build_scdb_model_10_no_reset.ipynb

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    "Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "==================\n",
    "  *  __Title__: Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "  *  __Authors__: [Daniel Martin Katz](http://www.law.msu.edu/faculty_staff/profile.php?prof=780), [Michael J Bommarito II](http://bommaritollc.com/), [Josh Blackman](http://joshblackman.com)\n",
    "  *  __Paper URL__: [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244)\n",
    "  *  __Blog URL__: [http://lexpredict.com/portfolio/predicting-the-supreme-court/](http://lexpredict.com/portfolio/predicting-the-supreme-court/)\n",
    "\n",
    "## Paper Abstract\n",
    "Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized,and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).\n",
    "\n",
    "## Source Description\n",
    "The source and data in this repository allow for the reproduction of the results in this paper.  \n",
    "\n",
    "## Data Description\n",
    "The data used in this paper is available from the [Supreme Court Database (SCDB)](http://scdb.wustl.edu/).\n",
    "\n",
    "## Version\n",
    "The latest version of this model was relesed in October 2015."
   ]
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  {
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   "execution_count": 1,
   "metadata": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Imports\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.stats.proportion\n",
    "\n",
    "# seaborn\n",
    "import seaborn\n",
    "seaborn.set()\n",
    "seaborn.set_style(\"darkgrid\")\n",
    "\n",
    "# Project imports\n",
    "from model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Get raw data\n",
    "raw_data = get_raw_scdb_data(\"data/input/SCDB_2015_01_justiceCentered_Citation.csv\")\n",
    "\n",
    "# Get feature data\n",
    "feature_df = preprocess_raw_data(raw_data, include_direction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77342, 82)\n",
      "(77342, 1365)\n"
     ]
    }
   ],
   "source": [
    "# Output some diagnostics on features\n",
    "print(raw_data.shape)\n",
    "print(feature_df.shape)\n",
    "assert(raw_data.shape[0] == feature_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    justice_outcome_disposition\n",
      " 1                        43951\n",
      " 0                        26293\n",
      "-1                         7098\n",
      "    justice_outcome_disposition\n",
      " 1                     0.568268\n",
      " 0                     0.339958\n",
      "-1                     0.091774\n"
     ]
    }
   ],
   "source": [
    "# Output basic quantities for sample\n",
    "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts()))\n",
    "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts(normalize=True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
     "name": "stdout",
     "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",
      "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",
      "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",
      "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",
      "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",
      "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",
      "Term: 2011\n",
      "Term: 2012\n",
      "Term: 2013\n",
      "Term: 2014\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py:487: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight(\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn('class_weight presets \"balanced\" or \"balanced_subsample\" are '\n"
     ]
    }
   ],
   "source": [
    "# Setup training time period\n",
    "min_training_years = 5\n",
    "term_range = range(raw_data[\"term\"].min() + min_training_years,\n",
    "                   raw_data[\"term\"].max()+1)\n",
    "\n",
    "# Setting growing random forest parameters\n",
    "# Number of trees to grow per term\n",
    "trees_per_term = 10\n",
    "\n",
    "# Number of trees to begin with\n",
    "initial_trees = min_training_years * trees_per_term\n",
    "\n",
    "# Number of years between \"forest fires\"\n",
    "reset_interval = 9999\n",
    "\n",
    "# Setup model\n",
    "m = None\n",
    "term_count = 0\n",
    "\n",
    "for term in term_range:\n",
    "    # Diagnostic output\n",
    "    print(\"Term: {0}\".format(term))\n",
    "    term_count += 1\n",
    "    \n",
    "    # Setup train and test periods\n",
    "    train_index = (raw_data.loc[:, \"term\"] < term).values\n",
    "    test_index = (raw_data.loc[:, \"term\"] == term).values\n",
    "    \n",
    "    # Setup train data\n",
    "    feature_data_train = feature_df.loc[train_index, :]\n",
    "    target_data_train = raw_data.loc[train_index, \"justice_outcome_disposition\"].astype(int).values\n",
    "\n",
    "    # Setup test data\n",
    "    feature_data_test = feature_df.loc[test_index, :]\n",
    "    target_data_test = raw_data.loc[test_index, \"justice_outcome_disposition\"].astype(int).values\n",
    "                                              \n",
    "    # Build or grow a model depending on initial/reset condition\n",
    "    if not m:\n",
    "        # Grow an initial forest\n",
    "        m = sklearn.ensemble.RandomForestClassifier(n_estimators=initial_trees + (term_count * trees_per_term), \n",
    "                                                    class_weight=\"balanced_subsample\",\n",
    "                                                    warm_start=True,\n",
    "                                                    n_jobs=-1)\n",
    "    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.RandomForestClassifier(n_estimators=initial_trees + (term_count * trees_per_term),\n",
    "                                                    class_weight=\"balanced_subsample\",\n",
    "                                                    warm_start=True,\n",
    "                                                    n_jobs=-1)\n",
    "    else:\n",
    "        # Grow the forest by increasing the number of trees (requires warm_start=True)\n",
    "        m.set_params(n_estimators=initial_trees + (term_count * trees_per_term))\n",
    "\n",
    "    # Fit the forest model\n",
    "    m.fit(feature_data_train,\n",
    "          target_data_train)\n",
    "\n",
    "    # Fit the \"dummy\" model\n",
    "    d = sklearn.dummy.DummyClassifier(strategy=\"most_frequent\")\n",
    "    d.fit(feature_data_train, target_data_train)\n",
    "    \n",
    "    # Perform forest predictions\n",
    "    raw_data.loc[test_index, \"rf_predicted\"] = m.predict(feature_data_test)\n",
    "    \n",
    "    # Store scores per class\n",
    "    scores = m.predict_proba(feature_data_test)\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_other\"] = scores[:, 0]\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_affirm\"] = scores[:, 1]\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_reverse\"] = scores[:, 2]\n",
    "    \n",
    "    # Store dummy predictions\n",
    "    raw_data.loc[test_index, \"dummy_predicted\"] = d.predict(feature_data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.96      0.28      0.44      6700\n",
      "          0       0.58      0.27      0.37     24325\n",
      "          1       0.62      0.89      0.73     41142\n",
      "\n",
      "avg / total       0.64      0.63      0.58     72167\n",
      "\n",
      "[[ 1887   390  4423]\n",
      " [   48  6568 17709]\n",
      " [   23  4356 36763]]\n",
      "0.626574473097\n",
      "================================\n",
      "\n",
      "Dummy model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.00      0.00      0.00      6700\n",
      "          0       0.00      0.00      0.00     24325\n",
      "          1       0.57      1.00      0.73     41142\n",
      "\n",
      "avg / total       0.33      0.57      0.41     72167\n",
      "\n",
      "[[    0     0  6700]\n",
      " [    0     0 24325]\n",
      " [    0     0 41142]]\n",
      "0.57009436446\n",
      "================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Evaluation range\n",
    "evaluation_index = raw_data.loc[:, \"term\"].isin(term_range)\n",
    "target_actual = raw_data.loc[evaluation_index, \"justice_outcome_disposition\"]\n",
    "target_predicted = raw_data.loc[evaluation_index, \"rf_predicted\"]\n",
    "target_dummy = raw_data.loc[evaluation_index, \"dummy_predicted\"]\n",
    "raw_data.loc[:, \"rf_correct\"] = numpy.nan\n",
    "raw_data.loc[:, \"dummy_correct\"] = numpy.nan\n",
    "raw_data.loc[evaluation_index, \"rf_correct\"] = (target_actual == target_predicted).astype(float)\n",
    "raw_data.loc[evaluation_index, \"dummy_correct\"] = (target_actual == target_dummy).astype(float)\n",
    "\n",
    "# Compare model\n",
    "print(\"RF model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_predicted))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_predicted))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_predicted))\n",
    "print(\"=\"*32)\n",
    "print(\"\")\n",
    "\n",
    "# Dummy model\n",
    "print(\"Dummy model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_dummy))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_dummy))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_dummy))\n",
    "print(\"=\"*32)\n",
    "print(\"\")"
   ]
  },
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Y1BIRERERwZ0Vms1biEdUAEBAlep8RERUCZYfExEREREByOZNv+wYAJKaXsejIaJKMagl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YQ9MxwlqaYamG3eiDr73MSEX4AN7ceRvbjR14vpda23lnWFSWz4T/HtT5VFGUCqmz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\n7d27V1u3btUDDzwgy7J04403tvzcPPEdr+6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3y3EcjY6O6uMf/3i2x5K1z58nnnhCv/Zr\nv6Y3vvGNsixLlmXpIx/5iC6//HL9/u//vl5++WVt2rRJn/nMZ7JhYvfcc4++9KUvyXVd/dEf/ZGu\nuuoqSax/Hq1k/a+55hpNTU0pDEMNDg7qvvvu09atW1n/nGl17T/72c/q3nvv1ete9zoZY2RZlu67\n7z4NDw+vaO1fk1ALAAAAAMCrgUkMAAAAAIDcItQCAAAAAHKLUAsAAAAAyC1CLQAAAAAgtwi1AAAA\nAIDcItQCAAAAAHKLUAsAAAAAyK3/DwmTyInJjkWKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f702278b4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_correct_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"rf_correct\"].mean()\n",
    "dummy_correct_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"dummy_correct\"].mean()\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.plot(rf_correct_ts.index, rf_correct_ts,\n",
    "         marker='o', alpha=0.75)\n",
    "plt.plot(dummy_correct_ts.index, dummy_correct_ts,\n",
    "         marker='>', alpha=0.75)\n",
    "plt.legend(('Random forest', 'Dummy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7022654b90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AkMIVZnYq99cBAAA9hcDMTuX+OgAAoKcQmNnp3F8H0DojcgCg6xOYASAPjMgB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70226e6bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_spread_ts = rf_correct_ts - dummy_correct_ts\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.bar(rf_spread_ts.index, rf_spread_ts,\n",
    "        alpha=0.75)\n",
    "plt.xlabel(\"Term\")\n",
    "plt.ylabel(\"Spread (%)\")\n",
    "plt.title(\"Spread over dummy model for justice accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>decision_delay</td>\n",
       "      <td>0.028171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>justice_previous_lc_direction_diff</td>\n",
       "      <td>0.026368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>justice_cumulative_lc_direction_diff</td>\n",
       "      <td>0.025995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>issue_area_9</td>\n",
       "      <td>0.016174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>justice_cumulative_action</td>\n",
       "      <td>0.015409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>justice_previous_agreement</td>\n",
       "      <td>0.015095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>justice_previous_court_direction_diff</td>\n",
       "      <td>0.014940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>justice_previous_action</td>\n",
       "      <td>0.014886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>justice_cumulative_agreement</td>\n",
       "      <td>0.014832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>justice_previous_direction</td>\n",
       "      <td>0.014732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    feature  importance\n",
       "130                          decision_delay    0.028171\n",
       "1363     justice_previous_lc_direction_diff    0.026368\n",
       "1364   justice_cumulative_lc_direction_diff    0.025995\n",
       "1154                           issue_area_9    0.016174\n",
       "1352              justice_cumulative_action    0.015409\n",
       "1357             justice_previous_agreement    0.015095\n",
       "1349  justice_previous_court_direction_diff    0.014940\n",
       "1351                justice_previous_action    0.014886\n",
       "1358           justice_cumulative_agreement    0.014832\n",
       "1345             justice_previous_direction    0.014732"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature importance\n",
    "feature_importance_df = pandas.DataFrame(zip(feature_df.columns, m.feature_importances_),\n",
    "                                         columns=[\"feature\", \"importance\"])\n",
    "feature_importance_df.sort_values([\"importance\"], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
      "Ttest_relResult(statistic=13.317956183521696, pvalue=5.5695453951134186e-20)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=5.3374499616411635, pvalue=9.426290530279906e-08)\n",
      "================\n",
      "Binomial:\n",
      "3.16846166377e-209\n"
     ]
    }
   ],
   "source": [
    "# Output stats\n",
    "print(\"t-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ttest_rel(rf_correct_ts.values,\n",
    "                   dummy_correct_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"ranksum-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ranksums(rf_correct_ts.values,\n",
    "                   dummy_correct_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"Binomial:\")\n",
    "print(statsmodels.stats.proportion.binom_test(raw_data.loc[evaluation_index, \"rf_correct\"].sum(),\n",
    "                                              raw_data.loc[evaluation_index, \"rf_correct\"].shape[0],\n",
    "                                              raw_data.loc[evaluation_index, \"dummy_correct\"].mean(),\n",
    "                                              alternative=\"larger\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get case-level prediction\n",
    "#scdb_data.loc[evaluation_index, \"rf_predicted_case\"] = \n",
    "rf_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n",
    "    .groupby([\"docketId\"])[\"rf_predicted\"]\\\n",
    "    .agg(lambda x: x.value_counts().index[0]))\n",
    "rf_predicted_case.columns = [\"rf_predicted_case\"]\n",
    "\n",
    "dummy_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n",
    "    .groupby([\"docketId\"])[\"dummy_predicted\"]\\\n",
    "    .agg(lambda x: x.value_counts().index[0]))\n",
    "dummy_predicted_case.columns = [\"dummy_predicted_case\"]\n",
    "\n",
    "# Set DFs\n",
    "rf_predicted_case = raw_data[[\"docketId\", \"case_outcome_disposition\", \"rf_predicted\"]].join(rf_predicted_case, on=\"docketId\")\n",
    "dumy_predicted_case = raw_data[[\"docketId\", \"dummy_predicted\"]].join(dummy_predicted_case, on=\"docketId\")\n",
    "\n",
    "raw_data.loc[:, \"rf_predicted_case\"] = rf_predicted_case\n",
    "raw_data.loc[:, \"dummy_predicted_case\"] = dumy_predicted_case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1    5435\n",
      " 0    2639\n",
      "-1     433\n",
      "Name: case_outcome_disposition, dtype: int64\n",
      " 1    0.629707\n",
      " 0    0.305758\n",
      "-1    0.050168\n",
      "Name: case_outcome_disposition, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Output case distribution\n",
    "case_outcomes = raw_data.groupby([\"docketId\"])[\"case_outcome_disposition\"].agg(lambda x: x.mode())\n",
    "case_outcomes = case_outcomes.apply(lambda x: int(x) if type(x) in [numpy.float64] else None)\n",
    "print(case_outcomes.value_counts())\n",
    "print(case_outcomes.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.93      0.25      0.39      3676\n",
      "        0.0       0.53      0.18      0.27     21597\n",
      "        1.0       0.68      0.93      0.78     45782\n",
      "\n",
      "avg / total       0.64      0.66      0.60     71055\n",
      "\n",
      "[[  903    54  2719]\n",
      " [   54  3867 17676]\n",
      " [   18  3359 42405]]\n",
      "0.663922313701\n",
      "================================\n",
      "\n",
      "Dummy model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.00      0.00      0.00      3676\n",
      "        0.0       0.00      0.00      0.00     21597\n",
      "        1.0       0.64      1.00      0.78     45782\n",
      "\n",
      "avg / total       0.42      0.64      0.50     71055\n",
      "\n",
      "[[    0     0  3676]\n",
      " [    0     0 21597]\n",
      " [    0     0 45782]]\n",
      "0.644317782\n",
      "================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Output comparison\n",
    "# Evaluation range\n",
    "evaluation_index = raw_data.loc[:, \"term\"].isin(term_range) & -raw_data.loc[:, \"case_outcome_disposition\"].isnull()\n",
    "target_actual = raw_data.loc[evaluation_index, \"case_outcome_disposition\"]\n",
    "target_predicted = raw_data.loc[evaluation_index, \"rf_predicted_case\"]\n",
    "target_dummy = raw_data.loc[evaluation_index, \"dummy_predicted_case\"]\n",
    "\n",
    "raw_data.loc[:, \"rf_correct_case\"] = numpy.nan\n",
    "raw_data.loc[:, \"dummy_correct_case\"] = numpy.nan\n",
    "raw_data.loc[evaluation_index, \"rf_correct_case\"] = (target_actual == target_predicted).astype(float)\n",
    "raw_data.loc[evaluation_index, \"dummy_correct_case\"] = (target_actual == target_dummy).astype(float)\n",
    "\n",
    "# Compare model\n",
    "print(\"RF model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_predicted))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_predicted))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_predicted))\n",
    "print(\"=\"*32)\n",
    "print(\"\")\n",
    "\n",
    "# Dummy model\n",
    "print(\"Dummy model\")\n",
    "print(\"=\"*32)\n",
    "print(sklearn.metrics.classification_report(target_actual, target_dummy))\n",
    "print(sklearn.metrics.confusion_matrix(target_actual, target_dummy))\n",
    "print(sklearn.metrics.accuracy_score(target_actual, target_dummy))\n",
    "print(\"=\"*32)\n",
    "print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7022358110>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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iIiIiR2PgSkRERERERI7GwJWIiIiIiIgcjYErERERERERORoDVyIiIiIiInI0Bq5ERERE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\nXNPHyo7DCZt7XP04TL9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by/p/4xvfkCS98cYb+sAHPiBJWrFihQYHB/XCCy+w/gW1atUq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\nAACQa/8fy+UomqvsUr0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70226e6f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_correct_case_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"rf_correct_case\"].mean()\n",
    "dummy_correct_case_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"dummy_correct_case\"].mean()\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.plot(rf_correct_case_ts.index, rf_correct_case_ts,\n",
    "         marker='o', alpha=0.75)\n",
    "plt.plot(dummy_correct_case_ts.index, dummy_correct_case_ts,\n",
    "         marker='>', alpha=0.75)\n",
    "plt.legend(('Random forest', 'Dummy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f70222788d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gBgAAgBQCMwAAAKQQmAEAACCFwAwAAAApBGYAAABIITADAABACoEZAAAAUgjM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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f704801d950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_spread_case_ts = rf_correct_case_ts - dummy_correct_case_ts\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.bar(rf_spread_case_ts.index, rf_spread_case_ts,\n",
    "        alpha=0.75)\n",
    "plt.xlabel(\"Term\")\n",
    "plt.ylabel(\"Spread (%)\")\n",
    "plt.title(\"Spread over dummy model for case accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f70225b4e10>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PvBtdPcfisoUzY/lSqZqiWLO8PXa+3htv7x95o6VqUjVJRlM2//7E2/Hoju64746rUv39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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70225dc810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_spread_case_dir_ts = pandas.expanding_sum(numpy.sign(rf_spread_case_ts))\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.plot(rf_spread_case_dir_ts.index, rf_spread_case_dir_ts,\n",
    "        alpha=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
      "Ttest_relResult(statistic=3.7584207971683461, pvalue=0.0003763711009210788)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=1.2176057724993905, pvalue=0.2233738197596421)\n",
      "================\n",
      "Binomial:\n",
      "0.000125381247602\n"
     ]
    }
   ],
   "source": [
    "# Output stats\n",
    "print(\"t-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ttest_rel(rf_correct_case_ts.values,\n",
    "                   dummy_correct_case_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"ranksum-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ranksums(rf_correct_case_ts.values,\n",
    "                   dummy_correct_case_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"Binomial:\")\n",
    "case_accuracy_data = raw_data.loc[evaluation_index, [\"docketId\", \"rf_correct_case\", \"dummy_correct_case\"]].drop_duplicates()\n",
    "print(statsmodels.stats.proportion.binom_test(case_accuracy_data[\"rf_correct_case\"].sum(),\n",
    "                                              case_accuracy_data[\"rf_correct_case\"].shape[0],\n",
    "                                              case_accuracy_data[\"dummy_correct_case\"].mean(),\n",
    "                                              alternative=\"larger\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>22281</th>\n",
       "      <th>22282</th>\n",
       "      <th>22283</th>\n",
       "      <th>22284</th>\n",
       "      <th>22285</th>\n",
       "      <th>22286</th>\n",
       "      <th>22287</th>\n",
       "      <th>22288</th>\n",
       "      <th>22289</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caseName</th>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "      <td>MIRANDA v. ARIZONA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justiceName</th>\n",
       "      <td>JHarlan2</td>\n",
       "      <td>HLBlack</td>\n",
       "      <td>WODouglas</td>\n",
       "      <td>PStewart</td>\n",
       "      <td>WJBrennan</td>\n",
       "      <td>BRWhite</td>\n",
       "      <td>EWarren</td>\n",
       "      <td>TCClark</td>\n",
       "      <td>AFortas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case_outcome_disposition</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justice_outcome_disposition</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.365</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          22281               22282  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                            JHarlan2             HLBlack   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   0                   1   \n",
       "rf_predicted                                  0                   1   \n",
       "rf_predicted_score_affirm                  0.59                0.15   \n",
       "rf_predicted_score_reverse                0.365                0.78   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22283               22284  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                           WODouglas            PStewart   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   1   \n",
       "rf_predicted_score_affirm                  0.13               0.375   \n",
       "rf_predicted_score_reverse                 0.78                0.57   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22285               22286  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                           WJBrennan             BRWhite   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   1   \n",
       "rf_predicted_score_affirm                 0.265                0.31   \n",
       "rf_predicted_score_reverse                0.695                0.61   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22287               22288  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                             EWarren             TCClark   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   0   \n",
       "rf_predicted_score_affirm                  0.24                0.51   \n",
       "rf_predicted_score_reverse                 0.74                0.46   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22289  \n",
       "caseName                     MIRANDA v. ARIZONA  \n",
       "justiceName                             AFortas  \n",
       "case_outcome_disposition                      1  \n",
       "justice_outcome_disposition                   1  \n",
       "rf_predicted                                  0  \n",
       "rf_predicted_score_affirm                 0.525  \n",
       "rf_predicted_score_reverse                0.415  \n",
       "rf_correct_case                               1  \n",
       "dummy_correct_case                            1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"MIRANDA v. ARIZONA\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>77288</th>\n",
       "      <th>77289</th>\n",
       "      <th>77290</th>\n",
       "      <th>77291</th>\n",
       "      <th>77292</th>\n",
       "      <th>77293</th>\n",
       "      <th>77294</th>\n",
       "      <th>77295</th>\n",
       "      <th>77296</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caseName</th>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justiceName</th>\n",
       "      <td>JGRoberts</td>\n",
       "      <td>AScalia</td>\n",
       "      <td>AMKennedy</td>\n",
       "      <td>CThomas</td>\n",
       "      <td>RBGinsburg</td>\n",
       "      <td>SGBreyer</td>\n",
       "      <td>SAAlito</td>\n",
       "      <td>SSotomayor</td>\n",
       "      <td>EKagan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case_outcome_disposition</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justice_outcome_disposition</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
       "      <td>0.394203</td>\n",
       "      <td>0.386957</td>\n",
       "      <td>0.363768</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.315942</td>\n",
       "      <td>0.326087</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>0.286957</td>\n",
       "      <td>0.301449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.562319</td>\n",
       "      <td>0.584058</td>\n",
       "      <td>0.597101</td>\n",
       "      <td>0.571014</td>\n",
       "      <td>0.642029</td>\n",
       "      <td>0.636232</td>\n",
       "      <td>0.597101</td>\n",
       "      <td>0.676812</td>\n",
       "      <td>0.649275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dummy_correct_case</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            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",
       "rf_predicted_score_affirm                0.394203              0.386957   \n",
       "rf_predicted_score_reverse               0.562319              0.584058   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77290                 77291  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                             AMKennedy               CThomas   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     1                     0   \n",
       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                0.363768                   0.4   \n",
       "rf_predicted_score_reverse               0.597101              0.571014   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77292                 77293  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                            RBGinsburg              SGBreyer   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     1                     1   \n",
       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                0.315942              0.326087   \n",
       "rf_predicted_score_reverse               0.642029              0.636232   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77294                 77295  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                               SAAlito            SSotomayor   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     0                     1   \n",
       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                0.369565              0.286957   \n",
       "rf_predicted_score_reverse               0.597101              0.676812   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77296  \n",
       "caseName                     OBERGEFELL v. HODGES  \n",
       "justiceName                                EKagan  \n",
       "case_outcome_disposition                        1  \n",
       "justice_outcome_disposition                     1  \n",
       "rf_predicted                                    1  \n",
       "rf_predicted_score_affirm                0.301449  \n",
       "rf_predicted_score_reverse               0.649275  \n",
       "rf_correct_case                                 1  \n",
       "dummy_correct_case                              1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"OBERGEFELL v. HODGES\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>77126</th>\n",
       "      <th>77127</th>\n",
       "      <th>77128</th>\n",
       "      <th>77129</th>\n",
       "      <th>77130</th>\n",
       "      <th>77131</th>\n",
       "      <th>77132</th>\n",
       "      <th>77133</th>\n",
       "      <th>77134</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>caseName</th>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "      <td>KING v. BURWELL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justiceName</th>\n",
       "      <td>JGRoberts</td>\n",
       "      <td>AScalia</td>\n",
       "      <td>AMKennedy</td>\n",
       "      <td>CThomas</td>\n",
       "      <td>RBGinsburg</td>\n",
       "      <td>SGBreyer</td>\n",
       "      <td>SAAlito</td>\n",
       "      <td>SSotomayor</td>\n",
       "      <td>EKagan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case_outcome_disposition</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>justice_outcome_disposition</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf_predicted_score_affirm</th>\n",
       "      <td>0.516785</td>\n",
       "      <td>0.509511</td>\n",
       "      <td>0.512553</td>\n",
       "      <td>0.494203</td>\n",
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       "    <tr>\n",
       "      <th>rf_predicted_score_reverse</th>\n",
       "      <td>0.448433</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                       77126            77127  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
       "justiceName                        JGRoberts          AScalia   \n",
       "case_outcome_disposition                   0                0   \n",
       "justice_outcome_disposition                0                1   \n",
       "rf_predicted                               0                0   \n",
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       "rf_predicted_score_reverse          0.448433         0.451359   \n",
       "rf_correct_case                            1                1   \n",
       "dummy_correct_case                         0                0   \n",
       "\n",
       "                                       77128            77129  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
       "justiceName                        AMKennedy          CThomas   \n",
       "case_outcome_disposition                   0                0   \n",
       "justice_outcome_disposition                0                1   \n",
       "rf_predicted                               0                0   \n",
       "rf_predicted_score_affirm           0.512553         0.494203   \n",
       "rf_predicted_score_reverse          0.446868         0.463768   \n",
       "rf_correct_case                            1                1   \n",
       "dummy_correct_case                         0                0   \n",
       "\n",
       "                                       77130            77131  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
       "justiceName                       RBGinsburg         SGBreyer   \n",
       "case_outcome_disposition                   0                0   \n",
       "justice_outcome_disposition                0                0   \n",
       "rf_predicted                               0                0   \n",
       "rf_predicted_score_affirm           0.559045         0.537588   \n",
       "rf_predicted_score_reverse          0.398926         0.430528   \n",
       "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",
       "justice_outcome_disposition                1                0                0  \n",
       "rf_predicted                               0                0                0  \n",
       "rf_predicted_score_affirm           0.521433         0.574987         0.542496  \n",
       "rf_predicted_score_reverse          0.449581         0.387332         0.429968  \n",
       "rf_correct_case                            1                1                1  \n",
       "dummy_correct_case                         0                0                0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"KING v. BURWELL\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "                                                                      73130  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       JGRoberts   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
       "rf_predicted_score_affirm                                          0.390625   \n",
       "rf_predicted_score_reverse                                             0.55   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73131  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       JPStevens   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               0   \n",
       "rf_predicted                                                              0   \n",
       "rf_predicted_score_affirm                                           0.50625   \n",
       "rf_predicted_score_reverse                                         0.434375   \n",
       "rf_correct_case                                                           1   \n",
       "dummy_correct_case                                                        1   \n",
       "\n",
       "                                                                      73132  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         AScalia   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
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       "rf_correct_case                                                           1   \n",
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       "\n",
       "                                                                      73133  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       AMKennedy   \n",
       "case_outcome_disposition                                                  1   \n",
       "justice_outcome_disposition                                               1   \n",
       "rf_predicted                                                              1   \n",
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       "rf_correct_case                                                           1   \n",
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       "\n",
       "                                                                      73134  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         CThomas   \n",
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       "rf_correct_case                                                           1   \n",
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       "\n",
       "                                                                      73135  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                      RBGinsburg   \n",
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       "\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                                                              1   \n",
       "rf_predicted_score_affirm                                          0.463808   \n",
       "rf_predicted_score_reverse                                         0.481504   \n",
       "rf_correct_case                                                           1   \n",
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       "\n",
       "                                                                      73137  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         SAAlito   \n",
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       "rf_correct_case                                                           1   \n",
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       "\n",
       "                                                                      73138  \n",
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       "rf_correct_case                                                           1  \n",
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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    "raw_data.loc[raw_data.loc[:, \"caseName\"] == 'CITIZENS UNITED v. FEDERAL ELECTION COMMISSION',\n",
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    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
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   "outputs": [
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       "                                                      72257  \\\n",
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       "\n",
       "                                                      72258  \\\n",
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       "\n",
       "                                                      72259  \\\n",
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       "justiceName                                       AMKennedy   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               0   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.375806   \n",
       "rf_predicted_score_reverse                         0.559677   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72260  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                        DHSouter   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               1   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.396774   \n",
       "rf_predicted_score_reverse                         0.553226   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72261  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                         CThomas   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               0   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                               0.4   \n",
       "rf_predicted_score_reverse                         0.535484   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72262  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                      RBGinsburg   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               1   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.359677   \n",
       "rf_predicted_score_reverse                         0.572581   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72263  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                        SGBreyer   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               1   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.403226   \n",
       "rf_predicted_score_reverse                          0.53871   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72264  \\\n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER   \n",
       "justiceName                                       JGRoberts   \n",
       "case_outcome_disposition                                  0   \n",
       "justice_outcome_disposition                               0   \n",
       "rf_predicted                                              1   \n",
       "rf_predicted_score_affirm                          0.382258   \n",
       "rf_predicted_score_reverse                         0.556452   \n",
       "rf_correct_case                                           0   \n",
       "dummy_correct_case                                        0   \n",
       "\n",
       "                                                      72265  \n",
       "caseName                     DISTRICT OF COLUMBIA v. HELLER  \n",
       "justiceName                                         SAAlito  \n",
       "case_outcome_disposition                                  0  \n",
       "justice_outcome_disposition                               0  \n",
       "rf_predicted                                              1  \n",
       "rf_predicted_score_affirm                           0.38871  \n",
       "rf_predicted_score_reverse                         0.562903  \n",
       "rf_correct_case                                           0  \n",
       "dummy_correct_case                                        0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == 'DISTRICT OF COLUMBIA v. HELLER',\n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Write output\n",
    "import cPickle as pickle\n",
    "import time\n",
    "\n",
    "timestamp_suffix = time.strftime(\"%Y%m%d%H%M%S\")\n",
    "raw_data.to_csv(\"data/output/model_output_{0}_no_reset_{1}.csv\".format(trees_per_term, timestamp_suffix),\n",
    "                 index=False)\n",
    "# You probably don't want to do this, as the object can be tens of GB\n",
    "#pickle.dump(m, open(\"scotus_scdb_model_{0}.pickle\".format(timestamp_suffix), \"w\"))"
   ]
  }
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