build_scdb_model_20.ipynb

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    "Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "==================\n",
    "  *  __Title__: Predicting the Behavior of the Supreme Court of the United States: A General Approach\n",
    "  *  __Authors__: [Daniel Martin Katz](http://www.law.msu.edu/faculty_staff/profile.php?prof=780), [Michael J Bommarito II](http://bommaritollc.com/), [Josh Blackman](http://joshblackman.com)\n",
    "  *  __Paper URL__: [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244)\n",
    "  *  __Blog URL__: [http://lexpredict.com/portfolio/predicting-the-supreme-court/](http://lexpredict.com/portfolio/predicting-the-supreme-court/)\n",
    "\n",
    "## Paper Abstract\n",
    "Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized,and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).\n",
    "\n",
    "## Source Description\n",
    "The source and data in this repository allow for the reproduction of the results in this paper.  \n",
    "\n",
    "## Data Description\n",
    "The data used in this paper is available from the [Supreme Court Database (SCDB)](http://scdb.wustl.edu/).\n",
    "\n",
    "## Version\n",
    "The latest version of this model was relesed in October 2015."
   ]
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     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Imports\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.stats.proportion\n",
    "\n",
    "# seaborn\n",
    "import seaborn\n",
    "seaborn.set()\n",
    "seaborn.set_style(\"darkgrid\")\n",
    "\n",
    "# Project imports\n",
    "from model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Get raw data\n",
    "raw_data = get_raw_scdb_data(\"data/input/SCDB_2015_01_justiceCentered_Citation.csv\")\n",
    "\n",
    "# Get feature data\n",
    "feature_df = preprocess_raw_data(raw_data, include_direction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(77342, 82)\n",
      "(77342, 1365)\n"
     ]
    }
   ],
   "source": [
    "# Output some diagnostics on features\n",
    "print(raw_data.shape)\n",
    "print(feature_df.shape)\n",
    "assert(raw_data.shape[0] == feature_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    justice_outcome_disposition\n",
      " 1                        43951\n",
      " 0                        26293\n",
      "-1                         7098\n",
      "    justice_outcome_disposition\n",
      " 1                     0.568268\n",
      " 0                     0.339958\n",
      "-1                     0.091774\n"
     ]
    }
   ],
   "source": [
    "# Output basic quantities for sample\n",
    "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts()))\n",
    "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts(normalize=True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "text": [
      "Term: 1951\n",
      "Term: 1952\n",
      "Term: 1953\n",
      "Natural court change; rebuilding with 160 trees\n",
      "Term: 1954\n",
      "Natural court change; rebuilding with 180 trees\n",
      "Term: 1955\n",
      "Term: 1956\n",
      "Natural court change; rebuilding with 220 trees\n",
      "Term: 1957\n",
      "Natural court change; rebuilding with 240 trees\n",
      "Term: 1958\n",
      "Natural court change; rebuilding with 260 trees\n",
      "Term: 1959\n",
      "Natural court change; rebuilding with 280 trees\n",
      "Term: 1960\n",
      "Term: 1961\n",
      "Natural court change; rebuilding with 320 trees\n",
      "Term: 1962\n",
      "Natural court change; rebuilding with 340 trees\n",
      "Term: 1963\n",
      "Term: 1964\n",
      "Term: 1965\n",
      "Natural court change; rebuilding with 400 trees\n",
      "Term: 1966\n",
      "Term: 1967\n",
      "Natural court change; rebuilding with 440 trees\n",
      "Term: 1968\n",
      "Term: 1969\n",
      "Natural court change; rebuilding with 480 trees\n",
      "Term: 1970\n",
      "Term: 1971\n",
      "Natural court change; rebuilding with 520 trees\n",
      "Term: 1972\n",
      "Term: 1973\n",
      "Term: 1974\n",
      "Term: 1975\n",
      "Natural court change; rebuilding with 600 trees\n",
      "Term: 1976\n",
      "Natural court change; rebuilding with 620 trees\n",
      "Term: 1977\n",
      "Term: 1978\n",
      "Term: 1979\n",
      "Term: 1980\n",
      "Term: 1981\n",
      "Natural court change; rebuilding with 720 trees\n",
      "Term: 1982\n",
      "Term: 1983\n",
      "Term: 1984\n",
      "Term: 1985\n",
      "Term: 1986\n",
      "Natural court change; rebuilding with 820 trees\n",
      "Term: 1987\n",
      "Natural court change; rebuilding with 840 trees\n",
      "Term: 1988\n",
      "Term: 1989\n",
      "Term: 1990\n",
      "Natural court change; rebuilding with 900 trees\n",
      "Term: 1991\n",
      "Natural court change; rebuilding with 920 trees\n",
      "Term: 1992\n",
      "Term: 1993\n",
      "Natural court change; rebuilding with 960 trees\n",
      "Term: 1994\n",
      "Natural court change; rebuilding with 980 trees\n",
      "Term: 1995\n",
      "Term: 1996\n",
      "Term: 1997\n",
      "Term: 1998\n",
      "Term: 1999\n",
      "Term: 2000\n",
      "Term: 2001\n",
      "Term: 2002\n",
      "Term: 2003\n",
      "Term: 2004\n",
      "Term: 2005\n",
      "Natural court change; rebuilding with 1200 trees\n",
      "Term: 2006\n",
      "Natural court change; rebuilding with 1220 trees\n",
      "Term: 2007\n",
      "Term: 2008\n",
      "Term: 2009\n",
      "Natural court change; rebuilding with 1280 trees\n",
      "Term: 2010\n",
      "Natural court change; rebuilding with 1300 trees\n",
      "Term: 2011\n",
      "Term: 2012\n",
      "Term: 2013\n",
      "Term: 2014\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/ve/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py:487: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight(\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n",
      "  warn('class_weight presets \"balanced\" or \"balanced_subsample\" are '\n"
     ]
    }
   ],
   "source": [
    "# Setup training time period\n",
    "min_training_years = 5\n",
    "term_range = range(raw_data[\"term\"].min() + min_training_years,\n",
    "                   raw_data[\"term\"].max()+1)\n",
    "\n",
    "# Setting growing random forest parameters\n",
    "# Number of trees to grow per term\n",
    "trees_per_term = 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 = 9999\n",
    "\n",
    "# Setup model\n",
    "m = None\n",
    "term_count = 0\n",
    "\n",
    "for term in term_range:\n",
    "    # Diagnostic output\n",
    "    print(\"Term: {0}\".format(term))\n",
    "    term_count += 1\n",
    "    \n",
    "    # Setup train and test periods\n",
    "    train_index = (raw_data.loc[:, \"term\"] < term).values\n",
    "    test_index = (raw_data.loc[:, \"term\"] == term).values\n",
    "    \n",
    "    # Setup train data\n",
    "    feature_data_train = feature_df.loc[train_index, :]\n",
    "    target_data_train = raw_data.loc[train_index, \"justice_outcome_disposition\"].astype(int).values\n",
    "\n",
    "    # Setup test data\n",
    "    feature_data_test = feature_df.loc[test_index, :]\n",
    "    target_data_test = raw_data.loc[test_index, \"justice_outcome_disposition\"].astype(int).values\n",
    "    \n",
    "    # Check if we should rebuild the model based on changing natural court\n",
    "    if term_count % reset_interval == 0:\n",
    "        # \"Forest fire;\" grow a new forest from scratch\n",
    "        print(\"Reset interval hit; rebuilding with {0} trees\".format(initial_trees + (term_count * trees_per_term)))\n",
    "        m = None\n",
    "    else:\n",
    "        # Check if the justice set has changed\n",
    "        if set(raw_data.loc[raw_data.loc[:, \"term\"] == (term-1), \"justice\"].unique()) != \\\n",
    "            set(raw_data.loc[raw_data.loc[:, \"term\"] == (term), \"justice\"].unique()):\n",
    "            # natural Court change; trigger forest fire\n",
    "            print(\"Natural court change; rebuilding with {0} trees\".format(initial_trees + (term_count * trees_per_term)))\n",
    "        \n",
    "            m = None\n",
    "                                              \n",
    "    # Build or grow a model depending on initial/reset condition\n",
    "    if not m:\n",
    "        # Grow an initial forest\n",
    "        m = sklearn.ensemble.RandomForestClassifier(n_estimators=initial_trees + (term_count * trees_per_term), \n",
    "                                                    class_weight=\"balanced_subsample\",\n",
    "                                                    warm_start=True,\n",
    "                                                    n_jobs=-1)\n",
    "    else:\n",
    "        # Grow the forest by increasing the number of trees (requires warm_start=True)\n",
    "        m.set_params(n_estimators=initial_trees + (term_count * trees_per_term))\n",
    "\n",
    "    # Fit the forest model\n",
    "    m.fit(feature_data_train,\n",
    "          target_data_train)\n",
    "\n",
    "    # Fit the \"dummy\" model\n",
    "    d = sklearn.dummy.DummyClassifier(strategy=\"most_frequent\")\n",
    "    d.fit(feature_data_train, target_data_train)\n",
    "    \n",
    "    # Perform forest predictions\n",
    "    raw_data.loc[test_index, \"rf_predicted\"] = m.predict(feature_data_test)\n",
    "    \n",
    "    # Store scores per class\n",
    "    scores = m.predict_proba(feature_data_test)\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_other\"] = scores[:, 0]\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_affirm\"] = scores[:, 1]\n",
    "    raw_data.loc[test_index, \"rf_predicted_score_reverse\"] = scores[:, 2]\n",
    "    \n",
    "    # Store dummy predictions\n",
    "    raw_data.loc[test_index, \"dummy_predicted\"] = d.predict(feature_data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF model\n",
      "================================\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         -1       0.92      0.44      0.60      6700\n",
      "          0       0.61      0.34      0.43     24325\n",
      "          1       0.65      0.88      0.75     41142\n",
      "\n",
      "avg / total       0.66      0.66      0.63     72167\n",
      "\n",
      "[[ 2975   417  3308]\n",
      " [   95  8165 16065]\n",
      " [  165  4725 36252]]\n",
      "0.65669904527\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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eeSduv/12XHTRRYhEIiV9L9lchgYDuHpXO5o8Apo8Aq4abG+IqaFiSobTzi1b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\nAAAAEFuEWgAAAABAbP1/MFu0/swsABsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff86715e10>"
      ]
     },
     "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 0x7eff8648d710>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QmAEAACCFwAwAAAApBGYAAABIITADAABACoEZAAAAUgjMAAAAkEJgBgAAgBQC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff86501910>"
      ]
     },
     "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>0.030604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>justice_cumulative_lc_direction_diff</td>\n",
       "      <td>0.026934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>justice_previous_lc_direction_diff</td>\n",
       "      <td>0.026517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>justice_cumulative_action</td>\n",
       "      <td>0.015373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>justice_previous_agreement</td>\n",
       "      <td>0.015324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>issue_area_9</td>\n",
       "      <td>0.015277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>justice_previous_action</td>\n",
       "      <td>0.015260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>justice_cumulative_agreement</td>\n",
       "      <td>0.015200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>justice_cumulative_direction</td>\n",
       "      <td>0.014630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>justice_previous_court_direction_diff</td>\n",
       "      <td>0.014621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    feature  importance\n",
       "130                          decision_delay    0.030604\n",
       "1364   justice_cumulative_lc_direction_diff    0.026934\n",
       "1363     justice_previous_lc_direction_diff    0.026517\n",
       "1352              justice_cumulative_action    0.015373\n",
       "1357             justice_previous_agreement    0.015324\n",
       "1154                           issue_area_9    0.015277\n",
       "1351                justice_previous_action    0.015260\n",
       "1358           justice_cumulative_agreement    0.015200\n",
       "1346           justice_cumulative_direction    0.014630\n",
       "1349  justice_previous_court_direction_diff    0.014621"
      ]
     },
     "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=16.269065838625398, pvalue=3.0745658964347267e-24)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=7.1864951269239956, pvalue=6.6475661548431584e-13)\n",
      "================\n",
      "Binomial:\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Output stats\n",
    "print(\"t-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ttest_rel(rf_correct_ts.values,\n",
    "                   dummy_correct_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"ranksum-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ranksums(rf_correct_ts.values,\n",
    "                   dummy_correct_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"Binomial:\")\n",
    "print(statsmodels.stats.proportion.binom_test(raw_data.loc[evaluation_index, \"rf_correct\"].sum(),\n",
    "                                              raw_data.loc[evaluation_index, \"rf_correct\"].shape[0],\n",
    "                                              raw_data.loc[evaluation_index, \"dummy_correct\"].mean(),\n",
    "                                              alternative=\"larger\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get case-level prediction\n",
    "#scdb_data.loc[evaluation_index, \"rf_predicted_case\"] = \n",
    "rf_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n",
    "    .groupby([\"docketId\"])[\"rf_predicted\"]\\\n",
    "    .agg(lambda x: x.value_counts().index[0]))\n",
    "rf_predicted_case.columns = [\"rf_predicted_case\"]\n",
    "\n",
    "dummy_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n",
    "    .groupby([\"docketId\"])[\"dummy_predicted\"]\\\n",
    "    .agg(lambda x: x.value_counts().index[0]))\n",
    "dummy_predicted_case.columns = [\"dummy_predicted_case\"]\n",
    "\n",
    "# Set DFs\n",
    "rf_predicted_case = raw_data[[\"docketId\", \"case_outcome_disposition\", \"rf_predicted\"]].join(rf_predicted_case, on=\"docketId\")\n",
    "dumy_predicted_case = raw_data[[\"docketId\", \"dummy_predicted\"]].join(dummy_predicted_case, on=\"docketId\")\n",
    "\n",
    "raw_data.loc[:, \"rf_predicted_case\"] = rf_predicted_case\n",
    "raw_data.loc[:, \"dummy_predicted_case\"] = dumy_predicted_case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1    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       0.90      0.50      0.64      3676\n",
      "        0.0       0.54      0.19      0.29     21597\n",
      "        1.0       0.69      0.92      0.79     45782\n",
      "\n",
      "avg / total       0.65      0.68      0.63     71055\n",
      "\n",
      "[[ 1846    72  1758]\n",
      " [   63  4205 17329]\n",
      " [  144  3567 42071]]\n",
      "0.677250017592\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 0x7eff8621a3d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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IiIiIiBoaA1ciIiIiIiJqaAxciYiIiIiIqKExcCUiIiIiIqKGxsCViIiIiIiIGhoDVyIi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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff86434a50>"
      ]
     },
     "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 0x7eff8617c850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EZgAAACghMAMAAEAJgRkAAABKCMwAAABQQmAGAACAEgIzAAAAlBCYAQAAoITA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\nopW423eGTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff8650d190>"
      ]
     },
     "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 0x7eff863038d0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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f0DA+vrTokujt649Vf3gks9x+9eqe2L7nYFw6Y1IsuXqmd8pOIGMVAKAKSNQwXLkmbQYn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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff861e2a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup time series\n",
    "rf_spread_case_dir_ts = pandas.expanding_sum(numpy.sign(rf_spread_case_ts))\n",
    "\n",
    "# Plot all accuracies\n",
    "f = plt.figure(figsize=(16, 12))\n",
    "plt.plot(rf_spread_case_dir_ts.index, rf_spread_case_dir_ts,\n",
    "        alpha=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test:\n",
      "Uncalibrated:\n",
      "Ttest_relResult(statistic=6.5184373972802172, pvalue=1.3698241936620401e-08)\n",
      "================\n",
      "ranksum-test:\n",
      "Uncalibrated:\n",
      "RanksumsResult(statistic=2.4185320138686524, pvalue=0.015583274250562388)\n",
      "================\n",
      "Binomial:\n",
      "3.25599341663e-10\n"
     ]
    }
   ],
   "source": [
    "# Output stats\n",
    "print(\"t-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ttest_rel(rf_correct_case_ts.values,\n",
    "                   dummy_correct_case_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"ranksum-test:\")\n",
    "print(\"Uncalibrated:\")\n",
    "print(scipy.stats.ranksums(rf_correct_case_ts.values,\n",
    "                   dummy_correct_case_ts.values))\n",
    "\n",
    "print(\"=\" * 16)\n",
    "print(\"Binomial:\")\n",
    "case_accuracy_data = raw_data.loc[evaluation_index, [\"docketId\", \"rf_correct_case\", \"dummy_correct_case\"]].drop_duplicates()\n",
    "print(statsmodels.stats.proportion.binom_test(case_accuracy_data[\"rf_correct_case\"].sum(),\n",
    "                                              case_accuracy_data[\"rf_correct_case\"].shape[0],\n",
    "                                              case_accuracy_data[\"dummy_correct_case\"].mean(),\n",
    "                                              alternative=\"larger\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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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",
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      ],
      "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              0.505174                0.28   \n",
       "rf_predicted_score_reverse             0.479826              0.6825   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22283               22284  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                           WODouglas            PStewart   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   0   \n",
       "rf_predicted_score_affirm                0.1675               0.555   \n",
       "rf_predicted_score_reverse                 0.82                0.43   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22285               22286  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                           WJBrennan             BRWhite   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   0   \n",
       "rf_predicted_score_affirm                   0.3              0.5025   \n",
       "rf_predicted_score_reverse                0.655                0.47   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22287               22288  \\\n",
       "caseName                     MIRANDA v. ARIZONA  MIRANDA v. ARIZONA   \n",
       "justiceName                             EWarren             TCClark   \n",
       "case_outcome_disposition                      1                   1   \n",
       "justice_outcome_disposition                   1                   0   \n",
       "rf_predicted                                  1                   0   \n",
       "rf_predicted_score_affirm                 0.185              0.4975   \n",
       "rf_predicted_score_reverse                 0.74              0.4875   \n",
       "rf_correct_case                               1                   1   \n",
       "dummy_correct_case                            1                   1   \n",
       "\n",
       "                                          22289  \n",
       "caseName                     MIRANDA v. ARIZONA  \n",
       "justiceName                             AFortas  \n",
       "case_outcome_disposition                      1  \n",
       "justice_outcome_disposition                   1  \n",
       "rf_predicted                                  1  \n",
       "rf_predicted_score_affirm                0.3175  \n",
       "rf_predicted_score_reverse                 0.61  \n",
       "rf_correct_case                               1  \n",
       "dummy_correct_case                            1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"MIRANDA v. ARIZONA\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
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       "      <td>OBERGEFELL v. HODGES</td>\n",
       "      <td>OBERGEFELL v. HODGES</td>\n",
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       "      <th>dummy_correct_case</th>\n",
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      "text/plain": [
       "                                            77288                 77289  \\\n",
       "caseName                     OBERGEFELL v. HODGES  OBERGEFELL v. HODGES   \n",
       "justiceName                             JGRoberts               AScalia   \n",
       "case_outcome_disposition                        1                     1   \n",
       "justice_outcome_disposition                     0                     0   \n",
       "rf_predicted                                    1                     1   \n",
       "rf_predicted_score_affirm                0.268116              0.308696   \n",
       "rf_predicted_score_reverse               0.697826              0.672464   \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                0.260145               0.32029   \n",
       "rf_predicted_score_reverse               0.718116              0.662319   \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                0.288406              0.266667   \n",
       "rf_predicted_score_reverse               0.697101              0.718116   \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                0.294203               0.27029   \n",
       "rf_predicted_score_reverse               0.684058              0.715942   \n",
       "rf_correct_case                                 1                     1   \n",
       "dummy_correct_case                              1                     1   \n",
       "\n",
       "                                            77296  \n",
       "caseName                     OBERGEFELL v. HODGES  \n",
       "justiceName                                EKagan  \n",
       "case_outcome_disposition                        1  \n",
       "justice_outcome_disposition                     1  \n",
       "rf_predicted                                    1  \n",
       "rf_predicted_score_affirm                0.249275  \n",
       "rf_predicted_score_reverse               0.736232  \n",
       "rf_correct_case                                 1  \n",
       "dummy_correct_case                              1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"OBERGEFELL v. HODGES\", \n",
    "              [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n",
    "              \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "                                       77126            77127  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
       "justiceName                        JGRoberts          AScalia   \n",
       "case_outcome_disposition                   0                0   \n",
       "justice_outcome_disposition                0                1   \n",
       "rf_predicted                               1                0   \n",
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       "rf_correct_case                            1                1   \n",
       "dummy_correct_case                         0                0   \n",
       "\n",
       "                                       77128            77129  \\\n",
       "caseName                     KING v. BURWELL  KING v. BURWELL   \n",
       "justiceName                        AMKennedy          CThomas   \n",
       "case_outcome_disposition                   0                0   \n",
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       "rf_predicted                               1                1   \n",
       "rf_predicted_score_affirm            0.48913          0.48913   \n",
       "rf_predicted_score_reverse          0.492029              0.5   \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",
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       "rf_predicted                               0                0   \n",
       "rf_predicted_score_affirm           0.581159         0.571014   \n",
       "rf_predicted_score_reverse          0.399275         0.407246   \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                               1                0                0  \n",
       "rf_predicted_score_affirm           0.484783         0.598551         0.545652  \n",
       "rf_predicted_score_reverse          0.503623         0.385507         0.434058  \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": {
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   "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",
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       "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                                          0.558594   \n",
       "rf_predicted_score_reverse                                         0.402344   \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",
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       "\n",
       "                                                                      73133  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                       AMKennedy   \n",
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       "rf_predicted                                                              1   \n",
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       "\n",
       "                                                                      73134  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
       "justiceName                                                         CThomas   \n",
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       "\n",
       "                                                                      73135  \\\n",
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       "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                                                              0   \n",
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       "rf_correct_case                                                           1   \n",
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       "\n",
       "                                                                      73137  \\\n",
       "caseName                     CITIZENS UNITED v. FEDERAL ELECTION COMMISSION   \n",
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       "\n",
       "                                                                      73138  \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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       "                                                      72258  \\\n",
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       "\n",
       "                                                      72259  \\\n",
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       "\n",
       "                                                      72260  \\\n",
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       "\n",
       "                                                      72261  \\\n",
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       "\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                          0.373387   \n",
       "rf_predicted_score_reverse                         0.604032   \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                          0.331452   \n",
       "rf_predicted_score_reverse                         0.642742   \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                          0.357258   \n",
       "rf_predicted_score_reverse                         0.609677   \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                          0.373387  \n",
       "rf_predicted_score_reverse                         0.596774  \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}_{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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