index, \"rf_predicted\"]\n",
    "x_index = (raw_data.loc[evaluation_index, \"rf_predicted_score_reverse\"] / (raw_data.loc[evaluation_index, \"rf_predicted_score_reverse\"] + raw_data.loc[evaluation_index, \"rf_predicted_score_affirm\"])) > 0.42\n",
    "target_predicted[x_index & (target_predicted == 0)] = 1\n",
    "target_dummy = raw_data.loc[evaluation_index, \"dummy_predicted\"]\n",
    "raw_data.loc[:, \"rf_correct\"] = numpy.nan\n",
    "raw_data.loc[:, \"dummy_correct\"] = numpy.nan\n",
    "raw_data.loc[evaluation_index, \"rf_correct\"] = (target_actual == target_predicted).astype(float)\n",
    "raw_data.loc[evaluation_index, \"dummy_correct\"] = (target_actual == target_dummy).astype(float)\n",
    "\n",
    "# Compare model\n",
    "print(\"RF model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_predicted))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_predicted))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_predicted))\n",
    "print(\"=\"*32)\n",
    "print(\"\")\n",
    "\n",
    "# Dummy model\n",
    "print(\"Dummy model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_dummy))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_dummy))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_dummy))\n",
    "print(\"=\"*32)\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 14,
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   "execution_count": 35,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "<matplotlib.legend.Legend at 0x7fe1282f9b10>"
      ]
     },
     "execution_count": 14,
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       "<matplotlib.legend.Legend at 0x7f4c9f777d10>"
      ]
     },
     "execution_count": 35,
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     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1286d8350>"
=======
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ca35b53d0>"
>>>>>>> parent of ebbaa95... mid-March update
      ]
     },
     "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",
<<<<<<< HEAD
   "execution_count": 15,
=======
   "execution_count": 36,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
<<<<<<< HEAD
       "<matplotlib.text.Text at 0x7fe1284b6410>"
      ]
     },
     "execution_count": 15,
=======
       "<matplotlib.text.Text at 0x7f4ca7327890>"
      ]
     },
     "execution_count": 36,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1285a6b90>"
=======
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       "bsmtt96ahoaGjBgxInv37s2wYcPy/PPPZ8yYMWlubu45a5Qkzz//fCZNmiT/Gne47Jubmw85V/a1\n",
       "r7f5t7W15corr8zixYszYcKEJH3Pf1CcKb7xxhvzr//6r0mSH//4xzn33HPz3//937niiivS3d2d\n",
       "ffv25fHHH8/v/M7vZOrUqfmnf/qnJMmDDz6YKVOm9OfSOQpen//06dMzbty4dHZ2ZteuXenu7s7T\n",
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       "H2QO9nt/3759mTVrVvbu3ZtNmzbl2WefzWmnnSb7GtfR0ZHFixfn5ptvzsiRI5Mkv//7v5+VK1cm\n",
       "SVauXJk/+IM/yOmnn56nnnoqHR0d6ezszOOPP54zzzxT/jWst9nvt/9Kwf1kX9v6kv+8efMyf/78\n",
       "t/R3X13R12sP+tnatWvz9a9/Pdu3b099fX1OPPHELFy4MAsWLEhXV1emTJmSr371q0lee3GlRx55\n",
       "JMOGDcu5556bz33uc+nu7s68efOyYcOGDB8+PNdee23GjBnTz7uit/qS/xNPPJEFCxakUqnk7LPP\n",
       "zp/92Z/Jv4b1JfvktV+GjzzySM/nsq9tfcl/yZIlefjhh1NXV5fzzz8/l112mfxrWF+yv/3223PH\n",
       "HXeku7s7c+fOzZQpU2Rf437wgx9kyZIlefvb397zFpvf+ta3Mm/evOzduzetra1ZtGhR6uvrc//9\n",
       "9+e73/1uKpVKLrvssnz0ox+Vfw3rbfZ1dXX5+Mc/nt27d2fnzp1paWnJV7/61Zx11lmyr2G9zX/j\n",
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       "AKC0lGIAAABKSykGAACgtIb09wIAgDdavHhxnnjiiezduzc///nPM2nSpCTJhRdemI997GP9vDoA\n",
       "GDy8TzEADGCbN2/Opz/96axataq/lwIAg5IzxQBQQ/bu3ZtvfvOb2bRpUzo6OvLxj388s2bNyooV\n",
       "K/LII49kx44d+cxnPpO77747o0ePzvr167N+/frMnj07//zP/5x169blfe97X77xjW/091YAYEBQ\n",
       "igGghnzve9/LuHHjsnDhwuzbty8zZ87M1KlTkyS//OUvc88996S+vj533313duzYke985ztZsWJF\n",
       "FixYkAceeCCVSiXvfe97c9VVV+X444/v590AQP9TigGghvzbv/1btm3blkcffTRJ0tXVlWeffTZJ\n",
       "cuqpp6a+vr5n7OTJk5MkLS0tede73pUTTjghSXLiiSfmpZdeUooBIEoxANSUoUOH5sorr8wHP/jB\n",
       "A76+YsWKDB069ICvDRky5KAfJ4mXFAGA13hLJgAY4H6zwJ555pm59957kyT79u3LX//1X6ejo6O/\n",
       "lgYANU8pBoABrq6urufjyy67LMOGDcvFF1+ciy++OCeffHIaGhredA4AcGjekgkAAIDScqYYAACA\n",
       "0lKKAQAAKC2lGAAAgNJSigEAACgtpRgAAIDSUooBAAAoLaUYAACA0vp/NoefpH9vsOsAAAAASUVO\n",
       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ca6e383d0>"
>>>>>>> parent of ebbaa95... mid-March update
      ]
     },
     "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",
<<<<<<< HEAD
   "execution_count": 16,
=======
   "execution_count": 37,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/workspace/ipython/IPython/kernel/__main__.py:4: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    },
    {
     "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",
<<<<<<< HEAD
       "      <th>1369</th>\n",
       "      <td>justice_previous_lc_direction_diff</td>\n",
       "      <td>0.020270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>justice_cumulative_lc_direction_diff</td>\n",
       "      <td>0.020242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>justice_cumulative_agreement</td>\n",
       "      <td>0.016381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>justice_previous_agreement</td>\n",
       "      <td>0.016034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>justice_previous_court_direction_diff</td>\n",
       "      <td>0.015474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>justice_cumulative_direction</td>\n",
       "      <td>0.015470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>justice_cumulative_court_direction_diff</td>\n",
       "      <td>0.015431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>justice_previous_direction</td>\n",
       "      <td>0.015354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>issue_area_9</td>\n",
       "      <td>0.015332</td>\n",
=======
       "      <th>1367</th>\n",
       "      <td>justice_cumulative_lc_direction_diff</td>\n",
       "      <td>0.020545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>justice_previous_lc_direction_diff</td>\n",
       "      <td>0.020275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>justice_cumulative_agreement</td>\n",
       "      <td>0.016418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>justice_previous_agreement</td>\n",
       "      <td>0.015976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>justice_cumulative_direction</td>\n",
       "      <td>0.015596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>justice_cumulative_court_direction_diff</td>\n",
       "      <td>0.015521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>justice_previous_direction</td>\n",
       "      <td>0.015489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>justice_previous_court_direction_diff</td>\n",
       "      <td>0.015486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>issue_area_9</td>\n",
       "      <td>0.015196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>justice_previous_action</td>\n",
       "      <td>0.014575</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>argument_month_-1</td>\n",
<<<<<<< HEAD
       "      <td>0.015067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>law_supp_508</td>\n",
       "      <td>0.014501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>justice_previous_action</td>\n",
       "      <td>0.014438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>justice_cumulative_action</td>\n",
       "      <td>0.014169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>justice_previous_court_action_diff</td>\n",
       "      <td>0.013344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>justice_previous_court_agreement_diff</td>\n",
       "      <td>0.013201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>justice_cumulative_court_action_diff</td>\n",
       "      <td>0.013062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>lc_outcome_-1</td>\n",
       "      <td>0.012866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>justice_cumulative_court_agreement_diff</td>\n",
       "      <td>0.012753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>issue_90150</td>\n",
       "      <td>0.012446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>jurisdiction_9</td>\n",
       "      <td>0.011319</td>\n",
=======
       "      <td>0.014554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>justice_cumulative_action</td>\n",
       "      <td>0.014179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>law_supp_508</td>\n",
       "      <td>0.013574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>justice_previous_court_action_diff</td>\n",
       "      <td>0.013459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>justice_previous_court_agreement_diff</td>\n",
       "      <td>0.013264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>justice_cumulative_court_action_diff</td>\n",
       "      <td>0.013118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>justice_cumulative_court_agreement_diff</td>\n",
       "      <td>0.012853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>lc_outcome_-1</td>\n",
       "      <td>0.012652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>issue_90150</td>\n",
       "      <td>0.012178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>jurisdiction_9</td>\n",
       "      <td>0.010745</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      feature  importance\n",
<<<<<<< HEAD
       "1369       justice_previous_lc_direction_diff    0.020270\n",
       "1370     justice_cumulative_lc_direction_diff    0.020242\n",
       "1364             justice_cumulative_agreement    0.016381\n",
       "1363               justice_previous_agreement    0.016034\n",
       "1355    justice_previous_court_direction_diff    0.015474\n",
       "1352             justice_cumulative_direction    0.015470\n",
       "1356  justice_cumulative_court_direction_diff    0.015431\n",
       "1351               justice_previous_direction    0.015354\n",
       "1159                             issue_area_9    0.015332\n",
       "105                         argument_month_-1    0.015067\n",
       "1342                             law_supp_508    0.014501\n",
       "1357                  justice_previous_action    0.014438\n",
       "1358                justice_cumulative_action    0.014169\n",
       "1361       justice_previous_court_action_diff    0.013344\n",
       "1367    justice_previous_court_agreement_diff    0.013201\n",
       "1362     justice_cumulative_court_action_diff    0.013062\n",
       "880                             lc_outcome_-1    0.012866\n",
       "1368  justice_cumulative_court_agreement_diff    0.012753\n",
       "1088                              issue_90150    0.012446\n",
       "710                            jurisdiction_9    0.011319"
      ]
     },
     "execution_count": 16,
=======
       "1367     justice_cumulative_lc_direction_diff    0.020545\n",
       "1366       justice_previous_lc_direction_diff    0.020275\n",
       "1361             justice_cumulative_agreement    0.016418\n",
       "1360               justice_previous_agreement    0.015976\n",
       "1349             justice_cumulative_direction    0.015596\n",
       "1353  justice_cumulative_court_direction_diff    0.015521\n",
       "1348               justice_previous_direction    0.015489\n",
       "1352    justice_previous_court_direction_diff    0.015486\n",
       "1156                             issue_area_9    0.015196\n",
       "1354                  justice_previous_action    0.014575\n",
       "105                         argument_month_-1    0.014554\n",
       "1355                justice_cumulative_action    0.014179\n",
       "1339                             law_supp_508    0.013574\n",
       "1358       justice_previous_court_action_diff    0.013459\n",
       "1364    justice_previous_court_agreement_diff    0.013264\n",
       "1359     justice_cumulative_court_action_diff    0.013118\n",
       "1365  justice_cumulative_court_agreement_diff    0.012853\n",
       "878                             lc_outcome_-1    0.012652\n",
       "1086                              issue_90150    0.012178\n",
       "709                            jurisdiction_9    0.010745"
      ]
     },
     "execution_count": 37,
>>>>>>> parent of ebbaa95... mid-March update
     "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(\"importance\", ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 17,
=======
   "execution_count": 38,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.stats.proportion"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 18,
=======
   "execution_count": 39,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
<<<<<<< HEAD
      "Ttest_relResult(statistic=14.03358329962745, pvalue=3.3459808355984393e-21)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=5.5619588655572558, pvalue=2.6676317990352936e-08)\n",
      "================\n",
      "Binomial:\n",
      "3.90111342154e-239\n"
=======
      "Ttest_relResult(statistic=14.625360103601805, pvalue=4.418880998420542e-22)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=5.538677329912395, pvalue=3.0476455848880839e-08)\n",
      "================\n",
      "Binomial:\n",
      "4.38283243876e-242\n"
>>>>>>> parent of ebbaa95... mid-March update
     ]
    }
   ],
   "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",
<<<<<<< HEAD
   "execution_count": 19,
=======
   "execution_count": 40,
>>>>>>> parent of ebbaa95... mid-March update
   "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\"]"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 20,
=======
   "execution_count": 41,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set DFs\n",
    "rf_predicted_case = raw_data[[\"docketId\", \"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",
<<<<<<< HEAD
   "execution_count": 21,
=======
   "execution_count": 42,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
<<<<<<< HEAD
      "       -1.0       0.91      0.48      0.62      3676\n",
      "        0.0       0.54      0.23      0.32     21597\n",
      "        1.0       0.69      0.91      0.78     45782\n",
      "\n",
      "avg / total       0.66      0.68      0.63     71055\n",
      "\n",
      "[[ 1750    72  1854]\n",
      " [   81  4864 16652]\n",
      " [   99  4154 41529]]\n",
      "0.677545563296\n",
=======
      "       -1.0       0.89      0.49      0.63      3676\n",
      "        0.0       0.53      0.22      0.31     21597\n",
      "        1.0       0.69      0.90      0.78     45782\n",
      "\n",
      "avg / total       0.65      0.68      0.63     71055\n",
      "\n",
      "[[ 1794    63  1819]\n",
      " [   89  4817 16691]\n",
      " [  126  4274 41382]]\n",
      "0.675434522553\n",
>>>>>>> parent of ebbaa95... mid-March update
      "================================\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",
<<<<<<< HEAD
   "execution_count": 22,
=======
   "execution_count": 43,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
<<<<<<< HEAD
       "<matplotlib.legend.Legend at 0x7fe127fe8590>"
      ]
     },
     "execution_count": 22,
=======
       "<matplotlib.legend.Legend at 0x7f4c9f6310d0>"
      ]
     },
     "execution_count": 43,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe1281fbf90>"
=======
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       "RK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ca73b5a10>"
>>>>>>> parent of ebbaa95... mid-March update
      ]
     },
     "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",
<<<<<<< HEAD
   "execution_count": 23,
=======
   "execution_count": 44,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
<<<<<<< HEAD
       "<matplotlib.text.Text at 0x7fe127f51e50>"
      ]
     },
     "execution_count": 23,
=======
       "<matplotlib.text.Text at 0x7f4c9f61bb90>"
      ]
     },
     "execution_count": 44,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "oRgAAIBiCcUAAAAUSygGAACgWEIxAAAAxRKKAQAAKJZQDAAAQLGEYgAAAIolFAMAAFAsoRgAAIBi\n",
>>>>>>> parent of ebbaa95... mid-March update
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<<<<<<< HEAD
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe128138a10>"
=======
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       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ca7006990>"
>>>>>>> parent of ebbaa95... mid-March update
      ]
     },
     "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",
<<<<<<< HEAD
   "execution_count": 24,
=======
   "execution_count": 45,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
<<<<<<< HEAD
       "[<matplotlib.lines.Line2D at 0x7fe127cf5410>]"
      ]
     },
     "execution_count": 24,
=======
       "[<matplotlib.lines.Line2D at 0x7f4c9f296f90>]"
      ]
     },
     "execution_count": 45,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ca70bcdd0>"
>>>>>>> parent of ebbaa95... mid-March update
      ]
     },
     "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",
<<<<<<< HEAD
   "execution_count": 25,
=======
   "execution_count": 46,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
<<<<<<< HEAD
      "Ttest_relResult(statistic=5.5158258194109004, pvalue=6.9342505013365325e-07)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=2.4757189777433788, pvalue=0.01329681932663293)\n",
      "================\n",
      "Binomial:\n",
      "8.09649142534e-78\n"
=======
      "Ttest_relResult(statistic=4.7181662623237575, pvalue=1.3661043550455556e-05)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=2.292244135311964, pvalue=0.021891559458295004)\n",
      "================\n",
      "Binomial:\n",
      "1.92752581765e-68\n"
>>>>>>> parent of ebbaa95... mid-March update
     ]
    }
   ],
   "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",
    "print(statsmodels.stats.proportion.binom_test(raw_data.loc[evaluation_index, \"rf_correct_case\"].sum(),\n",
    "                                              raw_data.loc[evaluation_index, \"rf_correct_case\"].shape[0],\n",
    "                                              raw_data.loc[evaluation_index, \"dummy_correct_case\"].mean(),\n",
    "                                              alternative=\"larger\"))"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 26,
=======
   "execution_count": 47,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    </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",
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       "      <th>rf_predicted</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
<<<<<<< HEAD
       "      <td>0.6575</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0825</td>\n",
       "      <td>0.4675</td>\n",
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       "      <td>0.5475</td>\n",
       "      <td>0.5125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.7975</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.4225</td>\n",
       "      <td>0.4275</td>\n",
=======
       "      <td>0.646543</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1125</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.2275</td>\n",
       "      <td>0.3325</td>\n",
       "      <td>0.1825</td>\n",
       "      <td>0.5475</td>\n",
       "      <td>0.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.330957</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.4825</td>\n",
       "      <td>0.7275</td>\n",
       "      <td>0.6225</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.4325</td>\n",
       "      <td>0.4175</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          22281               22282  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                            JHarlan2             HLBlack   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   0                   1   \n",
       "rf_predicted                                  0                   1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.6575                0.17   \n",
       "rf_predicted_score_reverse                 0.32              0.7975   \n",
=======
       "rf_predicted_score_affirm              0.646543              0.1625   \n",
       "rf_predicted_score_reverse             0.330957               0.805   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted                                  1                   1   \n",
       "rf_predicted_score_affirm                0.0825              0.4675   \n",
       "rf_predicted_score_reverse                 0.88                0.51   \n",
=======
       "rf_predicted                                  1                   0   \n",
       "rf_predicted_score_affirm                0.1125               0.495   \n",
       "rf_predicted_score_reverse                0.855              0.4825   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22285               22286  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                           WJBrennan             BRWhite   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                  0.22               0.285   \n",
       "rf_predicted_score_reverse                0.735                0.65   \n",
=======
       "rf_predicted_score_affirm                0.2275              0.3325   \n",
       "rf_predicted_score_reverse               0.7275              0.6225   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.1525              0.5475   \n",
       "rf_predicted_score_reverse                 0.81              0.4225   \n",
=======
       "rf_predicted_score_affirm                0.1825              0.5475   \n",
       "rf_predicted_score_reverse                 0.78              0.4325   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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                                  0  \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.5125  \n",
       "rf_predicted_score_reverse               0.4275  \n",
=======
       "rf_predicted_score_affirm                 0.545  \n",
       "rf_predicted_score_reverse               0.4175  \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                               1  \n",
       "dummy_correct_case                            1  "
      ]
     },
<<<<<<< HEAD
     "execution_count": 26,
=======
     "execution_count": 47,
>>>>>>> parent of ebbaa95... mid-March update
     "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",
<<<<<<< HEAD
   "execution_count": 27,
=======
   "execution_count": 48,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>caseName</th>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
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       "      <td>OBERGEFELL v. HODGES</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
<<<<<<< HEAD
       "      <td>0.293931</td>\n",
       "      <td>0.330887</td>\n",
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       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.689402</td>\n",
       "      <td>0.661866</td>\n",
       "      <td>0.703171</td>\n",
       "      <td>0.64058</td>\n",
       "      <td>0.727536</td>\n",
       "      <td>0.719565</td>\n",
       "      <td>0.686957</td>\n",
       "      <td>0.737681</td>\n",
       "      <td>0.722464</td>\n",
=======
       "      <td>0.281884</td>\n",
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       "      <td>0.333333</td>\n",
       "      <td>0.268116</td>\n",
       "      <td>0.263768</td>\n",
       "      <td>0.313768</td>\n",
       "      <td>0.272464</td>\n",
       "      <td>0.262319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.702899</td>\n",
       "      <td>0.671014</td>\n",
       "      <td>0.688406</td>\n",
       "      <td>0.65942</td>\n",
       "      <td>0.724638</td>\n",
       "      <td>0.725362</td>\n",
       "      <td>0.676812</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>0.728986</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.293931              0.330887   \n",
       "rf_predicted_score_reverse               0.689402              0.661866   \n",
=======
       "rf_predicted_score_affirm                0.281884              0.321014   \n",
       "rf_predicted_score_reverse               0.702899              0.671014   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.285235              0.350725   \n",
       "rf_predicted_score_reverse               0.703171               0.64058   \n",
=======
       "rf_predicted_score_affirm                0.295652              0.333333   \n",
       "rf_predicted_score_reverse               0.688406               0.65942   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.265217              0.269565   \n",
       "rf_predicted_score_reverse               0.727536              0.719565   \n",
=======
       "rf_predicted_score_affirm                0.268116              0.263768   \n",
       "rf_predicted_score_reverse               0.724638              0.725362   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77294                 77295  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                               SAAlito            SSotomayor   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     0                     1   \n",
       "rf_predicted                                    1                     1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.305072              0.249275   \n",
       "rf_predicted_score_reverse               0.686957              0.737681   \n",
=======
       "rf_predicted_score_affirm                0.313768              0.272464   \n",
       "rf_predicted_score_reverse               0.676812              0.716667   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                0.266667  \n",
       "rf_predicted_score_reverse               0.722464  \n",
=======
       "rf_predicted_score_affirm                0.262319  \n",
       "rf_predicted_score_reverse               0.728986  \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                 1  \n",
       "dummy_correct_case                              1  "
      ]
     },
<<<<<<< HEAD
     "execution_count": 27,
=======
     "execution_count": 48,
>>>>>>> parent of ebbaa95... mid-March update
     "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",
<<<<<<< HEAD
   "execution_count": 28,
=======
   "execution_count": 49,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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<<<<<<< HEAD
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=======
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>>>>>>> parent of ebbaa95... mid-March update
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       "                                       77126            77127  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
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       "case_outcome_disposition                   0                0   \n",
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<<<<<<< HEAD
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=======
       "rf_predicted_score_affirm           0.599003         0.570018   \n",
       "rf_predicted_score_reverse          0.385779         0.415489   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                            1                1   \n",
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       "\n",
       "                                       77128            77129  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
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<<<<<<< HEAD
       "rf_predicted_score_affirm           0.583061           0.5548   \n",
       "rf_predicted_score_reverse          0.402446          0.42491   \n",
=======
       "rf_predicted_score_affirm           0.593931         0.563496   \n",
       "rf_predicted_score_reverse          0.390127         0.422736   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                            1                1   \n",
       "dummy_correct_case                         0                0   \n",
       "\n",
       "                                       77130            77131  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
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<<<<<<< HEAD
       "rf_predicted_score_affirm           0.609873         0.609148   \n",
       "rf_predicted_score_reverse          0.369113         0.366939   \n",
=======
       "rf_predicted_score_affirm           0.625271         0.629166   \n",
       "rf_predicted_score_reverse          0.354439         0.352718   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                            1                1   \n",
       "dummy_correct_case                         0                0   \n",
       "\n",
       "                                       77132            77133            77134  \n",
       "caseName                     KING v. BURWELL  KING v. BURWELL  KING v. BURWELL  \n",
       "justiceName                          SAAlito       SSotomayor           EKagan  \n",
       "case_outcome_disposition                   0                0                0  \n",
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       "rf_predicted                               0                0                0  \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm           0.569021         0.632337         0.609148  \n",
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=======
       "rf_predicted_score_affirm           0.569293         0.633242         0.621195  \n",
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>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                            1                1                1  \n",
       "dummy_correct_case                         0                0                0  "
      ]
     },
<<<<<<< HEAD
     "execution_count": 28,
=======
     "execution_count": 49,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 29,
=======
   "execution_count": 50,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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<<<<<<< HEAD
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=======
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>>>>>>> parent of ebbaa95... mid-March update
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      ],
      "text/plain": [
       "                                                                      73130  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       JGRoberts   \n",
       "case_outcome_disposition                                                  1   \n",
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       "rf_predicted                                                              1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                           0.41875   \n",
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=======
       "rf_predicted_score_affirm                                           0.40986   \n",
       "rf_predicted_score_reverse                                         0.569046   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
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       "rf_predicted                                                              0   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                            0.5875   \n",
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=======
       "rf_predicted_score_affirm                                          0.576562   \n",
       "rf_predicted_score_reverse                                         0.377344   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73132  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         AScalia   \n",
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<<<<<<< HEAD
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=======
       "rf_predicted_score_affirm                                          0.410641   \n",
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>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73133  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       AMKennedy   \n",
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       "rf_predicted                                                              1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                          0.414062   \n",
       "rf_predicted_score_reverse                                         0.554688   \n",
=======
       "rf_predicted_score_affirm                                          0.406735   \n",
       "rf_predicted_score_reverse                                          0.55889   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                          0.385937   \n",
       "rf_predicted_score_reverse                                         0.586719   \n",
=======
       "rf_predicted_score_affirm                                          0.393454   \n",
       "rf_predicted_score_reverse                                         0.572953   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                              0.55   \n",
       "rf_predicted_score_reverse                                          0.40625   \n",
=======
       "rf_predicted_score_affirm                                          0.555469   \n",
       "rf_predicted_score_reverse                                         0.410156   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                          0.510156   \n",
       "rf_predicted_score_reverse                                         0.452344   \n",
=======
       "rf_predicted_score_affirm                                          0.513281   \n",
       "rf_predicted_score_reverse                                         0.453906   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                          0.379688   \n",
       "rf_predicted_score_reverse                                         0.592969   \n",
=======
       "rf_predicted_score_affirm                                          0.400485   \n",
       "rf_predicted_score_reverse                                         0.576078   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "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",
<<<<<<< HEAD
       "rf_predicted_score_affirm                                          0.355469  \n",
       "rf_predicted_score_reverse                                         0.475781  \n",
=======
       "rf_predicted_score_affirm                                          0.382812  \n",
       "rf_predicted_score_reverse                                         0.464844  \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                                           1  \n",
       "dummy_correct_case                                                        1  "
      ]
     },
<<<<<<< HEAD
     "execution_count": 29,
=======
     "execution_count": 50,
>>>>>>> parent of ebbaa95... mid-March update
     "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",
<<<<<<< HEAD
   "execution_count": 30,
=======
   "execution_count": 51,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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<<<<<<< HEAD
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=======
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>>>>>>> parent of ebbaa95... mid-March update
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       "                                                      72257  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
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       "rf_predicted                                              1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.404032   \n",
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=======
       "rf_predicted_score_affirm                          0.441935   \n",
       "rf_predicted_score_reverse                         0.529839   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72258  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
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<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.356452   \n",
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=======
       "rf_predicted_score_affirm                          0.387097   \n",
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>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                           0   \n",
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       "\n",
       "                                                      72259  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
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       "rf_predicted                                              1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.312097   \n",
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=======
       "rf_predicted_score_affirm                          0.350806   \n",
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>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                           0   \n",
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       "\n",
       "                                                      72260  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
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<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.399194   \n",
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=======
       "rf_predicted_score_affirm                          0.398387   \n",
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>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                           0   \n",
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       "\n",
       "                                                      72261  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                         CThomas   \n",
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       "rf_predicted                                              1   \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.348387   \n",
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=======
       "rf_predicted_score_affirm                          0.372581   \n",
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>>>>>>> parent of ebbaa95... mid-March update
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       "\n",
       "                                                      72262  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
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<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.428226   \n",
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=======
       "rf_predicted_score_affirm                          0.437903   \n",
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>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                           0   \n",
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       "\n",
       "                                                      72263  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                        SGBreyer   \n",
       "case_outcome_disposition                                  0   \n",
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<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.395968   \n",
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=======
       "rf_predicted_score_affirm                          0.406452   \n",
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>>>>>>> parent of ebbaa95... mid-March update
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       "\n",
       "                                                      72264  \\\n",
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       "justiceName                                       JGRoberts   \n",
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<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.322581   \n",
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=======
       "rf_predicted_score_affirm                          0.368548   \n",
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>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                           0   \n",
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       "\n",
       "                                                      72265  \n",
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       "rf_predicted                                              1  \n",
<<<<<<< HEAD
       "rf_predicted_score_affirm                          0.345161  \n",
       "rf_predicted_score_reverse                         0.634677  \n",
=======
       "rf_predicted_score_affirm                          0.371774  \n",
       "rf_predicted_score_reverse                         0.614516  \n",
>>>>>>> parent of ebbaa95... mid-March update
       "rf_correct_case                                           0  \n",
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      ]
     },
<<<<<<< HEAD
     "execution_count": 30,
=======
     "execution_count": 51,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
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    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == 'DISTRICT OF COLUMBIA v. HELLER',\n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
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  {
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<<<<<<< HEAD
   "execution_count": 31,
=======
   "execution_count": 52,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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<<<<<<< HEAD
       " 1    0.728437\n",
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     "execution_count": 31,
=======
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     "execution_count": 52,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
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<<<<<<< HEAD
   "execution_count": 32,
=======
   "execution_count": 53,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>0.048571</td>\n",
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       "      <td>0.673571</td>\n",
=======
       "      <td>0.008571</td>\n",
       "      <td>0.432143</td>\n",
       "      <td>0.559286</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78104</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-085</td>\n",
       "      <td>2015-085</td>\n",
       "      <td>McDonnell v. U.S.</td>\n",
       "      <td>126</td>\n",
       "      <td>...</td>\n",
<<<<<<< HEAD
       "      <td>0.063571</td>\n",
       "      <td>0.419286</td>\n",
       "      <td>0.517143</td>\n",
=======
       "      <td>0.015000</td>\n",
       "      <td>0.555714</td>\n",
       "      <td>0.429286</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-085</td>\n",
       "      <td>2015-085</td>\n",
       "      <td>McDonnell v. U.S.</td>\n",
       "      <td>126</td>\n",
       "      <td>...</td>\n",
<<<<<<< HEAD
       "      <td>0.055000</td>\n",
       "      <td>0.278571</td>\n",
       "      <td>0.666429</td>\n",
=======
       "      <td>0.008571</td>\n",
       "      <td>0.453571</td>\n",
       "      <td>0.537857</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78106</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-085</td>\n",
       "      <td>2015-085</td>\n",
       "      <td>McDonnell v. U.S.</td>\n",
       "      <td>126</td>\n",
       "      <td>...</td>\n",
<<<<<<< HEAD
       "      <td>0.056429</td>\n",
       "      <td>0.273303</td>\n",
       "      <td>0.670268</td>\n",
=======
       "      <td>0.005714</td>\n",
       "      <td>0.448571</td>\n",
       "      <td>0.545714</td>\n",
>>>>>>> parent of ebbaa95... mid-March update
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       adminAction  adminActionState  authorityDecision1  authorityDecision2  \\\n",
       "78102          NaN               NaN                 NaN                 NaN   \n",
       "78103          NaN               NaN                 NaN                 NaN   \n",
       "78104          NaN               NaN                 NaN                 NaN   \n",
       "78105          NaN               NaN                 NaN                 NaN   \n",
       "78106          NaN               NaN                 NaN                 NaN   \n",
       "\n",
       "       caseDisposition  caseDispositionUnusual    caseId caseIssuesId  \\\n",
       "78102              NaN                     NaN  2015-085     2015-085   \n",
       "78103              NaN                     NaN  2015-085     2015-085   \n",
       "78104              NaN                     NaN  2015-085     2015-085   \n",
       "78105              NaN                     NaN  2015-085     2015-085   \n",
       "78106              NaN                     NaN  2015-085     2015-085   \n",
       "\n",
       "                caseName  caseOrigin         ...          \\\n",
       "78102  McDonnell v. U.S.         126         ...           \n",
       "78103  McDonnell v. U.S.         126         ...           \n",
       "78104  McDonnell v. U.S.         126         ...           \n",
       "78105  McDonnell v. U.S.         126         ...           \n",
       "78106  McDonnell v. U.S.         126         ...           \n",
       "\n",
       "       rf_predicted_score_other  rf_predicted_score_affirm  \\\n",
<<<<<<< HEAD
       "78102                  0.051429                   0.276875   \n",
       "78103                  0.048571                   0.277857   \n",
       "78104                  0.063571                   0.419286   \n",
       "78105                  0.055000                   0.278571   \n",
       "78106                  0.056429                   0.273303   \n",
       "\n",
       "       rf_predicted_score_reverse  dummy_predicted rf_correct dummy_correct  \\\n",
       "78102                    0.671697                1          0             0   \n",
       "78103                    0.673571                1          0             0   \n",
       "78104                    0.517143                1          0             0   \n",
       "78105                    0.666429                1          0             0   \n",
       "78106                    0.670268                1          0             0   \n",
=======
       "78030                  0.005714                   0.447857   \n",
       "78031                  0.008571                   0.432143   \n",
       "78032                  0.015000                   0.555714   \n",
       "78033                  0.008571                   0.453571   \n",
       "78034                  0.005714                   0.448571   \n",
       "\n",
       "       rf_predicted_score_reverse  dummy_predicted rf_correct dummy_correct  \\\n",
       "78030                    0.546429                1          0             0   \n",
       "78031                    0.559286                1          0             0   \n",
       "78032                    0.429286                1          0             0   \n",
       "78033                    0.537857                1          0             0   \n",
       "78034                    0.545714                1          0             0   \n",
>>>>>>> parent of ebbaa95... mid-March update
       "\n",
       "      rf_predicted_case dummy_predicted_case  rf_correct_case  \\\n",
       "78102                 1                    1              NaN   \n",
       "78103                 1                    1              NaN   \n",
       "78104                 1                    1              NaN   \n",
       "78105                 1                    1              NaN   \n",
       "78106                 1                    1              NaN   \n",
       "\n",
       "       dummy_correct_case  \n",
       "78102                 NaN  \n",
       "78103                 NaN  \n",
       "78104                 NaN  \n",
       "78105                 NaN  \n",
       "78106                 NaN  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
<<<<<<< HEAD
     "execution_count": 32,
=======
     "execution_count": 53,
>>>>>>> parent of ebbaa95... mid-March update
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.tail()"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 33,
=======
   "execution_count": 54,
>>>>>>> parent of ebbaa95... mid-March update
   "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/fantayscotus_2015_build_scdb_model_40_{0}.csv\".format(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\"))"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 34,
=======
   "execution_count": 55,
>>>>>>> parent of ebbaa95... mid-March update
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raw_data.loc[raw_data[\"term\"] == 2015,\n",
    "             [\"caseName\", \"docket\", \"justice\", \"justiceName\", \"rf_predicted\", \"rf_predicted_case\", \"rf_predicted_score_reverse\", \"rf_predicted_score_affirm\"]]\\\n",
    "    .to_csv(\"fantayscotus_2015_build_scdb_model_40_{0}.csv\".format(timestamp_suffix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.371561244597\n",
      "0.417417417417\n"
     ]
    },
    {
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       "9cjOzsbatWuRn58Pp9OJo0eP4tlnnwUA/N3f/R02btwY9fk4FzcREZECcSYxIiIiBWJAExERKRAD\n",
       "moiISIEY0ERERArEgCYiIlIgBjQREZECMaCJiIgUiAFNRESkQP8fr2BBRYuiNxwAAAAASUVORK5C\n",
       "YII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ca3687d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sklearn.isotonic\n",
    "import sklearn.calibration\n",
    "\n",
    "calibrator = sklearn.isotonic.IsotonicRegression()\n",
    "calibration_mask = -raw_data[\"rf_predicted_score_reverse\"].isnull() & raw_data[\"justice_outcome_disposition\"].isin([0, 1])\n",
    "input_score = raw_data.loc[calibration_mask, \"rf_predicted_score_reverse\"] / (raw_data.loc[calibration_mask, \"rf_predicted_score_reverse\"] + raw_data.loc[calibration_mask, \"rf_predicted_score_affirm\"])\n",
    "input_target = raw_data.loc[calibration_mask, \"justice_outcome_disposition\"]\n",
    "calibrator.fit(input_score, input_target)\n",
    "\n",
    "x = numpy.linspace(0, 1, 1000)\n",
    "y = calibrator.predict(x)\n",
    "plt.plot(x, y)\n",
    "plt.axhline(0.5)\n",
    "plt.axvline(0.5)\n",
    "cutoff = 1 - input_target.mean()\n",
    "print(cutoff)\n",
    "threshold = x[numpy.where(y >= cutoff)][0]\n",
    "print(threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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