{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# US Judge Ideology\n",
    "## By George Radner and Ian Sapollnik\n",
    "### ECON 407 Final Assignment\n",
    "### April 21, 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we assess the following questions:\n",
    "- How has the ideological position of US Federal District Court Judges changed over time?\n",
    "- What has driven these changes?\n",
    "- What influences how judges vote?\n",
    "\n",
    "This notebook is divided into various parts. Part 1 outlines the data used and cleans the data. Part 2 presents an overview of the data. Part 3 shows overall trends in ideology. Part 4 isolates specific effects. Part 5 looks at how judges make decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 0 - Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All packages used in this notebook are imported here.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import (\n",
    "    linear_model, metrics, neural_network, pipeline, preprocessing, model_selection, tree\n",
    ")\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "import plotly\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from scipy import stats\n",
    "import statsmodels as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "from patsy.builtins import *\n",
    "from patsy import dmatrices\n",
    "import statistics as st\n",
    "\n",
    "#Jellyfish is not included in syzygy. Uncomment if necessary.\n",
    "#!pip install jellyfish\n",
    "import jellyfish as jf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 - Data Collection and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attributes of Federal Judges Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main dataset for this analysis is the [Attributes of U.S. Federal Judges Database](http://artsandsciences.sc.edu/poli/juri/attributes.htm), from The Judicial Research Initiative (JuRI) at the University of South Carolina. The data was downloaded as a .dta file from the website, but had no data labels or encoding. The labeling file was written only for SAS. Before starting to clean the data in Python, we took the SAS cleaning code and manually changed it into Stata code in order to label the values and clean other portions of the data. After running this through Stata, the data was exported as a .csv file, and we do the final cleaning (everything that could possibly be done in Python) here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name_original</th>\n",
       "      <th>name</th>\n",
       "      <th>songer_code</th>\n",
       "      <th>circuit_original</th>\n",
       "      <th>circuit</th>\n",
       "      <th>id</th>\n",
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       "      <th>appres</th>\n",
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       "      <th>yearb</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abruzzo, Matthew J.</td>\n",
       "      <td>Abruzzo, Matthew J.</td>\n",
       "      <td>10201.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Second</td>\n",
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       "      <td>F. Roosevelt</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>1966</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acheson, Marcus Wilson</td>\n",
       "      <td>Acheson, Marcus Wilson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Third</td>\n",
       "      <td>4</td>\n",
       "      <td>Hayes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1891</td>\n",
       "      <td>1828.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acker, William Marsh, Jr.</td>\n",
       "      <td>Acker, William Marsh, Jr.</td>\n",
       "      <td>11101.0</td>\n",
       "      <td>11</td>\n",
       "      <td>Eleventh</td>\n",
       "      <td>10</td>\n",
       "      <td>Reagan</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1996</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ackerman, Harold Arnold</td>\n",
       "      <td>Ackerman, Harold Arnold</td>\n",
       "      <td>10301.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Third</td>\n",
       "      <td>15</td>\n",
       "      <td>Carter</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>1994</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ackerman, James Waldo</td>\n",
       "      <td>Ackerman, James Waldo</td>\n",
       "      <td>10701.0</td>\n",
       "      <td>7</td>\n",
       "      <td>Seventh</td>\n",
       "      <td>20</td>\n",
       "      <td>Ford</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1984</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 147 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name_original                       name  songer_code  \\\n",
       "0        Abruzzo, Matthew J.        Abruzzo, Matthew J.      10201.0   \n",
       "1     Acheson, Marcus Wilson     Acheson, Marcus Wilson          NaN   \n",
       "2  Acker, William Marsh, Jr.  Acker, William Marsh, Jr.      11101.0   \n",
       "3    Ackerman, Harold Arnold    Ackerman, Harold Arnold      10301.0   \n",
       "4      Ackerman, James Waldo      Ackerman, James Waldo      10701.0   \n",
       "\n",
       "   circuit_original   circuit  id          pres      appres  yearl   yearb  \\\n",
       "0                 2    Second   2  F. Roosevelt    Democrat   1966  1889.0   \n",
       "1                 3     Third   4         Hayes  Republican   1891  1828.0   \n",
       "2                11  Eleventh  10        Reagan  Republican   1996  1927.0   \n",
       "3                 3     Third  15        Carter    Democrat   1994  1928.0   \n",
       "4                 7   Seventh  20          Ford  Republican   1984  1926.0   \n",
       "\n",
       "   ...  pmayor pccoun pccom pada pda plother plotherl plawprof pprivate  \\\n",
       "0  ...     NaN    NaN   NaN  NaN NaN     NaN      NaN      NaN      1.0   \n",
       "1  ...     NaN    NaN   NaN  NaN NaN     NaN      NaN      NaN      1.0   \n",
       "2  ...     NaN    NaN   NaN  NaN NaN     NaN      NaN      NaN      1.0   \n",
       "3  ...     NaN    NaN   NaN  NaN NaN     NaN      NaN      NaN      1.0   \n",
       "4  ...     NaN    NaN   NaN  NaN NaN     1.0      3.0      NaN      1.0   \n",
       "\n",
       "  recdate  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
       "4     NaN  \n",
       "\n",
       "[5 rows x 147 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load raw data\n",
    "judge_att_data = pd.read_csv('Judge Attribute Data.csv')\n",
    "\n",
    "# Here is what it looks like right now\n",
    "judge_att_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Abruzzo, Matthew J.</td>\n",
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       "      <td>Retired</td>\n",
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       "      <td>Acheson, Marcus Wilson</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Hayes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1891</td>\n",
       "      <td>1828.0</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>B. Harrison</td>\n",
       "      <td>Elevated</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acker, William Marsh, Jr.</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Reagan</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1996</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>Retired</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ackerman, Harold Arnold</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>Carter</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>1994</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>Retired</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>51.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ackerman, James Waldo</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>Ford</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1984</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Reagan</td>\n",
       "      <td>Died</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
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      ],
      "text/plain": [
       "                        Name  Circuit  ID Appointing President  \\\n",
       "0        Abruzzo, Matthew J.        2   2         F. Roosevelt   \n",
       "1     Acheson, Marcus Wilson        3   4                Hayes   \n",
       "2  Acker, William Marsh, Jr.       11  10               Reagan   \n",
       "3    Ackerman, Harold Arnold        3  15               Carter   \n",
       "4      Ackerman, James Waldo        7  20                 Ford   \n",
       "\n",
       "  Appointing President Party  Year of Departure  Year of Birth  Year of Death  \\\n",
       "0                   Democrat               1966         1889.0         1971.0   \n",
       "1                 Republican               1891         1828.0         1906.0   \n",
       "2                 Republican               1996         1927.0         9999.0   \n",
       "3                   Democrat               1994         1928.0         9999.0   \n",
       "4                 Republican               1984         1926.0         1984.0   \n",
       "\n",
       "  President when Departed Reason for Departing  ... Previous Position - ccoun  \\\n",
       "0            L.B. Johnson              Retired  ...                         0   \n",
       "1             B. Harrison             Elevated  ...                         0   \n",
       "2                 Clinton              Retired  ...                         0   \n",
       "3                 Clinton              Retired  ...                         0   \n",
       "4                  Reagan                 Died  ...                         0   \n",
       "\n",
       "  Previous Position - ccom Previous Position - ada Previous Position - da  \\\n",
       "0                        0                       0                      0   \n",
       "1                        0                       0                      0   \n",
       "2                        0                       0                      0   \n",
       "3                        0                       0                      0   \n",
       "4                        0                       0                      0   \n",
       "\n",
       "  Previous Position - lother Previous Position - lotherl  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          0                           0   \n",
       "4                          1                           1   \n",
       "\n",
       "   Previous Position - lawprof Previous Position - private Politician  \\\n",
       "0                            0                           1          0   \n",
       "1                            0                           1          0   \n",
       "2                            0                           1          0   \n",
       "3                            0                           1          1   \n",
       "4                            0                           1          0   \n",
       "\n",
       "   Age When Appointed  \n",
       "0                47.0  \n",
       "1                52.0  \n",
       "2                55.0  \n",
       "3                51.0  \n",
       "4                50.0  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop unnecessary columns, rename necessary columns \n",
    "judge_att_data = judge_att_data.drop(columns = ['name_original','___l','___j','___char','elevate','dcother',\n",
    "                                                'liable', 'dummy','religion','circuit',\n",
    "                                                'songer_code','amon','crossl','pred','appt','temp',\n",
    "                                                'trans','liable','abamin','dsenate','rsenate','dhouse',\n",
    "                                                'rhouse','fhouse','fsenate','drhouse','drsenate',\n",
    "                                                'whouse','wsenate','nrhouse','nrsenate','dsens','rsens',\n",
    "                                                'yeari','yearc','e1','e2','e3','e4','e5','e6','congresi',\n",
    "                                                'unity','e7','e8','yearo','congreso','unityo','cityb',\n",
    "                                                'badeg','bastate','bastatus','jddeg','jdstate','jdstatus',\n",
    "                                                'grad1','grad2','tperm','fsens','drsens','wsens','nrsens',\n",
    "                                                'osens','agego','service','csb','ba','bast','bapp','ls',\n",
    "                                                'lsst','jdpp','graddeg1','graddeg2','statecab','state2',\n",
    "                                                'recdate','ageon'])\n",
    "judge_att_data = judge_att_data.rename(columns = {'name':'Name','circuit_original':'Circuit','id':'ID',\n",
    "                                        'pres':'Appointing President','yearl':'Year of Departure',\n",
    "                                        'yearb':'Year of Birth','yeard':'Year of Death',\n",
    "                                        'pleft':'President when Departed','left':'Reason for Departing',\n",
    "                                        'party':'Judge Party','district':'District','state':'State',\n",
    "                                        'city':'City','gender':'Gender','race':'Race',\n",
    "                                        'ayear':'Year of Appointment','crossa':'Cross Appointment',\n",
    "                                        'recess':'Recess Appointment','aba':'ABA Rating',\n",
    "                                        'assets':'Assets','congress':'Congress','unityi':'Unity',\n",
    "                                        'hdem':'House Democrats','hrep':'House Republicans',\n",
    "                                        'sdem':'Senate Democrats','srep':'Senate Republicans',\n",
    "                                        'hother':'House Independents','sother':'Senate Independents',\n",
    "                                        'networth':'Net Worth','appres':'Appointing President Party'})\n",
    "\n",
    "# Replace zero values with missing for net worth and assets\n",
    "def replace_zero_with_na(x):\n",
    "    if x == 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return x\n",
    "judge_att_data['Assets'] = judge_att_data['Assets'].apply(replace_zero_with_na)\n",
    "judge_att_data['Net Worth'] = judge_att_data['Net Worth'].apply(replace_zero_with_na)\n",
    "\n",
    "# Turn the position indicator columns into dummies and rename (these all start with 'p')\n",
    "def turn_into_dummy(val):\n",
    "    if np.isnan(val):\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "position_columns = list(filter(lambda col: col[0] == 'p', list(judge_att_data.columns)))\n",
    "for col in position_columns:\n",
    "    judge_att_data[col] = judge_att_data[col].apply(turn_into_dummy)\n",
    "    judge_att_data = judge_att_data.rename(columns = {col:'Previous Position - ' + col[1:]})\n",
    "    \n",
    "# Creating new variable for whether judge held any of the elected positions\n",
    "# These are the variables for the judge holding elected office of some kind     \n",
    "political_positions =  ['Previous Position - house', 'Previous Position - senate',\n",
    "                        'Previous Position - gov','Previous Position - ssenate',\n",
    "                        'Previous Position - shouse','Previous Position - mayor','Previous Position - ccoun']\n",
    "\n",
    "# Creating column of 0's which we will then fill\n",
    "judge_att_data[\"Politician\"] = 0*judge_att_data['Previous Position - house']\n",
    "for position in political_positions:\n",
    "    judge_att_data[\"Politician\"] = np.maximum(judge_att_data[\"Politician\"],judge_att_data[position])\n",
    "    \n",
    "# Creating new variable for judge's age at the time of appointment\n",
    "judge_att_data[\"Age When Appointed\"] = judge_att_data[\"Year of Appointment\"] - judge_att_data[\"Year of Birth\"]\n",
    "\n",
    "# Here is the data now\n",
    "judge_att_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Judge Ideology Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have data on individual judge ideology scores, which comes from [Christina L Boyd](http://clboyd.net/ideology.html) at the University of Georgia. In this dataset, a negative ideology score is more liberal, while a positive ideology score is more conservative. The scores range from -1 to 1.\n",
    "\n",
    "The judge names in the two datasets do not match perfectly. As a result, we must fuzzy match the names to obtain a high number matches between the two datasets. To do this, we calculate the [Jaro-Winkler distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) between each string in the Judge Attribute Data and the Ideology Data, and take a match for the highest scoring match for each name in the Attribute Data. The Jaro-Winkler distance assigns stronger weights to characters at the beginning of the string. Since the names are in the Last, First M. format, this makes last names more important than first names for the matching, which gives more accurate results. With some manual inspection, we chose 0.89 as the minimum score for an accurate name match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Ideology Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abrams, Leslie Joyce</td>\n",
       "      <td>-0.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abrams, Ronnie</td>\n",
       "      <td>-0.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acker, William Marsh</td>\n",
       "      <td>0.407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ackerman, Harold Arnold</td>\n",
       "      <td>-0.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ackerman, James Waldo</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name  Ideology Score\n",
       "0     Abrams, Leslie Joyce          -0.294\n",
       "1           Abrams, Ronnie          -0.302\n",
       "2     Acker, William Marsh           0.407\n",
       "3  Ackerman, Harold Arnold          -0.306\n",
       "4    Ackerman, James Waldo           0.061"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load ideology data\n",
    "judge_ideo_score = pd.read_excel('Judge Ideology Scores.xlsx')\n",
    "judge_ideo_score = judge_ideo_score[['judgename','ideology_score']]\n",
    "judge_ideo_score = judge_ideo_score.rename(columns = {'judgename':'Name','ideology_score':'Ideology Score'})\n",
    "\n",
    "# Here is what it looks like\n",
    "judge_ideo_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Closest Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Cote, Denise Louise</td>\n",
       "      <td>Cote, Denise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>Martin, John S., Jr.</td>\n",
       "      <td>Martin, John S.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Blackburn, Robert E.</td>\n",
       "      <td>Blackburn, Robert E.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>Payne, Robert Elkin</td>\n",
       "      <td>Payne, Robert E.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>Foley, James T.</td>\n",
       "      <td>Foley, James Thomas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>Kinkeade, James E.</td>\n",
       "      <td>Kinkeade, James E.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>Wilkerson, James Herbert</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Hall, Sam B., Jr.</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Bicks, Alexander</td>\n",
       "      <td>Bicks, Alexander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>Marchant, Henry</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name          Closest Name\n",
       "481        Cote, Denise Louise          Cote, Denise\n",
       "1438      Martin, John S., Jr.       Martin, John S.\n",
       "168       Blackburn, Robert E.  Blackburn, Robert E.\n",
       "1763       Payne, Robert Elkin      Payne, Robert E.\n",
       "731            Foley, James T.   Foley, James Thomas\n",
       "1236        Kinkeade, James E.    Kinkeade, James E.\n",
       "2446  Wilkerson, James Herbert                      \n",
       "902          Hall, Sam B., Jr.                      \n",
       "153           Bicks, Alexander      Bicks, Alexander\n",
       "1424           Marchant, Henry                      "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define function for getting the best matching from a given list. We will use this again later on.\n",
    "def get_best_name_match_from_list(name, data_list):\n",
    "    best_match = \"\"\n",
    "    highest_jw = 0\n",
    "    \n",
    "    for potential_match in data_list:\n",
    "        # This gives the Jaro-Winkler score which we use for matching\n",
    "        current_score = jf.jaro_winkler(potential_match, name)\n",
    "        if ((current_score > highest_jw) and (current_score > 0.89)):\n",
    "            highest_jw = current_score\n",
    "            best_match = potential_match\n",
    "    \n",
    "    return best_match\n",
    "\n",
    "# Create column of closest name\n",
    "judge_att_data['Closest Name'] = judge_att_data['Name'].apply(lambda x : get_best_name_match_from_list(x,judge_ideo_score['Name']))\n",
    "# Here is what some results look like. Note that blanks exist where no match was found.\n",
    "judge_att_data[['Name','Closest Name']].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge ideology data into attribute data using the closest match found\n",
    "judge_att_data = judge_att_data.merge(judge_ideo_score, left_on = 'Closest Name', right_on = 'Name', how = 'left')\n",
    "judge_att_data = judge_att_data.drop(columns = ['Name_y','Closest Name'])\n",
    "judge_att_data = judge_att_data.rename(columns = {'Name_x':'Name'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2561 entries, 0 to 2560\n",
      "Data columns (total 73 columns):\n",
      "Name                           2561 non-null object\n",
      "Circuit                        2561 non-null int64\n",
      "ID                             2561 non-null int64\n",
      "Appointing President           2561 non-null object\n",
      "Appointing President Party     2561 non-null object\n",
      "Year of Departure              2561 non-null int64\n",
      "Year of Birth                  2489 non-null float64\n",
      "Year of Death                  2494 non-null float64\n",
      "President when Departed        2559 non-null object\n",
      "Reason for Departing           2561 non-null object\n",
      "Judge Party                    2555 non-null object\n",
      "District                       2561 non-null object\n",
      "State                          2561 non-null object\n",
      "City                           2377 non-null object\n",
      "Gender                         2561 non-null object\n",
      "Race                           2561 non-null object\n",
      "Year of Appointment            2561 non-null int64\n",
      "Cross Appointment              2561 non-null object\n",
      "Recess Appointment             2561 non-null object\n",
      "Assets                         894 non-null float64\n",
      "ABA Rating                     2393 non-null object\n",
      "Congress                       1905 non-null object\n",
      "Unity                          2561 non-null object\n",
      "House Democrats                2561 non-null int64\n",
      "House Republicans              2561 non-null int64\n",
      "Senate Democrats               2561 non-null int64\n",
      "Senate Republicans             2561 non-null int64\n",
      "House Independents             2561 non-null int64\n",
      "Senate Independents            2561 non-null int64\n",
      "Net Worth                      894 non-null float64\n",
      "Previous Position - ssc        2561 non-null int64\n",
      "Previous Position - slc        2561 non-null int64\n",
      "Previous Position - locct      2561 non-null int64\n",
      "Previous Position - sjdget     2561 non-null int64\n",
      "Previous Position - ausa       2561 non-null int64\n",
      "Previous Position - usa        2561 non-null int64\n",
      "Previous Position - sgo        2561 non-null int64\n",
      "Previous Position - sg         2561 non-null int64\n",
      "Previous Position - ago        2561 non-null int64\n",
      "Previous Position - ag         2561 non-null int64\n",
      "Previous Position - cc         2561 non-null int64\n",
      "Previous Position - sp         2561 non-null int64\n",
      "Previous Position - mag        2561 non-null int64\n",
      "Previous Position - bank       2561 non-null int64\n",
      "Previous Position - terr       2561 non-null int64\n",
      "Previous Position - cab        2561 non-null int64\n",
      "Previous Position - asatty     2561 non-null int64\n",
      "Previous Position - satty      2561 non-null int64\n",
      "Previous Position - cabdept    2561 non-null int64\n",
      "Previous Position - scab       2561 non-null int64\n",
      "Previous Position - scabdpt    2561 non-null int64\n",
      "Previous Position - aag        2561 non-null int64\n",
      "Previous Position - indreg1    2561 non-null int64\n",
      "Previous Position - reg1       2561 non-null int64\n",
      "Previous Position - reg2       2561 non-null int64\n",
      "Previous Position - reg3       2561 non-null int64\n",
      "Previous Position - house      2561 non-null int64\n",
      "Previous Position - senate     2561 non-null int64\n",
      "Previous Position - gov        2561 non-null int64\n",
      "Previous Position - ssenate    2561 non-null int64\n",
      "Previous Position - shouse     2561 non-null int64\n",
      "Previous Position - mayor      2561 non-null int64\n",
      "Previous Position - ccoun      2561 non-null int64\n",
      "Previous Position - ccom       2561 non-null int64\n",
      "Previous Position - ada        2561 non-null int64\n",
      "Previous Position - da         2561 non-null int64\n",
      "Previous Position - lother     2561 non-null int64\n",
      "Previous Position - lotherl    2561 non-null int64\n",
      "Previous Position - lawprof    2561 non-null int64\n",
      "Previous Position - private    2561 non-null int64\n",
      "Politician                     2561 non-null int64\n",
      "Age When Appointed             2489 non-null float64\n",
      "Ideology Score                 1881 non-null float64\n",
      "dtypes: float64(6), int64(51), object(16)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "judge_att_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Judge Decision Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we have data on decisions made by US Federal District Court judges. This comes from [The Carp-Manning U.S. District Court Case Database](https://www.umassd.edu/cas/polisci/resources/us-district-court-database/). For every decision, the dataset assigns a 'liberal' or 'conservative' designation to the decision that was made, and states which judge authored the decision. The dataset also has information on the court that made the decision, when the decision was made, and what legal category the decision falls under. We take this data and merge the Judge Attribute Data into it, again using Jaro-Winkler fuzzy matching. Thus we can also explore what influences specific decisions that judges have made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Authoring Judge</th>\n",
       "      <th>Court Location</th>\n",
       "      <th>Number of Judges</th>\n",
       "      <th>Circuit</th>\n",
       "      <th>State</th>\n",
       "      <th>District</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Decision Ideology</th>\n",
       "      <th>Case Type</th>\n",
       "      <th>Case Category</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Year of Appointment</th>\n",
       "      <th>Appointing President</th>\n",
       "      <th>Judge Party</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avis, John Boyd</td>\n",
       "      <td>Camden, NJ</td>\n",
       "      <td>1</td>\n",
       "      <td>3RD CIRCUIT</td>\n",
       "      <td>NEW JERSEY</td>\n",
       "      <td>New Jersey Dist of NJ</td>\n",
       "      <td>July</td>\n",
       "      <td>1932</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>alien petitions</td>\n",
       "      <td>Civil Liberties/Rights Cases</td>\n",
       "      <td>10010028</td>\n",
       "      <td>1929</td>\n",
       "      <td>HOOVER</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avis, John Boyd</td>\n",
       "      <td>Camden, NJ</td>\n",
       "      <td>1</td>\n",
       "      <td>3RD CIRCUIT</td>\n",
       "      <td>NEW JERSEY</td>\n",
       "      <td>New Jersey Dist of NJ</td>\n",
       "      <td>July</td>\n",
       "      <td>1932</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>alien petitions</td>\n",
       "      <td>Civil Liberties/Rights Cases</td>\n",
       "      <td>10010029</td>\n",
       "      <td>1929</td>\n",
       "      <td>HOOVER</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Strum, Louie W.</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>1</td>\n",
       "      <td>5TH CIRCUIT</td>\n",
       "      <td>FLORIDA</td>\n",
       "      <td>Florida Southern Dist</td>\n",
       "      <td>September</td>\n",
       "      <td>1932</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>(non)conv-criminal case</td>\n",
       "      <td>Criminal Justice Cases</td>\n",
       "      <td>10010033</td>\n",
       "      <td>1931</td>\n",
       "      <td>HOOVER</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Moscowitz, Grover</td>\n",
       "      <td>Brooklyn, NY</td>\n",
       "      <td>5</td>\n",
       "      <td>2ND CIRCUIT</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>New York Eastern Dist</td>\n",
       "      <td>July</td>\n",
       "      <td>1932</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>criminal court motions</td>\n",
       "      <td>Criminal Justice Cases</td>\n",
       "      <td>10010104</td>\n",
       "      <td>1925</td>\n",
       "      <td>COOLIDGE</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cochran, Andrew</td>\n",
       "      <td>Maysville, KY</td>\n",
       "      <td>1</td>\n",
       "      <td>6TH CIRCUIT</td>\n",
       "      <td>KENTUCKY</td>\n",
       "      <td>Kentucky Eastern Dist</td>\n",
       "      <td>March</td>\n",
       "      <td>1932</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>voting rights</td>\n",
       "      <td>Civil Liberties/Rights Cases</td>\n",
       "      <td>10010142</td>\n",
       "      <td>1901</td>\n",
       "      <td>MCKINLEY</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Authoring Judge    Court Location  Number of Judges      Circuit  \\\n",
       "0    Avis, John Boyd        Camden, NJ                 1  3RD CIRCUIT   \n",
       "1    Avis, John Boyd        Camden, NJ                 1  3RD CIRCUIT   \n",
       "2    Strum, Louie W.  Jacksonville, FL                 1  5TH CIRCUIT   \n",
       "3  Moscowitz, Grover      Brooklyn, NY                 5  2ND CIRCUIT   \n",
       "4    Cochran, Andrew     Maysville, KY                 1  6TH CIRCUIT   \n",
       "\n",
       "        State               District      Month  Year Decision Ideology  \\\n",
       "0  NEW JERSEY  New Jersey Dist of NJ       July  1932      Conservative   \n",
       "1  NEW JERSEY  New Jersey Dist of NJ       July  1932      Conservative   \n",
       "2     FLORIDA  Florida Southern Dist  September  1932      Conservative   \n",
       "3    NEW YORK  New York Eastern Dist       July  1932      Conservative   \n",
       "4    KENTUCKY  Kentucky Eastern Dist      March  1932           Liberal   \n",
       "\n",
       "                 Case Type                 Case Category  Case Number  \\\n",
       "0          alien petitions  Civil Liberties/Rights Cases     10010028   \n",
       "1          alien petitions  Civil Liberties/Rights Cases     10010029   \n",
       "2  (non)conv-criminal case        Criminal Justice Cases     10010033   \n",
       "3   criminal court motions        Criminal Justice Cases     10010104   \n",
       "4            voting rights  Civil Liberties/Rights Cases     10010142   \n",
       "\n",
       "   Year of Appointment Appointing President Judge Party Gender  \\\n",
       "0                 1929               HOOVER  Republican   male   \n",
       "1                 1929               HOOVER  Republican   male   \n",
       "2                 1931               HOOVER    Democrat   male   \n",
       "3                 1925             COOLIDGE  Republican   male   \n",
       "4                 1901             MCKINLEY  Republican   male   \n",
       "\n",
       "              Race  \n",
       "0  white/caucasian  \n",
       "1  white/caucasian  \n",
       "2  white/caucasian  \n",
       "3  white/caucasian  \n",
       "4  white/caucasian  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data and rename columns\n",
    "decision_data = pd.read_csv('Carp-Manning.csv')\n",
    "decision_data = decision_data.rename(columns\n",
    "                                     = {'judge':'Authoring Judge','crtpoint':'Court Location',\n",
    "                                        'numjudge':'Number of Judges','circuit':'Circuit',\n",
    "                                        'state':'State','statdist':'District','month':'Month',\n",
    "                                        'year':'Year','libcon':'Decision Ideology',\n",
    "                                        'casetype':'Case Type','category':'Case Category',\n",
    "                                        'casnum':'Case Number','apyear':'Year of Appointment',\n",
    "                                        'appres':'Appointing President','party':'Judge Party',\n",
    "                                        'gender':'Gender','race':'Race'})\n",
    "\n",
    "# Here is what the data looks like\n",
    "decision_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get best match in Attributes Data\n",
    "decision_data['Closest Name'] = decision_data['Authoring Judge'].apply(lambda x : get_best_name_match_from_list(x,judge_att_data['Name']))\n",
    "# Here are some matches\n",
    "decision_data[['Authoring Judge','Closest Name']].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now merge based on these matches\n",
    "decision_data = decision_data.merge(judge_att_data, left_on = 'Closest Name', right_on = 'Name', how = 'left')\n",
    "\n",
    "# Get rid of duplicate columns\n",
    "duplicate_cols = list(filter(lambda col: col[-2:] == '_y', list(decision_data.columns)))\n",
    "decision_data = decision_data.drop(columns = duplicate_cols)\n",
    "decision_data = decision_data.drop(columns = ['Closest Name','Name'])\n",
    "duplicate_cols = [col[:-2] for col in duplicate_cols]\n",
    "for col in duplicate_cols:\n",
    "    decision_data = decision_data.rename(columns = {col + '_x': col})\n",
    "    \n",
    "# Here is what it looks like now\n",
    "decision_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Data Overview and Charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now present some graphs, charts and map that highlight key attributes of the Judge Attribute and Ideology Data. Ideology data are not consistent before 1956, so we eliminate years before this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ideology data are not consistent before 1956, so we eliminate years before this.\n",
    "judges = judge_att_data[judge_att_data[\"Year of Appointment\"] > 1956].copy()\n",
    "\n",
    "\n",
    "# Rescaling the ideology variable for presentation\n",
    "judges[\"Ideology Score\"] = judges[\"Ideology Score\"].apply(lambda x: x*100)\n",
    "\n",
    "# The absolute value of the ideology score measures a judge's distance from the ideological centre\n",
    "judges[\"Absolute Ideology\"] = judges[\"Ideology Score\"].apply(abs)\n",
    "\n",
    "# The dataset includes attributes of judges including:\n",
    "# Gender, race, age, politican party, past experience (including in politics)\n",
    "judges.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Judge Ideology Over Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To provide motivation for our analysis, we will create a graph showing how the average ideology of judges shifts over time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe with mean ideology and absolute ideology by year\n",
    "year_ideology = judges.groupby(\"Year of Appointment\")[[\"Absolute Ideology\",\"Ideology Score\"]].mean()\n",
    "year_ideology.reset_index(inplace = True)\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "fig, ax = plt.subplots(2,1,figsize=(11,8.5))\n",
    "\n",
    "# Hide grid lines, set a white face color, and set yaxis label\n",
    "for counter, value in enumerate([\"Mean Ideology Score\",\"Mean Absolute Ideology Score\"]):\n",
    "    ax[counter].set_facecolor('white')\n",
    "    ax[counter].grid(False)\n",
    "    ax[counter].set_ylabel(value)\n",
    "\n",
    "# Plot mean ideology, absolute ideology over time \n",
    "for counter, value in enumerate([\"Ideology Score\",\"Absolute Ideology\"]):\n",
    "    ax[counter].plot(year_ideology[\"Year of Appointment\"],\n",
    "                    year_ideology[value],\"-o\")\n",
    "      \n",
    "ax[0].set_title(\"Judge Ideology Varies by President...\")   \n",
    "ax[1].set_title(\"...But Absolute Ideology is on the Rise\")\n",
    "\n",
    "ax[1].set_xlabel(\"Year of Judge Appointment\")\n",
    "\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Judge Ideology By Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above showed judges have become more ideological. Our next question is whether this trend occurred in Democratic judges, Republican judges, or both? Since ideology varies so much based on the president in power, we'll look at the judge's year of birth, rather than year of appointment to see whether newer judges are more ideoligical than older ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_ideology = judges.groupby([\"Year of Birth\",\"Judge Party\"])[[\"Ideology Score\",\"Absolute Ideology\"]].mean()\n",
    "age_ideology.reset_index(inplace = True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11,8.5))\n",
    "\n",
    "ax.set_facecolor('white')\n",
    "ax.grid(False)\n",
    "\n",
    "# Data is sparse for judges born before 1900 \n",
    "initial_age = 1900\n",
    "\n",
    "recent = age_ideology[\"Year of Birth\"] >= initial_age\n",
    "\n",
    "democrats = age_ideology[\"Judge Party\"] == \"Democrat\"\n",
    "republicans = age_ideology[\"Judge Party\"] == \"Republican\"\n",
    "\n",
    "ax.plot(age_ideology[democrats & recent][\"Year of Birth\"],\n",
    "        age_ideology[democrats & recent][\"Ideology Score\"],\"-o\",label=\"Democrats\",color=\"#0e44f5\")\n",
    "\n",
    "ax.plot(age_ideology[republicans & recent][\"Year of Birth\"],\n",
    "        age_ideology[republicans & recent][\"Ideology Score\"],\"-o\",label=\"Republicans\",color=\"#f23417\")\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "ax.set_title(\"Divergence of Judge Ideology By Party\")\n",
    "\n",
    "ax.set_xlabel(\"Year of Birth\")\n",
    "ax.set_ylabel(\"Mean Ideology Score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Judge Ideology Over Time and States"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we explore how ideology, both in real and absolute terms, has varied across states and time. We note that this appears to be very dependent on who is president."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load crosswalk between state names and state codes\n",
    "state_name_to_code = pd.read_csv('State Name to Code.csv')\n",
    "state_name_to_code.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean judge ideology data and take only necessary columns\n",
    "map_data = judge_att_data[['Name','State','Year of Appointment','Year of Departure','Ideology Score']].copy()\n",
    "map_data = map_data[map_data['State'] != 'Puerto Rico']\n",
    "map_data = map_data.merge(state_name_to_code, how = 'left')\n",
    "map_data = map_data[map_data['Ideology Score'].apply(lambda x : ~np.isnan(x))]\n",
    "\n",
    "# Create empty dataframe to be filled by each unique judge-year pair\n",
    "# A judge-year pair exists if the judge was active in the year\n",
    "judge_ideo_by_year = pd.DataFrame(columns = ['Name','State','StateCode','Year','Ideology Score'])\n",
    "\n",
    "# Fill dataframe\n",
    "for index, row in map_data.iterrows():\n",
    "    name = row['Name']\n",
    "    state = row['State']\n",
    "    statecode = row['StateCode']\n",
    "    app_year = int(row['Year of Appointment'])\n",
    "    # If the judge is still active in 2004, the year of departure is '9999'\n",
    "    dep_year = np.min([2004, int(row['Year of Departure'])])\n",
    "    ideo = row['Ideology Score']\n",
    "    for year in range(app_year, dep_year + 1):\n",
    "        judge_ideo_by_year = judge_ideo_by_year.append({'Name':name,'State':state,'StateCode':statecode,\n",
    "                                                        'Year':year,'Ideology Score':ideo},\n",
    "                                                        ignore_index = True)\n",
    "# Add absolute ideology score\n",
    "judge_ideo_by_year['Absolute Ideology Score'] = judge_ideo_by_year['Ideology Score'].apply(np.abs)\n",
    "# Now group by year and state, and take means\n",
    "state_ideo_by_year = judge_ideo_by_year.groupby(['Year','State','StateCode']).mean().reset_index()\n",
    "# Scale ideology score for readability\n",
    "state_ideo_by_year['Ideology Score'] = state_ideo_by_year['Ideology Score'].apply(lambda x: 100*x)\n",
    "state_ideo_by_year['Absolute Ideology Score'] = state_ideo_by_year['Absolute Ideology Score'].apply(lambda x: 100*x)\n",
    "# Here is what a random sample of the data looks like\n",
    "state_ideo_by_year.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we make interactive map of average state judge ideology by year. For the two maps presented, use the slider to see ideology changing by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dict of US presidents by year\n",
    "president_by_year = dict(\n",
    "                    [(n, 'George W. Bush')\n",
    "                        for n in range(2001, 2005)] +\n",
    "                    [(n, 'Bill Clinton')\n",
    "                        for n in range(1993, 2001)] +\n",
    "                    [(n, 'George H. W. Bush')\n",
    "                        for n in range(1989, 1993)] +\n",
    "                    [(n, 'Ronald Reagan')\n",
    "                        for n in range(1981, 1989)] +\n",
    "                    [(n, 'Jimmy Carter')\n",
    "                        for n in range(1977, 1981)] +\n",
    "                    [(n, 'Gerald Ford')\n",
    "                        for n in range(1975, 1977)] +\n",
    "                    [(n, 'Richard Nixon')\n",
    "                        for n in range(1969, 1975)] +\n",
    "                    [(n, 'Lyndon B. Johnson')\n",
    "                        for n in range(1964, 1969)] +\n",
    "                    [(n, 'John F. Kennedy')\n",
    "                        for n in range(1961, 1964)] +\n",
    "                    [(n, 'Dwight D. Eisenhower')\n",
    "                        for n in range(1956, 1961)]\n",
    "                    )\n",
    "\n",
    "# Define plotting function.\n",
    "def plot_ideology_by_year(beginyear = 1956, scl = None, absolute = False):\n",
    "    \n",
    "    plotly.offline.init_notebook_mode()\n",
    "    \n",
    "    if absolute:\n",
    "        ideo = 'Absolute Ideology Score'\n",
    "        zmin = 0\n",
    "        text = 'Average Absolute Judge Ideology Score by State and Year'\n",
    "    else:\n",
    "        ideo = 'Ideology Score'\n",
    "        zmin = -60\n",
    "        text = 'Average Judge Ideology Score by State and Year'\n",
    "\n",
    "    # Create dict of data to feed into plotly. \n",
    "    data = [dict(type='choropleth',\n",
    "                 marker = go.choropleth.Marker(\n",
    "                     line = go.choropleth.marker.Line(\n",
    "                         color = 'rgb(255,255,255)',\n",
    "                         width = 1.5\n",
    "                     )),\n",
    "                 hoverinfo = 'z+text',\n",
    "                 colorbar = go.choropleth.ColorBar(\n",
    "                     title = ideo,\n",
    "                     thickness = 40\n",
    "                 ),\n",
    "                 colorscale = scl,\n",
    "                 zmin = zmin,\n",
    "                 zmax = 60,\n",
    "                 autocolorscale = False,\n",
    "                 locations = state_ideo_by_year[state_ideo_by_year['Year'] == year]['StateCode'],\n",
    "                 z = state_ideo_by_year[state_ideo_by_year['Year'] == year][ideo].astype(float),\n",
    "                 text = state_ideo_by_year[state_ideo_by_year['Year'] == year]['State'],\n",
    "                 locationmode='USA-states') \n",
    "            for year in range(beginyear,2005)]\n",
    "\n",
    "    # Create slider for the map\n",
    "    steps = []\n",
    "    for i in range(len(data)):\n",
    "        step = dict(method='update',\n",
    "                    args = [\n",
    "                        # Make the ith trace visible\n",
    "                        {'visible': [False for t in range(len(data))]},\n",
    "\n",
    "                        # Set the title for the ith trace\n",
    "                        {'title.text': text + \"<br />\" + f\"President is {president_by_year[i+beginyear]}\"}],\n",
    "                    label='Year {}'.format(i + beginyear))\n",
    "        step['args'][0]['visible'][i] = True\n",
    "        steps.append(step)\n",
    "    sliders = [dict(active=0,\n",
    "                    pad={\"t\": 1},\n",
    "                    steps=steps)]    \n",
    "\n",
    "    # Define layout\n",
    "    layout = dict(geo = go.layout.Geo(\n",
    "                    scope = 'usa',\n",
    "                    projection = go.layout.geo.Projection(type = 'albers usa')),\n",
    "                  sliders=sliders,\n",
    "                  title = {'text': text + \"<br />\" + f\"President is {president_by_year[i+beginyear]}\"}\n",
    "                 )\n",
    "\n",
    "    # Create map with plotly\n",
    "    fig = dict(data=data, layout=layout)\n",
    "    return plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This first map presents judge ideology in absolute terms. Unsurprisingly, as the presidency changes from one party to another, we see judge ideology follow the President's ideology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Choose a colour scale from blue (democrat) to red (republican)\n",
    "demrep_scl = [\n",
    "    [0.0, 'rgb(0,24,229)'],\n",
    "    [0.2, 'rgb(38,19,186)'],\n",
    "    [0.4, 'rgb(76,14,144)'],\n",
    "    [0.6, 'rgb(114,9,102)'],\n",
    "    [0.8, 'rgb(152,4,60)'],\n",
    "    [1.0, 'rgb(191,0,18)']\n",
    "]\n",
    "\n",
    "plot_ideology_by_year(scl = demrep_scl, absolute = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we show the same map with absolute ideology. We see absolute ideology increasing over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose a colour scale from light orange (low) to dark orange (high)\n",
    "hl_scl = [\n",
    "    [0.0, 'rgb(247,232,206)'],\n",
    "    [0.2, 'rgb(248,219,171)'],\n",
    "    [0.4, 'rgb(249,207,137)'],\n",
    "    [0.6, 'rgb(250,194,102)'],\n",
    "    [0.8, 'rgb(251,182,68)'],\n",
    "    [1.0, 'rgb(252,170,34)']\n",
    "]\n",
    "\n",
    "plot_ideology_by_year(scl = hl_scl, absolute = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3 - Judge Characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now move on to an analysis of the judge attribute and ideology data. Specifically, we attempt to determine how ideology is impacted by characteristics of the judges. We begin by preparing the data for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ideology</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Absolute Ideology</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Train MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Train MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OLS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural Network</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Ideology          Absolute Ideology         \n",
       "               Train MSE Test MSE         Train MSE Test MSE\n",
       "OLS                  NaN      NaN               NaN      NaN\n",
       "Lasso                NaN      NaN               NaN      NaN\n",
       "Random Forest        NaN      NaN               NaN      NaN\n",
       "Neural Network       NaN      NaN               NaN      NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prep data for prediction\n",
    "def prep_data(df, continuous_variables, categories, y_var, test_size=0.15):\n",
    "\n",
    "    ohe = preprocessing.OneHotEncoder(sparse=False)\n",
    "\n",
    "    y = df[y_var].values\n",
    "    X = np.zeros((y.size, 0))\n",
    "\n",
    "    # Add continuous variables if exist\n",
    "    if len(continuous_variables) > 0:\n",
    "        X = np.hstack([X, df[continuous_variables].values])\n",
    "\n",
    "    if len(categories) > 0:\n",
    "        X = np.hstack([X, ohe.fit_transform(df[categories])])\n",
    "\n",
    "    X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "        X, y, test_size=test_size, random_state=42\n",
    "    )\n",
    "\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "# This function will allow us to compare the MSE's of each model\n",
    "def fit_and_report_mses(mod, X_train, X_test, y_train, y_test):\n",
    "    mod.fit(X_train, y_train)\n",
    "    return dict(\n",
    "        mse_train=metrics.mean_squared_error(y_train, mod.predict(X_train)),\n",
    "        mse_test=metrics.mean_squared_error(y_test, mod.predict(X_test))\n",
    "    )\n",
    "\n",
    "# Dropping everything but the regressors and the outcome variable\n",
    "# Net Worth and Assets have NA/missinng values for 45% of entries so we drop them\n",
    "\n",
    "judges_ideology = judges.drop([ \"Name\",\"ID\",\"Year of Death\",\n",
    "                               \"Net Worth\",\"Assets\",\"Congress\"],1)\n",
    "\n",
    "# Removing rows with NAs\n",
    "judges_ideology = judges_ideology.dropna()\n",
    "\n",
    "# Continuous variables or variables that are already indicators\n",
    "continuous_variables = ['House Democrats', 'House Republicans', 'Senate Democrats', 'Senate Republicans',\n",
    "                        'House Independents', 'Senate Independents', 'Previous Position - ssc', 'Previous Position - slc',\n",
    "                        'Previous Position - locct', 'Previous Position - sjdget', 'Previous Position - ausa',\n",
    "                        'Previous Position - usa', 'Previous Position - sgo', 'Previous Position - sg', \n",
    "                        'Previous Position - ago', 'Previous Position - ag', 'Previous Position - cc', \n",
    "                        'Previous Position - sp', 'Previous Position - mag', 'Previous Position - bank',\n",
    "                        'Previous Position - terr', 'Previous Position - cab', 'Previous Position - asatty',\n",
    "                        'Previous Position - satty', 'Previous Position - cabdept', 'Previous Position - scab',\n",
    "                        'Previous Position - scabdpt', 'Previous Position - aag', 'Previous Position - indreg1',\n",
    "                        'Previous Position - reg1', 'Previous Position - reg2', 'Previous Position - reg3', \n",
    "                        'Previous Position - house', 'Previous Position - senate', 'Previous Position - gov',\n",
    "                        'Previous Position - ssenate', 'Previous Position - shouse', 'Previous Position - mayor',\n",
    "                        'Previous Position - ccoun', 'Previous Position - ccom', 'Previous Position - ada',\n",
    "                        'Previous Position - da', 'Previous Position - lother', 'Previous Position - lotherl',\n",
    "                        'Previous Position - lawprof', 'Previous Position - private']\n",
    "\n",
    "# Categorical variables \n",
    "categories = ['Year of Appointment', 'Year of Birth', 'Year of Departure', 'Cross Appointment', 'Recess Appointment',\n",
    "              'Unity', 'Circuit', 'Appointing President', 'Appointing President Party', 'President when Departed',\n",
    "              'Reason for Departing', 'Judge Party', 'District', 'State', 'City', 'Gender', 'Race', 'ABA Rating']\n",
    "\n",
    "# Creating test and training data for the two outcomes: ideology and absolute ideology\n",
    "X_train, X_test, ideo_train, ideo_test = prep_data(\n",
    "    judges_ideology,continuous_variables , categories, \"Ideology Score\"\n",
    ")\n",
    "\n",
    "X_train, X_test, abs_ideo_train, abs_ideo_test = prep_data(\n",
    "    judges_ideology,continuous_variables , categories, \"Absolute Ideology\"\n",
    ")\n",
    "\n",
    "# After some experimentation, we've selected some model parameters for lasso, random forest, and a neural network model\n",
    "alphas = np.exp(np.linspace(-2., -12., 25))\n",
    "\n",
    "lr_model = linear_model.LinearRegression()\n",
    "lasso_model = linear_model.LassoCV(cv=6, alphas = alphas,  max_iter=500)\n",
    "forest_model = RandomForestRegressor(n_estimators = 100)\n",
    "nn_scaled_model = pipeline.make_pipeline(\n",
    "    preprocessing.StandardScaler(),  # this will do the input scaling\n",
    "    neural_network.MLPRegressor((150,),activation = \"logistic\",\n",
    "                                solver=\"adam\",alpha=0.005)) # we tried a few alphas chose this one \n",
    "                                                            # based on minimizing mse_test ... but it was somewhat arbitrary\n",
    "\n",
    "models = { \"OLS\": lr_model, \"Lasso\": lasso_model,\n",
    "          \"Random Forest\": forest_model, \"Neural Network\": nn_scaled_model}\n",
    "\n",
    "# Creating an empty dataframe (with hierarchical columns) for the test and training MSE of each model \n",
    "MSE_by_model = pd.DataFrame(index = models.keys(),\n",
    "                            columns= pd.MultiIndex.from_arrays(([\"Ideology\",\"Ideology\",\"Absolute Ideology\",\"Absolute Ideology\"],\n",
    "                                                                     [\"Train MSE\",\"Test MSE\",\"Train MSE\",\"Test MSE\"])))\n",
    "# This is what that dataframe looks like\n",
    "MSE_by_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:562: ConvergenceWarning:\n",
      "\n",
      "Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:562: ConvergenceWarning:\n",
      "\n",
      "Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ideology</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Absolute Ideology</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Train MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Train MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OLS</th>\n",
       "      <td>93.451018</td>\n",
       "      <td>3.731771e+18</td>\n",
       "      <td>96.521900</td>\n",
       "      <td>1.515461e+19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>144.451759</td>\n",
       "      <td>2.191193e+02</td>\n",
       "      <td>158.052784</td>\n",
       "      <td>1.803260e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>17.029786</td>\n",
       "      <td>1.309845e+02</td>\n",
       "      <td>12.133112</td>\n",
       "      <td>8.674473e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural Network</th>\n",
       "      <td>37.042254</td>\n",
       "      <td>2.688825e+02</td>\n",
       "      <td>48.897053</td>\n",
       "      <td>2.258672e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Ideology               Absolute Ideology              \n",
       "                 Train MSE      Test MSE         Train MSE      Test MSE\n",
       "OLS              93.451018  3.731771e+18         96.521900  1.515461e+19\n",
       "Lasso           144.451759  2.191193e+02        158.052784  1.803260e+02\n",
       "Random Forest    17.029786  1.309845e+02         12.133112  8.674473e+01\n",
       "Neural Network   37.042254  2.688825e+02         48.897053  2.258672e+02"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Takes a few minutes to run\n",
    "ideo_mse_list = [fit_and_report_mses(model,X_train,X_test, ideo_train,ideo_test) for model in models.values()]\n",
    "abs_ideo_mse_list = [fit_and_report_mses(model,X_train,X_test, abs_ideo_train,abs_ideo_test) for model in models.values()]\n",
    "MSE_by_model[(\"Ideology\",\"Train MSE\")] = [result[\"mse_train\"] for result in ideo_mse_list]\n",
    "MSE_by_model[(\"Ideology\",\"Test MSE\")] = [result[\"mse_test\"] for result in ideo_mse_list]\n",
    "MSE_by_model[(\"Absolute Ideology\",\"Train MSE\")] = [result[\"mse_train\"] for result in abs_ideo_mse_list]\n",
    "MSE_by_model[(\"Absolute Ideology\",\"Test MSE\")] = [result[\"mse_test\"] for result in abs_ideo_mse_list]\n",
    "MSE_by_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our parameters (and computing time limitations), the Random Forest clearly does the best job predicting ideology and absolute ideology.\n",
    "\n",
    "We'll now use the Random Forest to see how much of the growth in absolute ideology over time is explained by the judge attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How much do changing attributes explain rising absolute ideology?\n",
    "\n",
    "We will generate fitted values and residuals for the absolute ideology using the Random Forest (because it performed the best above) and the entire sample (not just training data). Then we'll plot the average change in residuls and fitted values in a graph similar to one above that we used to motivate analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the fulldataset is equivalent to setting test_size = 0 \n",
    "X_full, X_empty_test, abs_ideo_full, abs_ideo_empty_train = prep_data(\n",
    "    judges_ideology,continuous_variables , categories, \"Absolute Ideology\",test_size=0\n",
    ")\n",
    "\n",
    "# Fitted values\n",
    "abs_ideo_hat = forest_model.fit(X_full,abs_ideo_full).predict(X_full)\n",
    "\n",
    "# Residuals\n",
    "abs_ideo_resid = abs_ideo_full - abs_ideo_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1584x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating dataframe to ultimately generate mean observed, predicted, and residual values by year \n",
    "judges_decomposition = judges_ideology[[\"Year of Appointment\",\"State\",\"Absolute Ideology\"]].copy()\n",
    "judges_decomposition[\"Residuals\"] = abs_ideo_resid\n",
    "judges_decomposition[\"Fitted\"] = abs_ideo_hat\n",
    "year_decomposition =  judges_decomposition.groupby(\"Year of Appointment\")[[\"Absolute Ideology\",\"Residuals\",\"Fitted\"]].mean()\n",
    "year_decomposition.reset_index(inplace = True)\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "fig, ax = plt.subplots(1,2,figsize=(22,8.5))\n",
    "\n",
    "colors = [\"#4135ed\" ,\"#5cb6cf\"]\n",
    "    \n",
    "for counter,value in enumerate([\"Absolute Ideology\",\"Fitted\"]):\n",
    "    ax[0].plot(year_decomposition[\"Year of Appointment\"],\n",
    "                    year_decomposition[value],\"-o\", color = colors[counter],label=value)\n",
    "    ax[1].scatter(year_decomposition[\"Year of Appointment\"],\n",
    "                    year_decomposition[value],  color = colors[counter],label=value)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(year_decomposition[\"Year of Appointment\"],year_decomposition[value])\n",
    "    line = slope*year_decomposition[\"Year of Appointment\"]+intercept\n",
    "    ax[1].plot(year_decomposition[\"Year of Appointment\"], line,color=colors[counter],label=\"_\")\n",
    "      \n",
    "ax[0].set_title(\"Are Judge Attributes Driving Rising Absolute Ideology?\")   \n",
    "ax[1].set_title(\"...They Do Not Appear to Explain Increasing Absolute Ideology\")   \n",
    "\n",
    "for n in [0,1]:\n",
    "    ax[n].set_facecolor('white')\n",
    "    ax[n].grid(False)\n",
    "    ax[n].set_ylabel(\"Yearly Mean\")\n",
    "    ax[n].set_xlabel(\"Year of Judge Appointment\")\n",
    "    ax[n].legend()\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fitted values do not show an increase in predicted absolute ideology over time. But part of the reason is structural: we are controlling for year of appointment and other variables that vary over time such as appointed president. Next, we'll look at whether the judge's static _personal_ attributes (excluding age) predict the rise in absolute over time. We'll use the following variables: race, gender, previous job experience, judge's political party, and American Bar Association Rating (level of qualification).\n",
    "\n",
    "First, we'll look at how judge personal attributes have changed over time. \n",
    "\n",
    "## Attributes Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age When Appointed</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Judge Party</th>\n",
       "      <th>Politician</th>\n",
       "      <th>ABA Rating</th>\n",
       "      <th>Year of Appointment</th>\n",
       "      <th>White</th>\n",
       "      <th>African American</th>\n",
       "      <th>Female</th>\n",
       "      <th>Republican</th>\n",
       "      <th>Well Qualified</th>\n",
       "      <th>Young</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Republican</td>\n",
       "      <td>0</td>\n",
       "      <td>Qualified</td>\n",
       "      <td>1982</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>1</td>\n",
       "      <td>Well-Qual.</td>\n",
       "      <td>1979</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Republican</td>\n",
       "      <td>0</td>\n",
       "      <td>Well-Qual.</td>\n",
       "      <td>1976</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>Republican</td>\n",
       "      <td>0</td>\n",
       "      <td>Qualified</td>\n",
       "      <td>1982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>48.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>0</td>\n",
       "      <td>Qualified</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age When Appointed              Race Gender Judge Party  Politician  \\\n",
       "2                 55.0             White   Male  Republican           0   \n",
       "3                 51.0             White   Male    Democrat           1   \n",
       "4                 50.0             White   Male  Republican           0   \n",
       "5                 57.0          Hispanic   Male  Republican           0   \n",
       "10                48.0  African American   Male    Democrat           0   \n",
       "\n",
       "    ABA Rating  Year of Appointment  White  African American  Female  \\\n",
       "2    Qualified                 1982      1                 0       0   \n",
       "3   Well-Qual.                 1979      1                 0       0   \n",
       "4   Well-Qual.                 1976      1                 0       0   \n",
       "5    Qualified                 1982      0                 0       0   \n",
       "10   Qualified                 1993      0                 1       0   \n",
       "\n",
       "    Republican  Well Qualified  Young  \n",
       "2            1               0      0  \n",
       "3            0               1      0  \n",
       "4            1               1      1  \n",
       "5            1               0      0  \n",
       "10           0               0      1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset of full dataframe with only key judge personal attributes, as well as year of appointment\n",
    "judges_personal = judges_ideology[[\"Age When Appointed\",\"Race\",\"Gender\",\"Judge Party\",\"Politician\",\"ABA Rating\",\"Year of Appointment\"]]\n",
    "\n",
    "# Manual \"one-hot encoding\" or in other words, creating columns which serve as indicator variables for key attributes\n",
    "for race_name in [\"White\",\"African American\"]:\n",
    "    judges_personal[race_name] = [1 if race == race_name else 0 for race in judges_personal[\"Race\"]]\n",
    "\n",
    "judges_personal[\"Female\"] = [1 if gender == \"Female\" else 0 for gender in judges_personal[\"Gender\"]]\n",
    "judges_personal[\"Republican\"] = [1 if party == \"Republican\" else 0 for party in judges_personal[\"Judge Party\"]]\n",
    "judges_personal[\"Well Qualified\"] = [1 if rating == \"Well-Qual.\" else 0 for rating in judges_personal[\"ABA Rating\"]]\n",
    "\n",
    "mean_age = np.mean(judges[\"Age When Appointed\"])\n",
    "judges_personal[\"Young\"] = [1 if age < mean_age else 0 for age in judges_personal[\"Age When Appointed\"]]\n",
    "\n",
    "judges_personal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>White</th>\n",
       "      <th>African American</th>\n",
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       "      <th>Republican</th>\n",
       "      <th>Well Qualified</th>\n",
       "      <th>Young</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year of Appointment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1957</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>46.153846</td>\n",
       "      <td>30.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
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       "      <td>36.363636</td>\n",
       "      <td>27.272727</td>\n",
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       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>100.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>45.454545</td>\n",
       "      <td>54.545455</td>\n",
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       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>95.081967</td>\n",
       "      <td>3.278689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.540984</td>\n",
       "      <td>47.540984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.225806</td>\n",
       "      <td>29.032258</td>\n",
       "      <td>41.935484</td>\n",
       "      <td>29.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>11.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>92.857143</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>64.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>94.736842</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>52.631579</td>\n",
       "      <td>47.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>92.857143</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>2.380952</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>45.238095</td>\n",
       "      <td>47.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>86.666667</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>43.333333</td>\n",
       "      <td>46.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.882353</td>\n",
       "      <td>5.882353</td>\n",
       "      <td>35.294118</td>\n",
       "      <td>41.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>78.571429</td>\n",
       "      <td>21.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>98.039216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960784</td>\n",
       "      <td>98.039216</td>\n",
       "      <td>43.137255</td>\n",
       "      <td>60.784314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>96.428571</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.714286</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>57.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          White  African American    Female  Republican  \\\n",
       "Year of Appointment                                                       \n",
       "1957                 100.000000          0.000000  0.000000  100.000000   \n",
       "1958                 100.000000          0.000000  0.000000   90.909091   \n",
       "1959                 100.000000          0.000000  0.000000   83.333333   \n",
       "1960                 100.000000          0.000000  0.000000  100.000000   \n",
       "1961                  95.081967          3.278689  0.000000    0.000000   \n",
       "1962                 100.000000          0.000000  3.225806   29.032258   \n",
       "1963                 100.000000          0.000000  0.000000    0.000000   \n",
       "1964                  92.857143          7.142857  0.000000    0.000000   \n",
       "1965                  94.736842          5.263158  0.000000    5.263158   \n",
       "1966                  92.857143          4.761905  2.380952    7.142857   \n",
       "1967                  86.666667          6.666667  0.000000    6.666667   \n",
       "1968                 100.000000          0.000000  5.882353    5.882353   \n",
       "1969                  78.571429         21.428571  0.000000  100.000000   \n",
       "1970                  98.039216          0.000000  1.960784   98.039216   \n",
       "1971                  96.428571          3.571429  0.000000   85.714286   \n",
       "\n",
       "                     Well Qualified      Young  \n",
       "Year of Appointment                             \n",
       "1957                      46.153846  30.769231  \n",
       "1958                      36.363636  27.272727  \n",
       "1959                      50.000000  33.333333  \n",
       "1960                      45.454545  54.545455  \n",
       "1961                      47.540984  47.540984  \n",
       "1962                      41.935484  29.032258  \n",
       "1963                      66.666667  11.111111  \n",
       "1964                      14.285714  64.285714  \n",
       "1965                      52.631579  47.368421  \n",
       "1966                      45.238095  47.619048  \n",
       "1967                      43.333333  46.666667  \n",
       "1968                      35.294118  41.176471  \n",
       "1969                      28.571429  50.000000  \n",
       "1970                      43.137255  60.784314  \n",
       "1971                      50.000000  57.142857  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe with the average fraction of judges with each personal attribute by year\n",
    "year_personal = judges_personal.groupby(\"Year of Appointment\")[[\"White\",\"African American\",\n",
    "                                                               \"Female\",\"Republican\",\"Well Qualified\",\"Young\"]].mean()\n",
    "\n",
    "# Multiplying by 100 to provide percentage term interpretation \n",
    "for name in list(year_personal):\n",
    "    year_personal[name] = 100*year_personal[name]\n",
    "    \n",
    "year_personal.head(n=15)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows considerable variation over time in the proportion of judges with each of the personal attributes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x4284 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(7,1,figsize=(11,8.5*7))\n",
    "\n",
    "for counter,attribute in enumerate(list(year_personal)):\n",
    "    ax[counter].set_facecolor('white')\n",
    "    ax[counter].grid(False)\n",
    "    ax[counter].set_ylabel(\"Percentage\")\n",
    "    ax[counter].set_xlabel(\"Year of Appointment\")\n",
    "    ax[counter].set_yticks(range(0,100,10))\n",
    "    ax[counter].plot(list(year_personal.index),year_personal[attribute],\"-o\",label=attribute)\n",
    "    ax[counter].set_title(attribute)\n",
    "    \n",
    "\n",
    "# All the attributes in one plot (a bit overwhelming, but makes for comparison)\n",
    "for attribute in list(year_personal):\n",
    "    ax[6].set_facecolor('white')\n",
    "    ax[6].grid(False)\n",
    "    ax[6].set_ylabel(\"Percentage\")\n",
    "    ax[6].set_xlabel(\"Year of Appointment\")\n",
    "    ax[6].set_yticks(range(0,100,10))\n",
    "    ax[6].plot(list(year_personal.index),year_personal[attribute],\"-o\",label=attribute)\n",
    "    ax[6].set_title(\"All Attributes\")\n",
    "    ax[6].legend()\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the plot above shows, there variation in judge attributes over time. But do the changing attributes predict changing ideology?\n",
    "\n",
    "Now we'll determine whether static personal attributes help explain rising judge absolute ideology.\n",
    "\n",
    "## Static Attributes and Ideology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1584x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We'll add \"P\" to the varialbe names to refer to \"personal\" and distinguish them from the variables above\n",
    "continuous_variablesP = ['Age When Appointed', 'Previous Position - slc', 'Previous Position - locct', \n",
    "                         'Previous Position - sjdget', 'Previous Position - ausa', 'Previous Position - usa',\n",
    "                         'Previous Position - sgo', 'Previous Position - sg', 'Previous Position - ago',\n",
    "                         'Previous Position - ag', 'Previous Position - cc', 'Previous Position - sp', \n",
    "                         'Previous Position - mag', 'Previous Position - bank', 'Previous Position - terr',\n",
    "                         'Previous Position - cab', 'Previous Position - asatty', 'Previous Position - satty',\n",
    "                         'Previous Position - cabdept', 'Previous Position - scab', 'Previous Position - scabdpt',\n",
    "                         'Previous Position - aag', 'Previous Position - indreg1', 'Previous Position - reg1',\n",
    "                         'Previous Position - reg2', 'Previous Position - reg3', 'Previous Position - house',\n",
    "                         'Previous Position - senate', 'Previous Position - gov', 'Previous Position - ssenate',\n",
    "                         'Previous Position - shouse', 'Previous Position - mayor', 'Previous Position - ccoun',\n",
    "                         'Previous Position - ccom', 'Previous Position - ada', 'Previous Position - da',\n",
    "                         'Previous Position - lother', 'Previous Position - lotherl', 'Previous Position - lawprof',\n",
    "                         'Previous Position - private']\n",
    "\n",
    "# Categorical variables \n",
    "categoriesP = ['Judge Party','Gender','Race','ABA Rating']\n",
    "\n",
    "# Using the fulldataset is equivalent to setting test_size = 0 \n",
    "X_fullP, X_empty_test, abs_ideo_fullP, abs_ideo_empty_train = prep_data(\n",
    "    judges_ideology,continuous_variablesP , categoriesP, \"Absolute Ideology\",test_size=0\n",
    ")\n",
    "\n",
    "# Fitted values\n",
    "abs_ideo_hatP = forest_model.fit(X_fullP,abs_ideo_fullP).predict(X_fullP)\n",
    "\n",
    "# Residuals\n",
    "abs_ideo_residP = abs_ideo_fullP - abs_ideo_hatP\n",
    "\n",
    "# Similar to above, creating dataframe with mean observed, residual and fitted values by year\n",
    "judges_decompositionP = judges_ideology[[\"Year of Appointment\",\"State\",\"Absolute Ideology\"]].copy()\n",
    "judges_decompositionP[\"Residuals\"] = abs_ideo_residP\n",
    "judges_decompositionP[\"Fitted\"] = abs_ideo_hatP\n",
    "year_decompositionP = judges_decompositionP.groupby(\"Year of Appointment\")[[\"Absolute Ideology\",\"Residuals\",\"Fitted\"]].mean()\n",
    "year_decompositionP.reset_index(inplace = True)\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "fig, ax = plt.subplots(1,2,figsize=(22,8.5))\n",
    "colors = [\"#4135ed\" ,\"#5cb6cf\"]\n",
    "\n",
    "# Plotting trends in observed and fitted values\n",
    "for counter,value in enumerate([\"Absolute Ideology\",\"Fitted\"]):\n",
    "    ax[0].plot(year_decompositionP[\"Year of Appointment\"],\n",
    "                    year_decompositionP[value],\"-o\", color = colors[counter],label=value)\n",
    "    ax[1].scatter(year_decompositionP[\"Year of Appointment\"],\n",
    "                    year_decompositionP[value],  color = colors[counter],label=value)\n",
    "    slope, intercept, r_value, p_value, std_err = stats.linregress(year_decompositionP[\"Year of Appointment\"],year_decompositionP[value])\n",
    "    line = slope*year_decompositionP[\"Year of Appointment\"]+intercept\n",
    "    ax[1].plot(year_decompositionP[\"Year of Appointment\"], line,color=colors[counter],label=\"_\")\n",
    "      \n",
    "ax[0].set_title(\"Are Judge Personal Attributes Driving Rising Absolute Ideology?\")   \n",
    "ax[1].set_title(\"...Personal Attributes Do not Predict Rising Absolute Ideology\")\n",
    "for n in [0,1]:\n",
    "    ax[n].set_facecolor('white')\n",
    "    ax[n].grid(False)\n",
    "    ax[n].set_ylabel(\"Yearly Mean\")\n",
    "    ax[n].set_xlabel(\"Year of Judge Appointment\")\n",
    "    ax[n].legend()\n",
    "    #ax[n].set_yticks(range(0,40,5))\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The meaning behind this chart is that the shifting of judges' static personal attributes over time does not explain the rise in absolute ideology. It is interest how similar this chart is to the the chart above, which included dynamic attributes of judges (including year of appointment, appointing president, etc.). The chart may beg the question: how much does adding dynamic attributes really improve the predictive power of the random forest model? Comparing the the MSE's of the random forest model with and without dynamic attributes (see below), it is clear the dynamic attributes do add predictive power. That said, the charts, taken together, suggest there are unobserved forces behind the rising absolute ideology of US judges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE with all Attributes: 10.762745067579159\n",
      "MSE with only Static Personal Attributes: 53.242898266036356\n"
     ]
    }
   ],
   "source": [
    "# All judge attributes\n",
    "full_mse = metrics.mean_squared_error(abs_ideo_full,\n",
    "                                      forest_model.fit(X_full,abs_ideo_full).predict(X_full))\n",
    "\n",
    "\n",
    "# Only including static personal attributes\n",
    "full_mseP = metrics.mean_squared_error(abs_ideo_fullP,\n",
    "                                      forest_model.fit(X_fullP,abs_ideo_fullP).predict(X_fullP))\n",
    "\n",
    "print(f\"MSE with all Attributes: {full_mse}\")\n",
    "print(f\"MSE with only Static Personal Attributes: {full_mseP}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4 - Estimating Nuisance Function\n",
    "\n",
    "We'll estimate the influence of judge political experience on judge ideology\n",
    "\n",
    "Are judges with past experience as elected officials more ideological?\n",
    "\n",
    "We'll estimate regressions of the form \n",
    "\n",
    "$$Y = \\beta Politician + f(x) + \\epsilon $$\n",
    "\n",
    "where $Y$ is ideology or absolute ideology, $Politician$ is an indicator for past politican experience, and $f(x)$ is the nuisance function for the controls (judge attributes) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ideology Score</th>\n",
       "      <th>Absolute Ideology</th>\n",
       "      <th>Politician</th>\n",
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       "      <th>Previous Position - senate</th>\n",
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       "      <th>Senate Democrats</th>\n",
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       "      <th>House Independents</th>\n",
       "      <th>Senate Independents</th>\n",
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      "text/plain": [
       "   Ideology Score  Absolute Ideology  Politician  Previous Position - house  \\\n",
       "0       40.700001          40.700001           0                          0   \n",
       "1      -30.599999          30.599999           1                          1   \n",
       "2        6.100000           6.100000           0                          0   \n",
       "3       53.799999          53.799999           0                          0   \n",
       "4      -22.600000          22.600000           0                          0   \n",
       "\n",
       "   Previous Position - senate  Previous Position - gov  \\\n",
       "0                           0                        0   \n",
       "1                           0                        0   \n",
       "2                           0                        0   \n",
       "3                           0                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   Previous Position - ssenate  Previous Position - shouse  \\\n",
       "0                            0                           0   \n",
       "1                            0                           0   \n",
       "2                            0                           0   \n",
       "3                            0                           0   \n",
       "4                            0                           0   \n",
       "\n",
       "   Previous Position - mayor  Previous Position - ccoun  House Democrats  \\\n",
       "0                          0                          0              243   \n",
       "1                          0                          0              277   \n",
       "2                          0                          0              291   \n",
       "3                          0                          0              243   \n",
       "4                          0                          0              258   \n",
       "\n",
       "   House Republicans  Senate Democrats  Senate Republicans  \\\n",
       "0                192                47                  53   \n",
       "1                158                59                  41   \n",
       "2                144                61                  38   \n",
       "3                192                47                  53   \n",
       "4                176                57                  43   \n",
       "\n",
       "   House Independents  Senate Independents  \n",
       "0                   0                    0  \n",
       "1                   0                    0  \n",
       "2                   0                    0  \n",
       "3                   0                    0  \n",
       "4                   1                    0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe which is ready for regressions/partial regressions\n",
    "\n",
    "# Setting continuous variables for this analysis (the categories are the same as the ones above)\n",
    "\n",
    "continuous_variables2 = [\"Ideology Score\",\"Absolute Ideology\",\"Politician\",\n",
    "                          'Previous Position - house', 'Previous Position - senate',\n",
    "                         'Previous Position - gov','Previous Position - ssenate',\n",
    "                         'Previous Position - shouse','Previous Position - mayor',\n",
    "                         'Previous Position - ccoun','House Democrats','House Republicans',\n",
    "                        'Senate Democrats', 'Senate Republicans','House Independents','Senate Independents']\n",
    "\n",
    "political_judges = judges_ideology[continuous_variables2]\n",
    "political_judges.reset_index(inplace = True)\n",
    "political_judges = political_judges.drop([\"index\"],axis=1)\n",
    "political_judges.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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