Compas Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compas Analysis\n",
    "\n",
    "What follows are the calculations performed for ProPublica's analaysis of the COMPAS Recidivism Risk Scores. It might be helpful to open [the methodology](https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm/) in another tab to understand the following.\n",
    "\n",
    "## Loading the Data\n",
    "\n",
    "We select fields for severity of charge, number of priors, demographics, age, sex, compas scores, and whether each person was accused of a crime within two years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# filter dplyr warnings\n",
    "%load_ext rpy2.ipython\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 7214\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "raw_data <- read.csv(\"./compas-scores-two-years.csv\")\n",
    "nrow(raw_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However not all of the rows are useable for the first round of analysis.\n",
    "\n",
    "There are a number of reasons remove rows because of missing data:\n",
    "* If the charge date of a defendants Compas scored crime was not within 30 days from when the person was arrested, we assume that because of data quality reasons, that we do not have the right offense.\n",
    "* We coded the recidivist flag -- `is_recid` -- to be -1 if we could not find a compas case at all.\n",
    "* In a similar vein, ordinary traffic offenses -- those with a `c_charge_degree` of 'O' -- will not result in Jail time are removed (only two of them).\n",
    "* We filtered the underlying data from Broward county to include only those rows representing people who had either recidivated in two years, or had at least two years outside of a correctional facility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 6172\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "df <- dplyr::select(raw_data, age, c_charge_degree, race, age_cat, score_text, sex, priors_count, \n",
    "                    days_b_screening_arrest, decile_score, is_recid, two_year_recid, c_jail_in, c_jail_out) %>% \n",
    "        filter(days_b_screening_arrest <= 30) %>%\n",
    "        filter(days_b_screening_arrest >= -30) %>%\n",
    "        filter(is_recid != -1) %>%\n",
    "        filter(c_charge_degree != \"O\") %>%\n",
    "        filter(score_text != 'N/A')\n",
    "nrow(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Higher COMPAS scores are slightly correlated with a longer length of stay. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 0.2073297\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "df$length_of_stay <- as.numeric(as.Date(df$c_jail_out) - as.Date(df$c_jail_in))\n",
    "cor(df$length_of_stay, df$decile_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After filtering we have the following demographic breakdown:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        25 - 45 Greater than 45    Less than 25 \n",
       "           3532            1293            1347 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(df$age_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "African-American            Asian        Caucasian         Hispanic \n",
       "            3175               31             2103              509 \n",
       " Native American            Other \n",
       "              11              343 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(df$race)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black defendants: 51.44%\n",
      "White defendants: 34.07%\n",
      "Hispanic defendants: 8.25%\n",
      "Asian defendants: 0.50%\n",
      "Native American defendants: 0.18%\n"
     ]
    }
   ],
   "source": [
    "print(\"Black defendants: %.2f%%\" %            (3175 / 6172 * 100))\n",
    "print(\"White defendants: %.2f%%\" %            (2103 / 6172 * 100))\n",
    "print(\"Hispanic defendants: %.2f%%\" %         (509  / 6172 * 100))\n",
    "print(\"Asian defendants: %.2f%%\" %            (31   / 6172 * 100))\n",
    "print(\"Native American defendants: %.2f%%\" %  (11   / 6172 * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  High    Low Medium \n",
       "  1144   3421   1607 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(df$score_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        race\n",
       "sex      African-American Asian Caucasian Hispanic Native American Other\n",
       "  Female              549     2       482       82               2    58\n",
       "  Male               2626    29      1621      427               9   285\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "xtabs(~ sex + race, data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female   Male \n",
       "  1175   4997 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(df$sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Men: 80.96%\n",
      "Women: 19.04%\n"
     ]
    }
   ],
   "source": [
    "print(\"Men: %.2f%%\" %   (4997 / 6172 * 100))\n",
    "print(\"Women: %.2f%%\" % (1175 / 6172 * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 2809\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "nrow(filter(df, two_year_recid == 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 45.51199\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "nrow(filter(df, two_year_recid == 1)) / nrow(df) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Judges are often presented with two sets of scores from the Compas system -- one that classifies people into High, Medium and Low risk, and a corresponding decile score. There is a clear downward trend in the decile scores as those scores increase for white defendants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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kDlbvRxys3o44WJ1d4ba4q+NgZq8hnDVrlp18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\n11AxBBBAAAEEEEAAAQQQQKC+Av8Hhdy441RW7tIAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 900 -h 363 -u px\n",
    "library(grid)\n",
    "library(gridExtra)\n",
    "pblack <- ggplot(data=filter(df, race ==\"African-American\"), aes(ordered(decile_score))) + \n",
    "          geom_bar() + xlab(\"Decile Score\") +\n",
    "          ylim(0, 650) + ggtitle(\"Black Defendant's Decile Scores\")\n",
    "pwhite <- ggplot(data=filter(df, race ==\"Caucasian\"), aes(ordered(decile_score))) + \n",
    "          geom_bar() + xlab(\"Decile Score\") +\n",
    "          ylim(0, 650) + ggtitle(\"White Defendant's Decile Scores\")\n",
    "grid.arrange(pblack, pwhite,  ncol = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            race\n",
       "decile_score African-American Asian Caucasian Hispanic Native American Other\n",
       "          1               365    15       605      159               0   142\n",
       "          2               346     4       321       89               2    60\n",
       "          3               298     5       238       73               1    32\n",
       "          4               337     0       243       47               0    39\n",
       "          5               323     1       200       39               0    19\n",
       "          6               318     2       160       27               2    20\n",
       "          7               343     1       113       28               2     9\n",
       "          8               301     2        96       14               0     7\n",
       "          9               317     0        77       17               2     7\n",
       "          10              227     1        50       16               2     8\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "xtabs(~ decile_score + race, data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Racial Bias in Compas\n",
    "\n",
    "After filtering out bad rows, our first question is whether there is a significant difference in Compas scores between races. To do so we need to change some variables into factors, and run a logistic regression, comparing low scores to high scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = score_factor ~ gender_factor + age_factor + race_factor + \n",
       "    priors_count + crime_factor + two_year_recid, family = \"binomial\", \n",
       "    data = df)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.9966  -0.7919  -0.3303   0.8121   2.6024  \n",
       "\n",
       "Coefficients:\n",
       "                            Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)                 -1.52554    0.07851 -19.430  < 2e-16 ***\n",
       "gender_factorFemale          0.22127    0.07951   2.783 0.005388 ** \n",
       "age_factorGreater than 45   -1.35563    0.09908 -13.682  < 2e-16 ***\n",
       "age_factorLess than 25       1.30839    0.07593  17.232  < 2e-16 ***\n",
       "race_factorAfrican-American  0.47721    0.06935   6.881 5.93e-12 ***\n",
       "race_factorAsian            -0.25441    0.47821  -0.532 0.594717    \n",
       "race_factorHispanic         -0.42839    0.12813  -3.344 0.000827 ***\n",
       "race_factorNative American   1.39421    0.76612   1.820 0.068784 .  \n",
       "race_factorOther            -0.82635    0.16208  -5.098 3.43e-07 ***\n",
       "priors_count                 0.26895    0.01110  24.221  < 2e-16 ***\n",
       "crime_factorM               -0.31124    0.06655  -4.677 2.91e-06 ***\n",
       "two_year_recid               0.68586    0.06402  10.713  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 8483.3  on 6171  degrees of freedom\n",
       "Residual deviance: 6168.4  on 6160  degrees of freedom\n",
       "AIC: 6192.4\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "df <- mutate(df, crime_factor = factor(c_charge_degree)) %>%\n",
    "      mutate(age_factor = as.factor(age_cat)) %>%\n",
    "      within(age_factor <- relevel(age_factor, ref = 1)) %>%\n",
    "      mutate(race_factor = factor(race)) %>%\n",
    "      within(race_factor <- relevel(race_factor, ref = 3)) %>%\n",
    "      mutate(gender_factor = factor(sex, labels= c(\"Female\",\"Male\"))) %>%\n",
    "      within(gender_factor <- relevel(gender_factor, ref = 2)) %>%\n",
    "      mutate(score_factor = factor(score_text != \"Low\", labels = c(\"LowScore\",\"HighScore\")))\n",
    "model <- glm(score_factor ~ gender_factor + age_factor + race_factor +\n",
    "                            priors_count + crime_factor + two_year_recid, family=\"binomial\", data=df)\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Black defendants are 45% more likely than white defendants to receive a higher score correcting for the seriousness of their crime, previous arrests, and future criminal behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 1.452841\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "control <- exp(-1.52554) / (1 + exp(-1.52554))\n",
    "exp(0.47721) / (1 - control + (control * exp(0.47721)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Women are 19.4% more likely than men to get a higher score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 1.194798\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "exp(0.22127) / (1 - control + (control * exp(0.22127)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most surprisingly, people under 25 are 2.5 times as likely to get a higher score as middle aged defendants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 2.49612\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "exp(1.30839) / (1 - control + (control * exp(1.30839)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk of Violent Recidivism\n",
    "\n",
    "Compas also offers a score that aims to measure a persons risk of violent recidivism, which has a similar overall accuracy to the Recidivism score. As before, we can use a logistic regression to test for racial bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 4743\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "raw_data <- read.csv(\"./compas-scores-two-years-violent.csv\")\n",
    "nrow(raw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 4020\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "df <- dplyr::select(raw_data, age, c_charge_degree, race, age_cat, v_score_text, sex, priors_count, \n",
    "                    days_b_screening_arrest, v_decile_score, is_recid, two_year_recid) %>% \n",
    "        filter(days_b_screening_arrest <= 30) %>%\n",
    "        filter(days_b_screening_arrest >= -30) %>% \n",
    "        filter(is_recid != -1) %>%\n",
    "        filter(c_charge_degree != \"O\") %>%\n",
    "        filter(v_score_text != 'N/A')\n",
    "nrow(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        25 - 45 Greater than 45    Less than 25 \n",
       "           2300             954             766 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(df$age_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "African-American            Asian        Caucasian         Hispanic \n",
       "            1918               26             1459              355 \n",
       " Native American            Other \n",
       "               7              255 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(df$race)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  High    Low Medium \n",
       "   279   2913    828 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(df$v_score_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 16.21891\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "nrow(filter(df, two_year_recid == 1)) / nrow(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 652\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "nrow(filter(df, two_year_recid == 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CCMQmcMkll2S+8Y1vuPzU7wTnE7rnwU7M7CgV9ZPajipp5Ir6kWuvvdZtm84880y3HdFn\n0b5vyZIl7n1t53SUrtj2TvNGk4aM5h4h1Ofa9ur9IJBxkxfatmiUQxDkuj5C2zL1U4WmVUaqk7Zb\nwbn/rq/WbwONltB86kN0asJFF12U0W+C4Ee8O1oaDhkNdtZlRowYkQmCX9cXfvnLX87867/+qytf\nsXxzfdwMwT85BYF35r333nN95vHHH5/RX76k4aLqa8J+UNOof1H5wxT2Fyq7tvcqv54H5xZmgh2d\nmSDwdZOW0pdUu++L9vOllDc0iD621/dp2kK/7crt+4qt8+W0c7T8PE9GgCGjybh6nasCQg0P1Qb7\nX/7lX9y5Uxq2oQBRKRoQBnudMsGFVdz7GloTnNCc2W677dz5anozOKrUZqimhgLq/IawU9S5WQqi\nFLwUSoUCQg13UeCiTkHnOqqsCgz0F27Yww25yhTsFXSLKDatNoTK850g4AvT/vvvn1GwpWEm6hD/\n9Kc/hR+5QCE8h1Adj+ZXUn20YVReYSdYKF9NX2iIiYIVnfuogFHBbbGhPTofJLj4jwu+lac6bgU/\nYYp2FDofTp1/mNR2CnyDPXVt2lcdogJHBXuhrTp2WSspIIwO2/2v//qvTLCX2H1WqE7uw+BfoYBw\n7Nix7rw9TVesrfTDLLjIi3MJ83z88ccLBoSqo9pP51OGddGPKuURDcLDvPSo9VvTax1UwKz5L730\nUjdJseUX81VAqHUhuDiSy6e9cpWzDrgM+YcAArEKvPjii9mALhgx4naQaQHBSJJMcJTJLUs/voML\noLnn2iaGQaPe0E4ffee1Uy3s+8Lz56NDIott71zGkX+FAkJNolMktJ1qb9uiUw20/VNqb1rVSdv7\nMGnnmOqkban6CQVS4XY0GOXi+pMwINTpD+HOTu3YDEZauHMelVexfPV51EevlYILxbgdwOqXVQ4F\npeGy/zFF2/8K5s8991z3pvpp7bRWm4QpDAhVXu3kVbAcpuOOOy6j30RKYYDVnlU1+75oP19qecO6\nRh+L9X2artBvu3L6PuVTbJ0vt52VHyk5gabgC0+qQwENJQw6lGzNdWVPDcfU+7riaJg0RCU4OmRB\ngGAaCqNhi8FePTfEUq+DTsCCgCqc3OWhF0EQ597TFcB0NStd8WufffbJTlfKkyBoc8NYgwt/uOEi\nGpYSDuvR/Bq2qeE4uUnztTdt9Mpxuupl0OG4q2BqeI2GrYRJQ4nCYTMaUvvzn//cgoDRfRxelU7z\nhClfvuFn+R6DPa42evRoC44QueFJQcfkrgin4Sy5SUM6NexWQzqDPdqm4aIaVpsvySAIcrIfaViU\nrvgql2j9NJ2G8UbbUDNFr0YXrZPMw+GU2czLfKJlaoixUrG2CgJg23PPPdusjxqKWSgFPwDc8Kjg\n/Ng2V+FT+YMfNW74ZnRevafhXhrqpL9gM2sa1qV1Pbi4gbvqaKHlF/MNlxHsCHFP2ytXOetAmDeP\nCCAQn0C4LQ92hlpw1CJ7qkLw49cN49dVRdWHabsQpnDoqF5rmKRSdBileyPnX7HtXc6kBV9qO6X+\nVduX9rYt0UxKmTa6rVe/qKS+UUPn1X8E57G793S17Gg/on5YpwMEAagFI3dM/Y1+O4SpUL7h57mP\ncg4CH3elcp0CoGUFF4xzpyzkTqvXGuof7Hx2p6zoitE6vSDaPuE8wQ5d19cFO/7Ctyw4mumGtWbf\nCJ6UYhWdvtTntdL3aehvod925fR9cim2zgeBfFntXKoz01UmwDmElbmlbq7g6mmuU4ueM6dKBsP0\n7D//8z/dOXf6Af3II4+4c82CvXXuNhXqIKLnpj377LOmS3OHSdPrvD4FPMFet/Dtkh51yWh1DNp4\nK0AKhnm6jYs2MPpTYBbs3Vsvr1KmDTu26MwKUrQsdWphUqAcJp2PpuBWl7pWh/HLX/7SfaQOOkz5\n8g0/y/eoS5QruNN5ZLp9hH50aEx9oRQc9XPnrumHi8oWXH0u76Qy0C0zwqSgVfVSO0eTptOPGZ3v\nEbrq/JTgSmbZyXLrFK1vdqISnyjI07k2OqdRqVhbBXuOXVAWDbhvueUWV+98iwsvf67zFcK66PzI\nYG+uO1c0Oo/qsNtuu7nbnYTvq55an4Kjze781GLLL8U3/NHRXrnKXQfC8vKIAALxCOi7r21ScFTO\nbdt1jreSgi6dixwcWXKBRDTIyd0ullKSYtu7UubXNNpBqf5XZWxv2xLNs5RpC9VJ531H+0XlG/aN\n2qGoHcnamRkcHXXT6bz4aD9RKN9o+aLPdX6m8tNvCwWjCti1067QzkidY6jfF+q7tP1XgJgvKTCN\n9ouaRudi6nzJaCrFKjp9Kc9rqe/TLcjUZvl+25XT98ml2DpfbjuX4sw0lQsQEFZuV9Nz6keoghr9\nacOuIEQb1NwjMPrC6mIi2kOnH7g6WVsb6GBoo7tnmzb8wbkMpvx0UrP2Euo+d2HSXkblrXnPO++8\n8O31HjWvyqI9n9qo6yI0wbl07oicJg5uR+ECIZ0QrhRcPcwdPVKQmpvKmTY6rzp7/SjQPRh1FFR7\nE8Ojg5pOddRFBbQHVJ2dTu5XkkV7SYbKU3/RpJP4dTEcJdnrJPdi91BUgKy9r8E5K+4IYPQHSjRf\nHUnUjxsF+AqoFOCpM9XFYaJJ7ad66eI9+lxtp/tPyr69VKhO0fkUQKtdFejqXl/B8BF3gRhdlECp\nWFvpQjcqu46EylttH1ySPHu/zOhy9Fzl0UUEdOQ7GNLrfjTpgkGnnHJKds92OI86Ox1BVX4KULX+\nqcO+6qqr3DqofIotv1TfUspV7joQ1oFHBBCIT0DfeX3/DznkkDbbYG0jdd9YPVaSdK/Z4BQHtz0q\ntr3Ll7cCv7Cf1sVtdF9CXThM2y0FquVs88qZNrcs6nfUd+nom7bFupiaRggp6XeDjiIqQNUygiGb\n7uJnpfSLmj/qo9dKwakBrq9SfxSc5+226+oXCwWWGr2jPkU7adXnKJjMl3SBMvUNcpStglztiNUR\nxWhqz0qf5+vPo3nUct+n+/EW+m1XTt8nj2LrfLntHPXleQICwZebVGcCwcZSh7Syfxorrnv/6Zwp\npeg5hDoPQieMB3vW3L0Lg41BRmPudY6Akm6DoIvKBBtId083nYgc/Ihf7zyKIMhz91QKrlTm5ov+\n0/lb0fLoXAWdt6Fx+mFSnrq4iM4N0MnmO++8szuHL/w8eg5hsWnDcwiDTiyc1dVPt5dQCgJMd16e\nTqbXifvBFUezF5XRBXV07kawN9Fd0CbofDJBp+wubd1evkHQ5eZTPaMnv+v8yOAHiPPVuXyyDIZ1\nZsuW70lwxNZ5BXs223wcPbdA51zoIgi6j5XO0ZCPzr9TiravXusCQapHsFfUXWBGl1sPOkt95M4p\n0XmHYdKFdVRGpUJ1CqfVOYRhu+q57s2l9UbneISpWFtpGp0XqnJp/ZN9cITQzRqeE6IX4XkUeq71\nMfjx4s611MVytB4FRwv10XpJyw6CMXfuoC4QoT+tV1pXw1Ro+cV8g+DX1VvThKlYuSpZB8J8eUQA\ngXgEgitVu++tzs2OpiBgcO+H59LrM50XF73thC7OpW1dELyt1/dpOxwEMpngCtyubyzWj0WXq3MI\nw+2nHnUOuO5le9lll2W3z5q+2LYlCHiy5xC2N21unYIdZG75Or9cSedPahuu/kQXidOtL8JzCHUB\nl+BIkNsWa3usPkr9uLaB7eUb9XEL+v9/Orda/VJwlNb1AWqHYknXLpBTMCJpvcmi/YXuX6s6qF9R\nPx9cOTw7fal9STX7vmg/X2p5sxX8/yel9H2FftuV2/e118eX2865deF1fAINyir4EpEQKCqgoyc6\nb6DQESmdQ6CjgTp6lWQKNkbuKFY4pKPYssqZNjcf1Ud7AbXnMTdpeKeWHwQQuR+1+1pHXLVHNDfp\nCJWO0Ok8wTiTbnGhcwFK8aq0XoXqVE49irWVNlHawx49D6W9vHXUU3uT81nnm1fnCGn9VpvnpmLL\nL8dX+RYrV1LrQG59eI0AAp0roGGV6hvDYeTFtneVlqzYtiU3z3KmzZ1XR77y9Sfqv9Rv5vssN4/c\n17k+4efFtr3hNJU8Kl8dyVSf0l4/Xswq7X2fbAv9tiu37yu2zifVzpWsG/U8DwFhPbc+dUcAAQQQ\nQAABBBBAAIG6Fij/EEddc1F5BBBAAAEEEEAAAQQQQCA9AgSE6WlLaoIAAggggAACCCCAAAIIlCVA\nQFgWFxMjgAACCCCAAAIIIIAAAukRICBMT1tSEwQQQAABBBBAAAEEEECgLAECwrK4mBgBBBBAAAEE\nEEAAAQQQSI/A+tfU96xuujRwEkm3EwjuQeNuYppE/h3NU7dwUBl1ywAfU3DPPHdjVl162Lek2w3o\nctJJrTsdra9u+tq9e3d3+4yO5pXE/MH9F003FdalwH1Mwf2uLLhfpLuxsG/lC29dodtY+JjC23Do\ncuk+puDeZW6bp0uUJ5FaW1tLvhVJEsuvNM+ktmX0g5W2yD/mox+s3I9+sHI7zUk/WLkf/WD+fpAj\nhJWvU8yJAAIIIIAAAggggAACCNS0AAFhTTcfhUcAAQQQQAABBBBAAAEEKhcgIKzcjjkRQAABBBBA\nAAEEEEAAgZoWICCs6eaj8AgggAACCCCAAAIIIIBA5QIEhJXbMScCCCCAAAIIIIAAAgggUNMCBIQ1\n3XwUHgEEEEAAAQQQQAABBBCoXICAsHI75kQAAQQQQAABBBBAAAEEalqAgLCmm4/CI4AAAggggAAC\nCCCAAAKVCxAQVm7HnAgggAACCCCAAAIIIIBATQsQENZ081F4BBBAAAEEEEAAAQQQQKByAQLCyu2Y\nEwEEEEAAAQQQQAA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Vkldzc7Npg+5r+WSnH20+lk9uSj6WTeXq0qWL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\nAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 900 -h 363 -u px\n",
    "library(grid)\n",
    "library(gridExtra)\n",
    "pblack <- ggplot(data=filter(df, race ==\"African-American\"), aes(ordered(v_decile_score))) + \n",
    "          geom_bar() + xlab(\"Violent Decile Score\") +\n",
    "          ylim(0, 700) + ggtitle(\"Black Defendant's Violent Decile Scores\")\n",
    "pwhite <- ggplot(data=filter(df, race ==\"Caucasian\"), aes(ordered(v_decile_score))) + \n",
    "          geom_bar() + xlab(\"Violent Decile Score\") +\n",
    "          ylim(0, 700) + ggtitle(\"White Defendant's Violent Decile Scores\")\n",
    "grid.arrange(pblack, pwhite,  ncol = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = score_factor ~ gender_factor + age_factor + race_factor + \n",
       "    priors_count + crime_factor + two_year_recid, family = \"binomial\", \n",
       "    data = df)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.9304  -0.5667  -0.3161   0.4192   2.8386  \n",
       "\n",
       "Coefficients:\n",
       "                            Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)                 -2.24274    0.11326 -19.802  < 2e-16 ***\n",
       "gender_factorFemale         -0.72890    0.12666  -5.755 8.66e-09 ***\n",
       "age_factorGreater than 45   -1.74208    0.18415  -9.460  < 2e-16 ***\n",
       "age_factorLess than 25       3.14591    0.11541  27.259  < 2e-16 ***\n",
       "race_factorAfrican-American  0.65893    0.10815   6.093 1.11e-09 ***\n",
       "race_factorAsian            -0.98521    0.70537  -1.397   0.1625    \n",
       "race_factorHispanic         -0.06416    0.19133  -0.335   0.7374    \n",
       "race_factorNative American   0.44793    1.03546   0.433   0.6653    \n",
       "race_factorOther            -0.20543    0.22464  -0.914   0.3605    \n",
       "priors_count                 0.13764    0.01161  11.854  < 2e-16 ***\n",
       "crime_factorM               -0.16367    0.09807  -1.669   0.0951 .  \n",
       "two_year_recid               0.93448    0.11527   8.107 5.20e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 4731.8  on 4019  degrees of freedom\n",
       "Residual deviance: 2998.8  on 4008  degrees of freedom\n",
       "AIC: 3022.8\n",
       "\n",
       "Number of Fisher Scoring iterations: 6\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "df <- mutate(df, crime_factor = factor(c_charge_degree)) %>%\n",
    "      mutate(age_factor = as.factor(age_cat)) %>%\n",
    "      within(age_factor <- relevel(age_factor, ref = 1)) %>%\n",
    "      mutate(race_factor = factor(race,\n",
    "                                  labels = c(\"African-American\", \n",
    "                                             \"Asian\",\n",
    "                                             \"Caucasian\", \n",
    "                                             \"Hispanic\", \n",
    "                                             \"Native American\",\n",
    "                                             \"Other\"))) %>%\n",
    "      within(race_factor <- relevel(race_factor, ref = 3)) %>%\n",
    "      mutate(gender_factor = factor(sex, labels= c(\"Female\",\"Male\"))) %>%\n",
    "      within(gender_factor <- relevel(gender_factor, ref = 2)) %>%\n",
    "      mutate(score_factor = factor(v_score_text != \"Low\", labels = c(\"LowScore\",\"HighScore\")))\n",
    "model <- glm(score_factor ~ gender_factor + age_factor + race_factor +\n",
    "                            priors_count + crime_factor + two_year_recid, family=\"binomial\", data=df)\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The violent score overpredicts recidivism for black defendants by 77.3% compared to white defendants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 1.773921\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "control <- exp(-2.24274) / (1 + exp(-2.24274))\n",
    "exp(0.65893) / (1 - control + (control * exp(0.65893)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defendands under 25 are 7.4 times as likely to get a higher score as middle aged defendants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 7.41424\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "exp(3.14591) / (1 - control + (control * exp(3.14591)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictive Accuracy of COMPAS\n",
    "\n",
    "In order to test whether Compas scores do an accurate job of deciding whether an offender is Low, Medium or High risk,  we ran a Cox Proportional Hazards model. Northpointe, the company that created COMPAS and markets it to Law Enforcement, also ran a Cox model in their [validation study](http://cjb.sagepub.com/content/36/1/21.abstract).\n",
    "\n",
    "We used the counting model and removed people when they were incarcerated. Due to errors in the underlying jail data, we need to filter out 32 rows that have an end date more than the start date. Considering that there are 13,334 total rows in the data, such a small amount of errors will not affect the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 10314\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(survival)\n",
    "library(ggfortify)\n",
    "\n",
    "data <- filter(filter(read.csv(\"./cox-parsed.csv\"), score_text != \"N/A\"), end > start) %>%\n",
    "        mutate(race_factor = factor(race,\n",
    "                                  labels = c(\"African-American\", \n",
    "                                             \"Asian\",\n",
    "                                             \"Caucasian\", \n",
    "                                             \"Hispanic\", \n",
    "                                             \"Native American\",\n",
    "                                             \"Other\"))) %>%\n",
    "        within(race_factor <- relevel(race_factor, ref = 3)) %>%\n",
    "        mutate(score_factor = factor(score_text)) %>%\n",
    "        within(score_factor <- relevel(score_factor, ref=2))\n",
    "\n",
    "grp <- data[!duplicated(data$id),]\n",
    "nrow(grp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Low   High Medium \n",
       "  5751   1952   2611 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(grp$score_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Caucasian African-American            Asian         Hispanic \n",
       "            3569             5147               51              944 \n",
       " Native American            Other \n",
       "              32              571 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(grp$race_factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = f, data = data)\n",
       "\n",
       "  n= 13344, number of events= 3469 \n",
       "\n",
       "                      coef exp(coef) se(coef)     z Pr(>|z|)    \n",
       "score_factorHigh   1.24969   3.48927  0.04146 30.14   <2e-16 ***\n",
       "score_factorMedium 0.79627   2.21725  0.04077 19.53   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "score_factorHigh       3.489     0.2866     3.217     3.785\n",
       "score_factorMedium     2.217     0.4510     2.047     2.402\n",
       "\n",
       "Concordance= 0.636  (se = 0.004 )\n",
       "Rsquare= 0.068   (max possible= 0.99 )\n",
       "Likelihood ratio test= 942.8  on 2 df,   p=0\n",
       "Wald test            = 954.8  on 2 df,   p=0\n",
       "Score (logrank) test = 1055  on 2 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "f <- Surv(start, end, event, type=\"counting\") ~ score_factor\n",
    "model <- coxph(f, data=data)\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People placed in the High category are 3.5 times as likely to recidivate, and the COMPAS system's concordance 63.6%. This is lower than the accuracy quoted in the Northpoint study of 68%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = decile_f, data = data)\n",
       "\n",
       "  n= 13344, number of events= 3469 \n",
       "\n",
       "                 coef exp(coef) se(coef)     z Pr(>|z|)    \n",
       "decile_score 0.194931  1.215228 0.005801 33.61   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "             exp(coef) exp(-coef) lower .95 upper .95\n",
       "decile_score     1.215     0.8229     1.201     1.229\n",
       "\n",
       "Concordance= 0.664  (se = 0.005 )\n",
       "Rsquare= 0.08   (max possible= 0.99 )\n",
       "Likelihood ratio test= 1112  on 1 df,   p=0\n",
       "Wald test            = 1129  on 1 df,   p=0\n",
       "Score (logrank) test = 1208  on 1 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "decile_f <- Surv(start, end, event, type=\"counting\") ~ decile_score\n",
    "dmodel <- coxph(decile_f, data=data)\n",
    "summary(dmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COMPAS's decile scores are a bit more accurate at 66%.\n",
    "\n",
    "We can test if the algorithm is behaving differently across races by including a race interaction term in the cox model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = f2, data = data)\n",
       "\n",
       "  n= 13344, number of events= 3469 \n",
       "\n",
       "                                                   coef exp(coef) se(coef)\n",
       "race_factorAfrican-American                     0.27879   1.32154  0.06080\n",
       "race_factorAsian                               -0.77694   0.45981  0.50200\n",
       "race_factorHispanic                            -0.06352   0.93846  0.09673\n",
       "race_factorNative American                     -1.25468   0.28517  1.00100\n",
       "race_factorOther                                0.01419   1.01429  0.11049\n",
       "score_factorHigh                                1.28350   3.60927  0.08357\n",
       "score_factorMedium                              0.84286   2.32301  0.07142\n",
       "race_factorAfrican-American:score_factorHigh   -0.18976   0.82716  0.09985\n",
       "race_factorAsian:score_factorHigh               1.31556   3.72684  0.76835\n",
       "race_factorHispanic:score_factorHigh           -0.11913   0.88769  0.19833\n",
       "race_factorNative American:score_factorHigh     1.95571   7.06896  1.08336\n",
       "race_factorOther:score_factorHigh               0.41455   1.51369  0.25921\n",
       "race_factorAfrican-American:score_factorMedium -0.17261   0.84147  0.09097\n",
       "race_factorAsian:score_factorMedium             0.98619   2.68101  0.71072\n",
       "race_factorHispanic:score_factorMedium          0.06529   1.06747  0.16415\n",
       "race_factorNative American:score_factorMedium   1.38965   4.01345  1.12032\n",
       "race_factorOther:score_factorMedium            -0.33393   0.71610  0.23188\n",
       "                                                    z Pr(>|z|)    \n",
       "race_factorAfrican-American                     4.586 4.52e-06 ***\n",
       "race_factorAsian                               -1.548   0.1217    \n",
       "race_factorHispanic                            -0.657   0.5114    \n",
       "race_factorNative American                     -1.253   0.2101    \n",
       "race_factorOther                                0.128   0.8978    \n",
       "score_factorHigh                               15.358  < 2e-16 ***\n",
       "score_factorMedium                             11.801  < 2e-16 ***\n",
       "race_factorAfrican-American:score_factorHigh   -1.900   0.0574 .  \n",
       "race_factorAsian:score_factorHigh               1.712   0.0869 .  \n",
       "race_factorHispanic:score_factorHigh           -0.601   0.5480    \n",
       "race_factorNative American:score_factorHigh     1.805   0.0710 .  \n",
       "race_factorOther:score_factorHigh               1.599   0.1098    \n",
       "race_factorAfrican-American:score_factorMedium -1.897   0.0578 .  \n",
       "race_factorAsian:score_factorMedium             1.388   0.1653    \n",
       "race_factorHispanic:score_factorMedium          0.398   0.6908    \n",
       "race_factorNative American:score_factorMedium   1.240   0.2148    \n",
       "race_factorOther:score_factorMedium            -1.440   0.1498    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                                               exp(coef) exp(-coef) lower .95\n",
       "race_factorAfrican-American                       1.3215     0.7567   1.17308\n",
       "race_factorAsian                                  0.4598     2.1748   0.17190\n",
       "race_factorHispanic                               0.9385     1.0656   0.77638\n",
       "race_factorNative American                        0.2852     3.5067   0.04009\n",
       "race_factorOther                                  1.0143     0.9859   0.81680\n",
       "score_factorHigh                                  3.6093     0.2771   3.06395\n",
       "score_factorMedium                                2.3230     0.4305   2.01956\n",
       "race_factorAfrican-American:score_factorHigh      0.8272     1.2090   0.68014\n",
       "race_factorAsian:score_factorHigh                 3.7268     0.2683   0.82664\n",
       "race_factorHispanic:score_factorHigh              0.8877     1.1265   0.60179\n",
       "race_factorNative American:score_factorHigh       7.0690     0.1415   0.84566\n",
       "race_factorOther:score_factorHigh                 1.5137     0.6606   0.91075\n",
       "race_factorAfrican-American:score_factorMedium    0.8415     1.1884   0.70405\n",
       "race_factorAsian:score_factorMedium               2.6810     0.3730   0.66579\n",
       "race_factorHispanic:score_factorMedium            1.0675     0.9368   0.77380\n",
       "race_factorNative American:score_factorMedium     4.0135     0.2492   0.44658\n",
       "race_factorOther:score_factorMedium               0.7161     1.3964   0.45457\n",
       "                                               upper .95\n",
       "race_factorAfrican-American                        1.489\n",
       "race_factorAsian                                   1.230\n",
       "race_factorHispanic                                1.134\n",
       "race_factorNative American                         2.028\n",
       "race_factorOther                                   1.260\n",
       "score_factorHigh                                   4.252\n",
       "score_factorMedium                                 2.672\n",
       "race_factorAfrican-American:score_factorHigh       1.006\n",
       "race_factorAsian:score_factorHigh                 16.802\n",
       "race_factorHispanic:score_factorHigh               1.309\n",
       "race_factorNative American:score_factorHigh       59.090\n",
       "race_factorOther:score_factorHigh                  2.516\n",
       "race_factorAfrican-American:score_factorMedium     1.006\n",
       "race_factorAsian:score_factorMedium               10.796\n",
       "race_factorHispanic:score_factorMedium             1.473\n",
       "race_factorNative American:score_factorMedium     36.069\n",
       "race_factorOther:score_factorMedium                1.128\n",
       "\n",
       "Concordance= 0.646  (se = 0.005 )\n",
       "Rsquare= 0.072   (max possible= 0.99 )\n",
       "Likelihood ratio test= 993.7  on 17 df,   p=0\n",
       "Wald test            = 988.8  on 17 df,   p=0\n",
       "Score (logrank) test = 1105  on 17 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "f2 <- Surv(start, end, event, type=\"counting\") ~ race_factor + score_factor + race_factor * score_factor\n",
    "model <- coxph(f2, data=data)\n",
    "print(summary(model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interaction term shows a similar disparity as the logistic regression above.\n",
    "\n",
    "High risk white defendants are 3.61 more likely than low risk white defendants, while High risk black defendants are 2.99 more likely than low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black High Hazard: 2.99\n",
      "White High Hazard: 3.61\n",
      "Black Medium Hazard: 1.95\n",
      "White Medium Hazard: 2.32\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print(\"Black High Hazard: %.2f\" % (math.exp(-0.18976 + 1.28350)))\n",
    "print(\"White High Hazard: %.2f\" % (math.exp(1.28350)))\n",
    "print(\"Black Medium Hazard: %.2f\" % (math.exp(0.84286-0.17261)))\n",
    "print(\"White Medium Hazard: %.2f\" % (math.exp(0.84286)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GgdSQ6WoazyUQgA8Om5w0h9w7B7xeL6LRKGmUEGB6VwmMC7OX+0yx7mHlLWHK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\n7m77+/vlxIkT7kdTBrN69Wo5cOAAPNGThuH8XJFBnZLbK2Ys1ps3D0k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ef7R9QDoHfPLy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\nLVCkIIDAgAkQEA4YPTtGAAEEEEAgugI2MXq2B+WI/Hpa4FM4JjG2Q+fOW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BBA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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 900 -h 563 -u px\n",
    "\n",
    "fit <- survfit(f, data=data)\n",
    "\n",
    "plotty <- function(fit, title) {\n",
    "  return(autoplot(fit, conf.int=T, censor=F) + ggtitle(title) + ylim(0,1))\n",
    "}\n",
    "plotty(fit, \"Overall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Black defendants do recidivate at higher rates according to race specific Kaplan Meier plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T+O7pn6Ix1GwUYmO4edzA1oEUAUVAEVAEFIGJRiBSPhfOcBAlViP2nPahod01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\nAgEIQAACEIAABCAAAQhAoKYJ/H8avYm6sjaIQgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 900 -h 363 -u px\n",
    "white <- filter(data, race == \"Caucasian\")\n",
    "white_fit <- survfit(f, data=white)\n",
    "\n",
    "black <- filter(data, race == \"African-American\")\n",
    "black_fit <- survfit(f, data=black)\n",
    "\n",
    "grid.arrange(plotty(white_fit, \"White defendants\"), \n",
    "             plotty(black_fit, \"Black defendants\"), ncol=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call: survfit(formula = f, data = data)\n",
       "\n",
       "                score_factor=Low \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     2.75e+03     1.21e+03     6.74e+03     2.79e+03     7.76e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    5.74e-03     7.64e-01     7.87e-01 \n",
       "\n",
       "                score_factor=High \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     5.05e+02     9.72e+02     6.73e+03     1.26e+03     4.08e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    1.22e-02     3.85e-01     4.33e-01 \n",
       "\n",
       "                score_factor=Medium \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     9.79e+02     1.02e+03     6.74e+03     1.39e+03     5.63e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    1.03e-02     5.43e-01     5.84e-01 \n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(fit, times=c(730))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call: survfit(formula = f, data = black)\n",
       "\n",
       "                score_factor=Low \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     1.02e+03     5.29e+02     2.59e+03     1.04e+03     7.43e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    9.70e-03     7.24e-01     7.62e-01 \n",
       "\n",
       "                score_factor=High \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     3.62e+02     7.19e+02     2.59e+03     9.14e+02     3.98e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    1.42e-02     3.71e-01     4.26e-01 \n",
       "\n",
       "                score_factor=Medium \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     5.78e+02     6.23e+02     2.59e+03     7.85e+02     5.48e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    1.34e-02     5.23e-01     5.75e-01 \n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(black_fit, times=c(730))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call: survfit(formula = f, data = white)\n",
       "\n",
       "                score_factor=Low \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     1.16e+03     4.57e+02     2.80e+03     1.18e+03     7.95e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    8.63e-03     7.78e-01     8.12e-01 \n",
       "\n",
       "                score_factor=High \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     1.02e+02     1.91e+02     2.69e+03     2.78e+02     4.35e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    2.72e-02     3.85e-01     4.91e-01 \n",
       "\n",
       "                score_factor=Medium \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     2.99e+02     3.06e+02     2.76e+03     4.60e+02     5.76e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    1.85e-02     5.41e-01     6.13e-01 \n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(white_fit, times=c(730))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Race specific models have similar concordance values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = f, data = white)\n",
       "\n",
       "  n= 4564, number of events= 1023 \n",
       "\n",
       "                      coef exp(coef) se(coef)     z Pr(>|z|)    \n",
       "score_factorHigh   1.27628   3.58327  0.08365 15.26   <2e-16 ***\n",
       "score_factorMedium 0.83965   2.31556  0.07144 11.75   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "score_factorHigh       3.583     0.2791     3.041     4.222\n",
       "score_factorMedium     2.316     0.4319     2.013     2.664\n",
       "\n",
       "Concordance= 0.625  (se = 0.007 )\n",
       "Rsquare= 0.056   (max possible= 0.971 )\n",
       "Likelihood ratio test= 262.8  on 2 df,   p=0\n",
       "Wald test            = 282.2  on 2 df,   p=0\n",
       "Score (logrank) test = 311.7  on 2 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(coxph(f, data=white))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = f, data = black)\n",
       "\n",
       "  n= 6862, number of events= 2035 \n",
       "\n",
       "                      coef exp(coef) se(coef)     z Pr(>|z|)    \n",
       "score_factorHigh   1.09514   2.98959  0.05475 20.00   <2e-16 ***\n",
       "score_factorMedium 0.67025   1.95473  0.05636 11.89   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "score_factorHigh       2.990     0.3345     2.685     3.328\n",
       "score_factorMedium     1.955     0.5116     1.750     2.183\n",
       "\n",
       "Concordance= 0.623  (se = 0.006 )\n",
       "Rsquare= 0.059   (max possible= 0.992 )\n",
       "Likelihood ratio test= 416.9  on 2 df,   p=0\n",
       "Wald test            = 401.3  on 2 df,   p=0\n",
       "Score (logrank) test = 432.9  on 2 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(coxph(f, data=black))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compas's violent recidivism score has a slightly higher overall concordance score of 65.1%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 10985\n",
       "Call:\n",
       "coxph(formula = vf, data = violent_data)\n",
       "\n",
       "  n= 18178, number of events= 818 \n",
       "\n",
       "                      coef exp(coef) se(coef)      z Pr(>|z|)    \n",
       "score_factorHigh   1.38311   3.98729  0.08410 16.447   <2e-16 ***\n",
       "score_factorMedium 0.80350   2.23334  0.08699  9.236   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "score_factorHigh       3.987     0.2508     3.381     4.702\n",
       "score_factorMedium     2.233     0.4478     1.883     2.649\n",
       "\n",
       "Concordance= 0.651  (se = 0.009 )\n",
       "Rsquare= 0.015   (max possible= 0.556 )\n",
       "Likelihood ratio test= 270.6  on 2 df,   p=0\n",
       "Wald test            = 272.2  on 2 df,   p=0\n",
       "Score (logrank) test = 307.9  on 2 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "violent_data <- filter(filter(read.csv(\"./cox-violent-parsed.csv\"), score_text != \"N/A\"), end > start) %>%\n",
    "        mutate(race_factor = factor(race,\n",
    "                                  labels = c(\"African-American\", \n",
    "                                             \"Asian\",\n",
    "                                             \"Caucasian\", \n",
    "                                             \"Hispanic\", \n",
    "                                             \"Native American\",\n",
    "                                             \"Other\"))) %>%\n",
    "        within(race_factor <- relevel(race_factor, ref = 3)) %>%\n",
    "        mutate(score_factor = factor(score_text)) %>%\n",
    "        within(score_factor <- relevel(score_factor, ref=2))\n",
    "\n",
    "\n",
    "vf <- Surv(start, end, event, type=\"counting\") ~ score_factor\n",
    "vmodel <- coxph(vf, data=violent_data)\n",
    "vgrp <- violent_data[!duplicated(violent_data$id),]\n",
    "print(nrow(vgrp))\n",
    "summary(vmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, there isn't a significant coefficient on African American's with High Scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = vf2, data = violent_data)\n",
       "\n",
       "  n= 18178, number of events= 818 \n",
       "\n",
       "                                                     coef  exp(coef)   se(coef)\n",
       "race_factorAfrican-American                     1.976e-01  1.219e+00  1.321e-01\n",
       "race_factorAsian                               -5.838e-01  5.577e-01  1.005e+00\n",
       "race_factorHispanic                            -1.754e-01  8.391e-01  2.150e-01\n",
       "race_factorNative American                     -1.304e+01  2.162e-06  7.607e+02\n",
       "race_factorOther                               -4.459e-02  9.564e-01  2.433e-01\n",
       "score_factorHigh                                1.250e+00  3.491e+00  1.720e-01\n",
       "score_factorMedium                              6.292e-01  1.876e+00  1.583e-01\n",
       "race_factorAfrican-American:score_factorHigh   -7.627e-03  9.924e-01  2.063e-01\n",
       "race_factorAsian:score_factorHigh               1.552e+00  4.721e+00  1.425e+00\n",
       "race_factorHispanic:score_factorHigh            1.606e-01  1.174e+00  3.969e-01\n",
       "race_factorNative American:score_factorHigh     1.465e+01  2.307e+06  7.607e+02\n",
       "race_factorOther:score_factorHigh               1.440e+00  4.220e+00  4.037e-01\n",
       "race_factorAfrican-American:score_factorMedium  1.297e-01  1.139e+00  1.987e-01\n",
       "race_factorAsian:score_factorMedium             1.651e+00  5.211e+00  1.235e+00\n",
       "race_factorHispanic:score_factorMedium          4.632e-01  1.589e+00  3.430e-01\n",
       "race_factorNative American:score_factorMedium   1.343e+01  6.782e+05  7.607e+02\n",
       "race_factorOther:score_factorMedium            -1.562e-02  9.845e-01  4.916e-01\n",
       "                                                    z Pr(>|z|)    \n",
       "race_factorAfrican-American                     1.497 0.134487    \n",
       "race_factorAsian                               -0.581 0.561115    \n",
       "race_factorHispanic                            -0.816 0.414623    \n",
       "race_factorNative American                     -0.017 0.986318    \n",
       "race_factorOther                               -0.183 0.854546    \n",
       "score_factorHigh                                7.268 3.64e-13 ***\n",
       "score_factorMedium                              3.976 7.02e-05 ***\n",
       "race_factorAfrican-American:score_factorHigh   -0.037 0.970513    \n",
       "race_factorAsian:score_factorHigh               1.089 0.275997    \n",
       "race_factorHispanic:score_factorHigh            0.405 0.685751    \n",
       "race_factorNative American:score_factorHigh     0.019 0.984633    \n",
       "race_factorOther:score_factorHigh               3.567 0.000361 ***\n",
       "race_factorAfrican-American:score_factorMedium  0.653 0.513889    \n",
       "race_factorAsian:score_factorMedium             1.337 0.181322    \n",
       "race_factorHispanic:score_factorMedium          1.350 0.176860    \n",
       "race_factorNative American:score_factorMedium   0.018 0.985917    \n",
       "race_factorOther:score_factorMedium            -0.032 0.974652    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                                               exp(coef) exp(-coef) lower .95\n",
       "race_factorAfrican-American                    1.219e+00  8.207e-01   0.94065\n",
       "race_factorAsian                               5.577e-01  1.793e+00   0.07786\n",
       "race_factorHispanic                            8.391e-01  1.192e+00   0.55062\n",
       "race_factorNative American                     2.162e-06  4.625e+05   0.00000\n",
       "race_factorOther                               9.564e-01  1.046e+00   0.59370\n",
       "score_factorHigh                               3.491e+00  2.865e-01   2.49195\n",
       "score_factorMedium                             1.876e+00  5.330e-01   1.37575\n",
       "race_factorAfrican-American:score_factorHigh   9.924e-01  1.008e+00   0.66232\n",
       "race_factorAsian:score_factorHigh              4.721e+00  2.118e-01   0.28928\n",
       "race_factorHispanic:score_factorHigh           1.174e+00  8.516e-01   0.53939\n",
       "race_factorNative American:score_factorHigh    2.307e+06  4.334e-07   0.00000\n",
       "race_factorOther:score_factorHigh              4.220e+00  2.369e-01   1.91314\n",
       "race_factorAfrican-American:score_factorMedium 1.139e+00  8.783e-01   0.77125\n",
       "race_factorAsian:score_factorMedium            5.211e+00  1.919e-01   0.46315\n",
       "race_factorHispanic:score_factorMedium         1.589e+00  6.293e-01   0.81136\n",
       "race_factorNative American:score_factorMedium  6.782e+05  1.474e-06   0.00000\n",
       "race_factorOther:score_factorMedium            9.845e-01  1.016e+00   0.37560\n",
       "                                               upper .95\n",
       "race_factorAfrican-American                        1.578\n",
       "race_factorAsian                                   3.995\n",
       "race_factorHispanic                                1.279\n",
       "race_factorNative American                           Inf\n",
       "race_factorOther                                   1.541\n",
       "score_factorHigh                                   4.891\n",
       "score_factorMedium                                 2.558\n",
       "race_factorAfrican-American:score_factorHigh       1.487\n",
       "race_factorAsian:score_factorHigh                 77.045\n",
       "race_factorHispanic:score_factorHigh               2.556\n",
       "race_factorNative American:score_factorHigh          Inf\n",
       "race_factorOther:score_factorHigh                  9.310\n",
       "race_factorAfrican-American:score_factorMedium     1.681\n",
       "race_factorAsian:score_factorMedium               58.627\n",
       "race_factorHispanic:score_factorMedium             3.113\n",
       "race_factorNative American:score_factorMedium        Inf\n",
       "race_factorOther:score_factorMedium                2.581\n",
       "\n",
       "Concordance= 0.663  (se = 0.01 )\n",
       "Rsquare= 0.017   (max possible= 0.556 )\n",
       "Likelihood ratio test= 303  on 17 df,   p=0\n",
       "Wald test            = 317.9  on 17 df,   p=0\n",
       "Score (logrank) test = 387.1  on 17 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "vf2 <- Surv(start, end, event, type=\"counting\") ~ race_factor + race_factor * score_factor\n",
    "vmodel <- coxph(vf2, data=violent_data)\n",
    "summary(vmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = vf, data = filter(violent_data, race == \"African-American\"))\n",
       "\n",
       "  n= 9712, number of events= 495 \n",
       "\n",
       "                     coef exp(coef) se(coef)      z Pr(>|z|)    \n",
       "score_factorHigh   1.2412    3.4596   0.1140 10.891  < 2e-16 ***\n",
       "score_factorMedium 0.7589    2.1359   0.1202  6.315 2.71e-10 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "score_factorHigh       3.460     0.2891     2.767     4.325\n",
       "score_factorMedium     2.136     0.4682     1.688     2.703\n",
       "\n",
       "Concordance= 0.638  (se = 0.012 )\n",
       "Rsquare= 0.013   (max possible= 0.571 )\n",
       "Likelihood ratio test= 127.5  on 2 df,   p=0\n",
       "Wald test            = 118.7  on 2 df,   p=0\n",
       "Score (logrank) test = 130.9  on 2 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(coxph(vf, data=filter(violent_data, race == \"African-American\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call:\n",
       "coxph(formula = vf, data = filter(violent_data, race == \"Caucasian\"))\n",
       "\n",
       "  n= 6041, number of events= 221 \n",
       "\n",
       "                     coef exp(coef) se(coef)     z Pr(>|z|)    \n",
       "score_factorHigh   1.2502    3.4909   0.1720 7.267 3.66e-13 ***\n",
       "score_factorMedium 0.6310    1.8794   0.1583 3.986 6.71e-05 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "                   exp(coef) exp(-coef) lower .95 upper .95\n",
       "score_factorHigh       3.491     0.2865     2.492     4.891\n",
       "score_factorMedium     1.879     0.5321     1.378     2.563\n",
       "\n",
       "Concordance= 0.61  (se = 0.016 )\n",
       "Rsquare= 0.008   (max possible= 0.444 )\n",
       "Likelihood ratio test= 49.63  on 2 df,   p=1.672e-11\n",
       "Wald test            = 55.48  on 2 df,   p=8.973e-13\n",
       "Score (logrank) test = 61.24  on 2 df,   p=5.018e-14\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(coxph(vf, data=filter(violent_data, race == \"Caucasian\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q9PMDn1P5WxgZGalkWstq04zfnOrBZX1Fa/7keTbRxEstJoMc2mtfvadkwxc8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\nZDxvvHxanj4RlhOoyTidyJNAPzyosrmFhLAVpHfaqy5kYb4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yxXMQKxEX/+nT6lbqiY7u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\nV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AACxSuQ94BwzZo1pmBQk4I9DZvxAsJFixa5gFCfNW3a1JYsWeKGlbZt21az/Hnuze5/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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 900 -h 363 -u px\n",
    "white <- filter(violent_data, race == \"Caucasian\")\n",
    "white_fit <- survfit(vf, data=white)\n",
    "\n",
    "black <- filter(violent_data, race == \"African-American\")\n",
    "black_fit <- survfit(vf, data=black)\n",
    "\n",
    "grid.arrange(plotty(white_fit, \"White defendants\"), \n",
    "             plotty(black_fit, \"Black defendants\"), ncol=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Directions of the Racial Bias\n",
    "\n",
    "The above analysis shows that the Compas algorithm does overpredict African-American defendant's future recidivism, but we haven't yet explored the direction of the bias. We can discover fine differences in overprediction and underprediction by comparing Compas scores across racial lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from truth_tables import PeekyReader, Person, table, is_race, count, vtable, hightable, vhightable\n",
    "from csv import DictReader\n",
    "\n",
    "people = []\n",
    "with open(\"./cox-parsed.csv\") as f:\n",
    "    reader = PeekyReader(DictReader(f))\n",
    "    try:\n",
    "        while True:\n",
    "            p = Person(reader)\n",
    "            if p.valid:\n",
    "                people.append(p)\n",
    "    except StopIteration:\n",
    "        pass\n",
    "\n",
    "pop = list(filter(lambda i: ((i.recidivist == True and i.lifetime <= 730) or\n",
    "                              i.lifetime > 730), list(filter(lambda x: x.score_valid, people))))\n",
    "recid = list(filter(lambda i: i.recidivist == True and i.lifetime <= 730, pop))\n",
    "rset = set(recid)\n",
    "surv = [i for i in pop if i not in rset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All defendants\n",
      "           \tLow\tHigh\n",
      "Survived   \t2681\t1282\t0.55\n",
      "Recidivated\t1216\t2035\t0.45\n",
      "Total: 7214.00\n",
      "False positive rate: 32.35\n",
      "False negative rate: 37.40\n",
      "Specificity: 0.68\n",
      "Sensitivity: 0.63\n",
      "Prevalence: 0.45\n",
      "PPV: 0.61\n",
      "NPV: 0.69\n",
      "LR+: 1.94\n",
      "LR-: 0.55\n"
     ]
    }
   ],
   "source": [
    "print(\"All defendants\")\n",
    "table(list(recid), list(surv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pop: 7214\n"
     ]
    }
   ],
   "source": [
    "print(\"Total pop: %i\" % (2681 + 1282 + 1216 + 2035))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average followup time 622.87 (sd 392.19)\n",
      "Median followup time 766\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "print(\"Average followup time %.2f (sd %.2f)\" % (statistics.mean(map(lambda i: i.lifetime, pop)),\n",
    "                                                statistics.stdev(map(lambda i: i.lifetime, pop))))\n",
    "print(\"Median followup time %i\" % (statistics.median(map(lambda i: i.lifetime, pop))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, the false positive rate is 32.35%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black defendants\n",
      "           \tLow\tHigh\n",
      "Survived   \t990\t805\t0.49\n",
      "Recidivated\t532\t1369\t0.51\n",
      "Total: 3696.00\n",
      "False positive rate: 44.85\n",
      "False negative rate: 27.99\n",
      "Specificity: 0.55\n",
      "Sensitivity: 0.72\n",
      "Prevalence: 0.51\n",
      "PPV: 0.63\n",
      "NPV: 0.65\n",
      "LR+: 1.61\n",
      "LR-: 0.51\n"
     ]
    }
   ],
   "source": [
    "print(\"Black defendants\")\n",
    "is_afam = is_race(\"African-American\")\n",
    "table(list(filter(is_afam, recid)), list(filter(is_afam, surv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That number is higher for African Americans at 44.85%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White defendants\n",
      "           \tLow\tHigh\n",
      "Survived   \t1139\t349\t0.61\n",
      "Recidivated\t461\t505\t0.39\n",
      "Total: 2454.00\n",
      "False positive rate: 23.45\n",
      "False negative rate: 47.72\n",
      "Specificity: 0.77\n",
      "Sensitivity: 0.52\n",
      "Prevalence: 0.39\n",
      "PPV: 0.59\n",
      "NPV: 0.71\n",
      "LR+: 2.23\n",
      "LR-: 0.62\n"
     ]
    }
   ],
   "source": [
    "print(\"White defendants\")\n",
    "is_white = is_race(\"Caucasian\")\n",
    "table(list(filter(is_white, recid)), list(filter(is_white, surv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And lower for whites at 23.45%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9125799573560769"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "44.85 / 23.45"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which means under COMPAS black defendants are 91% more likely to get a higher score and not go on to commit more crimes than white defendants after two year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COMPAS scores misclassify white reoffenders as low risk at 70.4% more often than black reoffenders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7048946052161487"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "47.72 / 27.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           \tLow\tHigh\n",
      "Survived   \t1407\t81\t0.61\n",
      "Recidivated\t771\t195\t0.39\n",
      "Total: 2454.00\n",
      "False positive rate: 5.44\n",
      "False negative rate: 79.81\n",
      "Specificity: 0.95\n",
      "Sensitivity: 0.20\n",
      "Prevalence: 0.39\n",
      "PPV: 0.71\n",
      "NPV: 0.65\n",
      "LR+: 3.71\n",
      "LR-: 0.84\n"
     ]
    }
   ],
   "source": [
    "hightable(list(filter(is_white, recid)), list(filter(is_white, surv)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           \tLow\tHigh\n",
      "Survived   \t1511\t284\t0.49\n",
      "Recidivated\t1160\t741\t0.51\n",
      "Total: 3696.00\n",
      "False positive rate: 15.82\n",
      "False negative rate: 61.02\n",
      "Specificity: 0.84\n",
      "Sensitivity: 0.39\n",
      "Prevalence: 0.51\n",
      "PPV: 0.72\n",
      "NPV: 0.57\n",
      "LR+: 2.46\n",
      "LR-: 0.72\n"
     ]
    }
   ],
   "source": [
    "hightable(list(filter(is_afam, recid)), list(filter(is_afam, surv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Risk of Violent Recidivism\n",
    "\n",
    "Compas also offers a score that aims to measure a persons risk of violent recidivism, which has a similar overall accuracy to the Recidivism score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vpeople = []\n",
    "with open(\"./cox-violent-parsed.csv\") as f:\n",
    "    reader = PeekyReader(DictReader(f))\n",
    "    try:\n",
    "        while True:\n",
    "            p = Person(reader)\n",
    "            if p.valid:\n",
    "                vpeople.append(p)\n",
    "    except StopIteration:\n",
    "        pass\n",
    "\n",
    "vpop = list(filter(lambda i: ((i.violent_recidivist == True and i.lifetime <= 730) or\n",
    "                              i.lifetime > 730), list(filter(lambda x: x.vscore_valid, vpeople))))\n",
    "vrecid = list(filter(lambda i: i.violent_recidivist == True and i.lifetime <= 730, vpeople))\n",
    "vrset = set(vrecid)\n",
    "vsurv = [i for i in vpop if i not in vrset]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All defendants\n",
      "           \tLow\tHigh\n",
      "Survived   \t4121\t1597\t0.89\n",
      "Recidivated\t347\t389\t0.11\n",
      "Total: 6454.00\n",
      "False positive rate: 27.93\n",
      "False negative rate: 47.15\n",
      "Specificity: 0.72\n",
      "Sensitivity: 0.53\n",
      "Prevalence: 0.11\n",
      "PPV: 0.20\n",
      "NPV: 0.92\n",
      "LR+: 1.89\n",
      "LR-: 0.65\n"
     ]
    }
   ],
   "source": [
    "print(\"All defendants\")\n",
    "vtable(list(vrecid), list(vsurv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even moreso for Black defendants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black defendants\n",
      "           \tLow\tHigh\n",
      "Survived   \t1692\t1043\t0.86\n",
      "Recidivated\t170\t273\t0.14\n",
      "Total: 3178.00\n",
      "False positive rate: 38.14\n",
      "False negative rate: 38.37\n",
      "Specificity: 0.62\n",
      "Sensitivity: 0.62\n",
      "Prevalence: 0.14\n",
      "PPV: 0.21\n",
      "NPV: 0.91\n",
      "LR+: 1.62\n",
      "LR-: 0.62\n"
     ]
    }
   ],
   "source": [
    "print(\"Black defendants\")\n",
    "is_afam = is_race(\"African-American\")\n",
    "vtable(list(filter(is_afam, vrecid)), list(filter(is_afam, vsurv)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White defendants\n",
      "           \tLow\tHigh\n",
      "Survived   \t1679\t380\t0.91\n",
      "Recidivated\t129\t77\t0.09\n",
      "Total: 2265.00\n",
      "False positive rate: 18.46\n",
      "False negative rate: 62.62\n",
      "Specificity: 0.82\n",
      "Sensitivity: 0.37\n",
      "Prevalence: 0.09\n",
      "PPV: 0.17\n",
      "NPV: 0.93\n",
      "LR+: 2.03\n",
      "LR-: 0.77\n"
     ]
    }
   ],
   "source": [
    "print(\"White defendants\")\n",
    "is_white = is_race(\"Caucasian\")\n",
    "vtable(list(filter(is_white, vrecid)), list(filter(is_white, vsurv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Black defendants are twice as likely to be false positives for a Higher violent score than white defendants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.066088840736728"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "38.14 / 18.46"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "White defendants are 63% more likely to get a lower score and commit another crime than Black defendants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.63200416992442"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "62.62 / 38.37"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender differences in Compas scores\n",
    "\n",
    "In terms of underlying recidivism rates, we can look at gender specific Kaplan Meier estimates. There is a striking difference between women and men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "\n",
    "female <- filter(data, sex == \"Female\")\n",
    "male   <- filter(data, sex == \"Male\")\n",
    "male_fit <- survfit(f, data=male)\n",
    "female_fit <- survfit(f, data=female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call: survfit(formula = f, data = male)\n",
       "\n",
       "                score_factor=Low \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     2.13e+03     1.02e+03     5.32e+03     2.18e+03     7.58e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    6.67e-03     7.45e-01     7.71e-01 \n",
       "\n",
       "                score_factor=High \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "     730.000      421.000      857.000     5307.000     1047.000        0.388 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "       0.013        0.363        0.414 \n",
       "\n",
       "                score_factor=Medium \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     7.52e+02     8.38e+02     5.31e+03     1.06e+03     5.35e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    1.18e-02     5.13e-01     5.59e-01 \n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(male_fit, times=c(730))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Call: survfit(formula = f, data = female)\n",
       "\n",
       "                score_factor=Low \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     6.17e+02     1.94e+02     1.42e+03     6.14e+02     8.38e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    1.07e-02     8.17e-01     8.59e-01 \n",
       "\n",
       "                score_factor=High \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     8.40e+01     1.15e+02     1.39e+03     2.09e+02     5.25e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    3.25e-02     4.65e-01     5.93e-01 \n",
       "\n",
       "                score_factor=Medium \n",
       "        time       n.risk      n.event      entered     censored     survival \n",
       "    7.30e+02     2.27e+02     1.84e+02     1.41e+03     3.33e+02     6.59e-01 \n",
       "     std.err lower 95% CI upper 95% CI \n",
       "    2.06e-02     6.19e-01     7.00e-01 \n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "summary(female_fit, times=c(730))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8HzmFVpMBhQnrl9jrKaJXqqJ7PiKQYUZSduo04+hhgTlPTRtcq9VqThflYv2v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\nh+KKAlwcw0wQm9l9OAWTjX8M8MTC8DwKzcZyGU3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OeA0mMwxNZxFZtWbCXcfbwUBMb0U5\nXlgKqhAm2DgNp9+rv0hYnXwkj8yyBAVLUTz+ngevH3cQHXgkY7IZrTV2A5mNNNG4XqGl99JXiFNa\nC8lp5A5eTBAQBASBhYzAfO4H4243dNTvha69Hubtb8LwxmvAHXdRZ0Ts1gMUJfRcGvBJ/A7kOSOq\nlpAHGjmN9E7ql56n9NF8Wl9N2rtsFGhUGoY2Zr22xFSpRGIbsnxlCHB2Bk/sDLaTFiI7iEz0FaQo\nYaIxqynr2mratsxqej7QomQvTvrO4Pme1yhNNAf1tlqa5ypm0wpz2WgTTE7DE1uj/wKlulIyKTmH\nnLaqp98OnGpqp/RXh95+WWrqaCOyIAgIAjOGwKVf89N0yl27duGXv/wlFlGx+XPPPYcvfvGLoy13\nUweyfft29ZmdwEJKg/z85z+Pr3zlK7jhhhvU+pycnLnjEPIVs8PLEhOh5JepupmU/yINi2E8feqK\nHUJuVkdRSZd1ZHSPU3CcVoOi9/aOkUrK0TzWKuT6w/3nrOgYMGIZjcLWTYFwRrstTilNdP16hg3g\nyWFhDcUwjAnRRO0YmQsCgoAgMN8RmO/9YNTlhpEHQmlQNLxpCwxP/xbGfXsRWbde1RiO5RCaqLtk\nkjTOOmGrzg+jgchkXjzkwgObB0b1czXW6z7fCMsMDzaKTS8CHK3TCGi4/pBZSRVpDTmHvcQCrTlz\n2lmZ1TTHOZLqf2POFsWCesx7Emf852k6h2d7XlHO3tqclbjesRGpJDX+aEBFHbX2aIhZLfJ1MKuq\nlQhrjOQsciSRSwKErOYSUrIkCMwEAtPuELIz+PDDD4PTwB566CEVFXTTaCJbXV0dPvnJT1KGpV9t\n+8xnPoOzZ8/i+uuvxwMPPIBcYjBLtNdffx08ysqpDrfddlvipnGXeX+OXtm4riHLxuxdZhLljZM+\nodJjamsd+4xUc4HDB2E2T65eihvk++G0j9GIKw+j0jmZApyNb9Xt1ONU20gHqlam/FdPxGP1pTHU\nFgVwtMWkROyXlFlw26rgqCg9349y9Lj9KVqYDm0ZsFAkkkZ3nTQUnMY01jM+VzaN8eLvA0/ZNO3Z\nzNR3Ltvn4e8bW7bPw+fg70K2z6PdD59PTBDINgLzvh+kDkdP2oNxqomnDg36+98P4y8fg576Qv5s\nJnmmsRi1uRQ/FDegb3jkHXPXujCe309lDaecuHddQMlUJD6fPj/VHZLIPevk8t+xhVJT+X2bTeP2\n+Z2U7f5pNvWDDoz8ltBw5bpCH9Uhtgc6SKuwT0UUWZpCM3rK2Gy9hmQvrlGreHi4PdyJnf378B/t\nj2KJo540FmtVuugSxyLtsLTz5mgbldNc2sTP2WN0wW1yqehjDkU0Ux3Emew3st0/8Z3P5P1cQlqW\nBIFLCEy7QzgwMKCcQT5FEUkssEOnOYTaaR9//HHcfffdar9Tp07h0KFDGKb6uFbSMvrqV79Kcn8j\nL6aDBw+Ct/OP+nvvvVc7fMI5v0y0jmPCna9wB/4jHtVe4mWWixjLqCOLUQ2giV+qlmSCmrEO0dbz\n/ahzJTpQlGar49rEi8aLkbgOzT0jHa22PnVeRWSpVUUxLCmP4IX9Rjy3T491teQoFseU48Sd4ZV2\nhFS7jm4vpau64mkJAjQHjTv3bJo2OKCdL1vn0hzPbN8PXz9/D2biPHyumTjPTNxPdAq1u3z/YoLA\nVBBYEP0glUvEKeOFzVhaihjVFHIGjG7LtcRk0g8daRWyc5jOllSQKH2nHj0XS4dvXhXDo2+b8It3\n7PjItRGKFF4aKOQlZryuK46qfp0HVK+0f0p3TYnr+H3O775sn0frl7L9np1KP2gBidTTv2JHoYIm\nRAQyncFulVo6QGmmnGqaik+NpQp17hpszd+Mt3rew57Bg7jgbyX9xBBq7FVY7V6uiGpY7L7WXgl2\n9MaqK/TCD2/YjzZyMp1Ui8hRQxOxmTpNJKelHEQ9DGaDiiomPrtsLGf7+fA1Sz+YjScnbU4GgWl3\nCBNPznWBqX9IMYpqvfXWW/iXf/kXtSs7hnfddZeKmj3xxBN49dVXcf/9VKBO9tnPflbN+T+uSczU\n+GWeT/TY/cR4lm1ThBiaWD3dr5FTR8NjC7pbyREMv/iCEvaNrFiV8eXxy4Ijq0k/bKmgP5LS4bJ7\nFSOmL6b4nsiKyO++dbkRJ9st+O0uM1ZVBBCKUU4POZ2bF/nUiOxEbYy3nfxB+HxxSm2NgfURyacd\nNQ+JHIcJJx+PMGfR2LnlzpCL6bNpPIJopaHvGf/OZemmGLP5dD+p76EswSbNCgKXITCf+0Ej1/7T\nu5Xff+GSUhhOHkdw1WqFQaz5AmIJMk2pwHjIV+wMmZXkBHcND2wCXj/mwI9fM+GOVV5iG03uR/eT\n87huMR0UHhrlHEhtc7o+O4k4x0v1kKkOz3S1r7Uz1/pBJ2zgKR/Ufxsj6Ap1KxbTYCyo5uys8+SK\nOHCP5xYaEWYtwxjaSHt2z/BBnBg4raQx/JSe2kLr+LcGy1y8v/BOVFkqlMOvYZM494J/TSQbf+f4\nNxGT6DBzdQPVO043cY30g8mYy6f5jcC0O4QcFezo6EAxjQ5yxK+kpCQJQa4dbGhoGHUU3377bfBL\ncd26dQiQjhHXFc5ZI6ctRqmyXN/Hps0T74d1CfXt7TAeOkh5Mxc7PHqxRZZSqs1kjZxPE+tAlZWD\ni/w1qy0MkUCFTlF8RxLSMLTtiXOu5+CpxBMmHSPaojfg4DkjfrXLjQ8RIymn6VyJBcgxDRCVOBuT\nCIgJAoKAIDDfEVgo/WC0tEwxjfLzjNbUwrjrPeiIPE6RzpBDlajbm/rM2QmsKQwrAhkewOQ691tX\neLG70Yp3TpGDGSXiESKVKXKPDCZyckxrnw7Fmas2pZ5SPk8jAkwOU2YpURM3y6Q0UVMM/RjAGa9P\n6SLyegNFAzUGU/6sGTvbLHOxc3A/vnXh34hoRq+IZvKoVrGAdBYrreXkJJai1latHZJ2ztIXPO0d\nJK1nGnXm62JjAhtus9hcoNozUq2imCAgCIyNwLQ7hH/0R3+Eb3/72yqStW3bNpXr/9RTT42mfTY2\nNirCGe2SmIn0kUcewYsvvqhSRbmWcC5bjEZJNTMePaItjs6ZqjtK+8QpAqPjSGI0AsOhA4gsXsJi\nf6P7ZbqgahcDJESb4BCyMD0NvaGmIIQznZmlvTDNN5vVqkd9kRdP7nHhbZKquJlEhEm+6IqNNaZY\nP5GNO3i9WUejvDr4AhSJIqdTCGiuGGJpQBAQBGYJAgulH2RN3WhlFTGOdoF1euP5BTBSlDC8YRN0\n7BCGKDPDPHZZADNSl9KAZFOXmUriRwYfV1cGiTWbyM+aLESURkLqRIa2rCyoMkz8wThag8aRjJNZ\n8qzlMkYQsJB0hc1sQznJV7hDTjQRs+hQdJjKWKKjzmEiVuy8FZsLcX/B7bgzfxu8EZ/STmQm0+5I\nL477TuF3RFRjI5KZ1c5lWGanQAJFAwvM+XAZnIlNjS6zkxmKjwy0c4orWz+ltjbpm5UERi45myyH\nwRqMYoKAIJCMgI7+gK4sBJTc3uinEEWvJkOeMtH+U0kZ5frFbFtSymjKyQyU5qqjwvtxjVJorY/9\nApHVaxDLzUOs7BJtc+JxHEXlOsuklFFtB3qxRusWIZ6mHu9Cjwl9JEmRqXGaIKdXnmo34bWjDiwi\nFlJ2CukU02pcJ8qpVHwuCzmErJmYaG6qIWHKcT6tlUYdp2oznTLa19c31UvN+LjxvnMZNzLBjpwq\nw1GOdopmZ9tm4n44ZTQvj3RDxQSBGURgon4t9VIm2n/W9oPEHeBvaYauqxOW559D8L73IU4kcTHK\n+IkVUtH6BNbvM+B89+Xj03soWri70Yb71w2pTBatH3Rbw5RxwkzWEzQ8xc2SMjo14LTSicR+kB00\ndsq6Qj3kIHrJQQwrdnJmNp3IBqNDOOs7h3cH98BtcOGU/6ySyODIJBPO5OjdpGmbC44qLncszki+\nQnNEzVSPyKymJeSUctppOpN+MB0qsm6+InD5G3ia7nQyziCfcrL7T9NlZrUZdnEm9KPoh3eUJDr0\nVG9h2r0T/o/+wZiF+GNeLPn0+n5iAStOTs/l/VlzMJ8G0zoHjUqgfsw2UjbUExW4m2r/ntrnRGGz\nBWWU7plL8hbTES1MORWJEevUlLh+SOkUG5QjmkM6ih77/8/ed0DJUV1p386Tk7JGOYMCQQEhAQJk\nggUmJ2Nsg/FydjEcg3HYtb3+bdhdOA7serE5a3Mc8dokw5KTTVQABAIJgXLOYWY0mtix/u97PTVT\n3dOhuqd7pmfmXZ1SV1e9UPVVT7133733u2Ep8UYHEISIpsfV2pje1whoBDQCfYBApuNapuX74JYS\nd1lbC1NMAyb6wxEfP1I8n6yXwBlniquuDj6DCKVIsxhThff7sWJntzFq7sR2obL4yvpSuQBxhVgX\nVcJFzha/D2mT/N1YSRNfoD7aVwhQ8RoOt01uppCRlLkPW8NtipzmUCAxGR+VwJPLZ6nNrMuUGLQi\nBlxBOdJ2FLGIB2R547vyYv1rclLZTBnqroH7sUtZEWkNrHFXqRyIZn3aQA76u4wF+/0HkfKii+Sv\nGtbDYuRHpItpKchs4vMzmu3oT43AQEMgbwrhQAMqm/tRbpwtzWlzFDKPE6XoD7+Fm02zGN7MLRkO\nWKYcGC2NIuSfsAhdcrx4yozVyNRaOBzK5HwMyB/tLpJ3tjpl2qiAnFirNDXVA5Vd5kDMh5Jo3gLt\n1xz8rVZOKoijq/O3Omz2rT81AhoBjYBGwAYC9FIZOkxccBMNIhehD8RpTnit0OPFefiQGCVI4RA3\nNsW3yjHqCBYuDxyLNfudCw+Vd7YVy0vrysRbBJUTZGiUAELSyT7KXIb0MtHSfxBgCgm6fXKj2yjj\n/NpATBMyQkh10QYlDG7DweMJb6jGUwWrIJS8DlIZFmpH+dcbVggtinva94NDAfGJILyhNZJxhEuq\nTpe55SepVBbxjbIuN1NozTSF1sRq/GtujNLhlrlKBUms4LoK4hx3GT59QoW3BAqkFo1Af0dAK4R5\nfILMERiB24yzHm6jKZhHzUswoNB5l7+t3EcZl5FRTCFcT51gVQ2PTP5iGobYvfagRwXrpyObMa/p\npPHtwm1/g1sxwO2t6/Ldb4Nlj8pZcYfljtbIE0b5pRSDdj7lGJIVH2/3STX6LoVbabyUwbKpYxLj\nUdHfNQIaAY1A/hBQOXHhvmHARZQkaZ4PPxA/QyA4NoFoLjx+QtrOOUZxPNlxGKmPOkozXGHh5DYp\nhtL31DvFcsMZDinqmLmQtGzbYa/UIg6RXiRa+icC1VDwYrNQkwXBUMylR+FqejBwWLGVJrs7sox+\ndsi53U6HEb/47vEP5bWG5fLc0VdllG+EVMG99Jrhn7MdR8jrMOMR6yMW5voOHZLWyDJniSLEKYFF\n0iS1IZlOhY1YRS6sJ0u90e2G9AGNQB4R0AphHsFl0xEE2TsQK+dgegWmpEiRD81/2ZXifeM1tRmg\nbm6n+2gG4kQch4MJgUtKJUwXnjghecvUkQG42jhlb70bK6wOsj7bEjKRXr+oa+WMlUgUc6QpuppL\nwpiP9/iwFckYWO8WT2+Fi6fNxm1dQWwhzDGkrhl5rLDFC++TLkiVUBhJLObtXiS+iv6uEdAIaAQ0\nAj1EwCDLNhcmQZLmQlokpqQApbgimCHJjFIa0/TBBT16njS2IZwiEh2jqBSejIXJXXXFsnKTT86d\n2cVYzcVNjmchjAmsxwm2lv6PAK2IVVCouI1HagqS0wSQz5CupsyB6PGAiMgfTunSSUVrUeU8tdF6\nuKttn6w8vlp+vPuXcCWtVkohCWsWVJySNWBh5JVujDQp5TWbRmhlrPJUSAn+OeCANQQxkdrimA2S\nuk5PEdAKYU8RtFHfjO1zIg0HY/1SSWDJOcqaWPTon6XosUek/SqwrtpkHzWobGJzNB1HgmAooBAD\nefjiV2ZpVZsO98+jTW7EFrrErrUw/rqrS8PCzZRZcCfdWeeV97cXgaW0QujqQ6thb0s7LJcHG93Y\nBHGPLqkpN6Q0ccx4b1+a7k8joBHQCAxYBIzyCqUQKsUQq3auPbslPGWqul/X/n0SmjiJGbjT3n9t\nTUi4pMmFRhKcmXLJgoj85lWvVGDBbx7CGUwJQxncB6WwDguUo1GX6Sq0DBwESPrCf8j2q5S4WqSj\nUGRkLuSkhKLYFmmXTS1bpSUcnfckunPGI84umyGzSqfLtradEpKw7IaC+Mjhp+Xxw892VqkFYc3U\nkknKNXR88RipwL/qcIWy/HngHko30lwK3WMP+Y8oNlXmVdzetksmlYyX0d4RabvRlsW0EOkCGSCQ\n/s2cQWO6aGoEjGIELls8DhKW5ssG1kH/1deKDwqh9803JHBOd1eIhHXNgzSf0RoJYWoL9/ZtEqkC\n41tcYP/Q8qh7zm4wkYaQ86mnhLMkeyEr6diaoLyMeI+n3i+XKxccl2HlXUqjeYm99dnU7pAWpruI\ngBgHlkvmukrkZtpb16P70QhoBDQCAxWBCMIknGS7hmWQKZbcmzZ2KoQMm3Dv2C6hqdNs3z7dRycO\nDyoXUlaqQvzgRae2yAsflSjlb8mMVimyeKJwMZApLKqhMPJ9n+O5u+3r1gV7DwGSv5Qjtm9eBZja\n8Y9ENZS2cLv6vt9/SJpC4GbAPwoVuilIYk+ZUTJFzqhaIMGOVBUkkPm4ZYMcR/mG0DHZgNQXzUda\npD4QnbhVuMvhHlqKHIc+mQylrdY7SjGUzgTDaS6FzKrc0slMKLdDs+CcSNeuPj84EdAKYYE+dwbg\nB879jHhf/7t4V7wtsuzi7K6UPqHt7eKsOyqOxi5tlIM1kkMq5Yg5nnYd9ShXUrfLIUEoTSk8W9Ne\nB4lsPndqkzy7plz+9kmZnDMjainsq8GZEARJQHAENNNQWk2FsFYT06R9lrqARkAjoBGwjQBZs0eM\ngLvobgnNnCW+DZ8o11E13qARFT4BZTFRiqRkfdDaN7IqrLxZWGZMTViuOa1RXl1fBvbRMll2UlNM\n+gnmM2QogYOJ0xHqoGXwIEDCFxK/UMxPktbUBxug4DXK0WA9XIvDirzGRCXqntnFvbDUe6Z5SimP\nTHVS11AHpTEke0FYQ7XycPCobIRFcmfbXtkLltNSkMpMLp4gw5AjcYTHZFOF4oljVB7TSa6tjun6\n0+c1AokQ0AphIlTydaysHCPiQfhx8pWSXiK1YyR4+mLxrFohoWefFjdiEaHPIAl9JRL/zk/fgLUE\nVmdpLTSF8R1GVZXKfchj44dGz5WVeaTuWEApT/6u4ma1jD4vmNMsz31UJs9AMTwb7qNTkcqir5RC\n88LpHtsIUhpKY2v6FzXLjaELUlFY05sTDC0aAY2ARiAFAkxWL0xSjzLMr+t583WJXLhMkc3QC8W1\nc4eEJk+x5TrKbjhmDAfZjNNhSHtHhGA5iMuWndQsTyE04cn3K+QSLEAydtwqdB9lLHmJL/a4tYze\nHxwIqFyFiM2j0kYLIi1/AVgDyUYaiqReNKCyRtdMbqZlcapMlMWV0TlYY+g4XFB3CVNnbG7dJhuM\nLQpUUwklCylzMSYTLF3IkupFUuYtlZGuYUKrnxaNQF8goBXCXkSd8XwGqJIVwYydfvEiCncMnB6v\nDwHUbWIgOb177UcweQUkOP8024NqfHcO+KpztVZdExVVi3BgnTA0IFsP+RDUn/1gSkvhFfOaZP0e\nr7y5oRRWOocMgfso3Xk6mMMtvRbuLgkLyopc4nNHELsSkXKQHmjRCGgENAIagQQIgHUxPGp0VPE7\n+VTFMup7+y1pv+yKaDw83E/cuxDDNWlyVNtL0ESiQ0MxdoQxpjR2ZCPgOHX53OPy6sdl8r8rK+Wy\nuU3IudsVnsCRa/8xN0IWonlsE7Wpjw0+BGgRnFs+R904WUh3tO2OIYRpibRmFD5TCdKbU8tndwMy\nYkQUCU63E3EH3m9ap2IISZbz56PLxefwwo31NDm3enFcSf1VI5BfBLRCmF98u7cOhVDIOGpXqBRO\nmChOuC1EoAyGaeWDcuh+f7WKxTCQ+ylrQVsu0IGH4hRCtse8TowxPNRIm2TPZNbYgNS3uOVjJLhn\njIcXTdZABzUMFxjJPVC2DJk9posggL1R+cp3+opM7qoZsYjNsM82trli3JPMNnxI9uglnWnIDfcm\nsMqaJ/SnRkAjoBEYZAhw4ZOMog6MWbQSurdsETfcR0MzOybOcBtlmiSmZcpEhlfB9X+/AfKQaC0q\nhQxPeGtjiYpZJ9HMLIwlDA2gtIJRexe2aaMi3SyI0RL6/8GMgNXqZ+LAnIStIKchWU2r0QYrc5WE\nm6JWxBYobWEoenaE+Q+LHEVpi55RuUCVYV7FM0oXyAfN6+SZoy+DHd0j1W784JMILYujQYCjYwiT\nAKQPZ4yAVggzhqxnFQy8JHqkLCBGIzR9hjjhduPaB9a2niiEvBUMzGSCo39PZBiUy7KuPINkCIU+\nKgfjEgVng8BZCP6ntPgdyGnogU4LiyfIbwKBoHywo1h2Henqlyu7gRAHcbiY4sIWTW1VZDC8lr4W\nupwmYmXlEME5SnMzVsfxhfkZTSGNuhaNgEZAIzCYEDCw0EiFUNwgLTtlrrjXfYRFTLjDgTSN4jx4\nQOipEmauQptCUrAxQ0Ky/ZBTxYWb1Ti+jMZC3Hvbi+XTfT45b3ZzDJnZduQqJEENUxHVWJixzfr6\nUyNgIsCYPzPuz4n51vCK4VLVGvWiokWRQlZTuopSeSR5TR1iFHMhQ7zVcn7NEpVa4/3j8ARLIWQ8\nnVY6CYyoE1OU0qc0AvYR0AqhfaxyUtIoQtwayFwcUIZ6wuoZGTNWXNu3Suikk3t8XcxdSHEyZQWX\nVmGBNIVWQpKWhpAPqj5Bzj+znN3PUsRzMBdiKfJAhOCy6vcHVAoMa32GWK6HNZF97gUD6kNv1IAI\nJiynIA8VhYP6uCHJffKtbfXFfj2UQm6mmMQGFcURxCFS3dWiEdAIaAQGNgJGCbxhmDIJA0ho0iRx\nbdkk3rfflMDiM1WMIWPpHUjD5KiuQigF4g5tCscQxrzvb3DDAti1SjgF48pEsFyv2FwiT66ukDOm\ntcrMMdHs4VzEa0JeQ24H4UbK+ia5mM1udTGNgIojJAwkrDFJa8he2g6lsBF5Dun2aSaxp/JYHzyW\nFWpXDFtmq56ON7QFky5kEwGtENoEKlfFSAgTwkarnKmIZdN2GAqh+4PVSGTaLgYC+HMhjjZY8Y4c\nEQdIAQykqaA4Md7SBZJS5HGpZPbqC/5j0uCGFgz4ORZaAmePjQ7kJ49rFz/cTN/ZViKHjrtVHOLK\nLR7lFjQLgz2Vw0IXTlwoTe0RuMIKSA4iiuyg0K9bX59GQCOgEcgWATJlq5h5JKRn7sHAOUvF++rL\nKhWFdSHTdfCghDpYr+32VQJr3yQof1sPelUYglmPFkRaC8diwfD1T0ukrDgs42FRtAqVQ6amGAai\nGsYlkqxGi0YgWwQc8GMqRlwit2ykCUrkfv9BhMiUggX1aEw8Yzbt6ToagWwR0Aphtsj1sB7dZBwd\nhC0OKGHpEtbHd8fEv8jMKu6P1ig3HGPIkPgiWX03oGC6SDjTgsBqKJoRS7scPONltCUEpL7ZjWT3\ncOVBTsNcCZVD5pk6+wRMKiC0Hm7GJOB9uJl+uq9IuZXG90Wr5szaqEIZf64vv6sValyAF6vUxzoU\nad6fyfDal9em+9YIaAQ0ArlGgLlvXYyZx4ub7KNcyHTt3SOhE07sdB0VjDcqgf2EiRl1z8XKSchR\nuAteJC2I8bbKxGFB2QeL4ftwIR1X09SN3ZpkaQyFYGz4SIRGaCZSK3p6vzcRYA7F6SWTMZ0rl0Ph\nSjkGV9Tt7buw4N59vtWb16X7GnwIaIWwr565yy3gVFHioGsNXGcyEsYSnjATbjibxbd/n7RfcXVG\n1VMWRlJ7B2ILHa1gRUVqCgMursmEK7KmcMW1pswBSxhcPes9ytXUPJerTypQ0xFbOA2DvelWam2b\nLkQkGHhvGwK04TJUAU+kMUMKawU4gAVrxkhSSGm99ZD1DrrvmxOf7mf0EY2ARkAjULgIGOUVEq6t\nhRK4V10kCWachw9FXUdhMVQupThD5m2OOZnkJ2SDbleUEftgI9IlYTHSKicjxODRdyvl8fcqsKDY\nqlJXWM9zv7ndKVvbvUohrIQ1sRiLjzrmOx4l/b23ECADKrd2xCjuQc5DLRqB3kRAK4S9iXaSviLM\n2zRyFKxyzRm5kYZmnCBM+Ot7/BFVT1kNk/SR1WHmLjx0UIzRtbaru5wG3CENuOGE5MhxlyKRsV05\ng4JUDE230vhqcye0y3LEkXyCOES6mU4eEZJFUwpzFZhxpGTCSyW8122HokQMLFeC/FpeJF0eUhbr\nCpWqDX1OI6AR0Aj0BQIMkzAqjovjOPJFIPVScPEZ4nvphah3y6nzOi/JtW2rRJCuIlPmUS5Kjq4G\nY7bTDaUwSurFRsle/fmFjbJya4ms2lIsS2c2q2OdHVp2uJDY6o9Oh2gtPMEXa3G0FNW7GoG8I0D2\nUCqG6aTM3Z8SeKW7G32+rxHQCmFfPwH2zxyDNT5xgs4407hCuuEYoO72rH5XAud+Jud342psVMyj\nEQzqmUgFVlsZm7H7KMhjejnMjy6mn5nVotxLdxwtkne3+uSFtWUyojIso+AeNA7uRF6sLPcXoZss\n2VlNiWACFAm5QJDQdYznamuCgjBPLRoBjYBGoKAQCA8fIW6Sl9F1FGNJ8LSF4lm+HHl2p4qBlEqm\nOLkACRZSpqzIRPgmpOsnGUSZN5aWPwqVu8VgqX7uo3LkKqxSsdvzJrXLGCiQTK2USKgc7jrilJGI\nyqB3hhaNQG8jQJbTkd7hvd2t7m+QI6AVwgL6ARgeWIFoLcwkTyGunwnqGazvaIUSVJLZQJru9mnB\nch492u2a1KovrzWF0PVmBAhpGlpcMWxwKark9BQta9NGhWTqqLBs2OOQLbCybT1UIlV7w4phjvEn\nU0YEctpnbzXmB8mqPxhrWRyK7x5XL2vfvXXDuh+NgEag/yJAJQ+KnwO5BynhcRPENX6P+J55SoKL\nzoBiOCV6b2AkdSEEgrl3DVgTMxWyOE/Ee70BLM/7kN6I4xfdQK9ecFwCiG3/FF4jKzcXw1Lok2Un\nNSdVCptBaL29zStjawJJy2R6bbq8RkAjoBEoZAS0QlhAT8eoQLwF8vOpvIBIASHcbAjdbEgw41m1\nUgJLz7NRI8MiIJpxYrOKgYE7nEYhZPkhZWGVk2/X0a5VW2s7vbHPVV7mNOR2uBHEN5gsHMLn39aX\nyt8/Sa9AU2mcP6lNmDaikOVYaywLrPVaq7FyTgVZi0ZAI6AR6AsEIgiLcJKwDLGCfBkFzjhL3Igx\ndG/cIOFJk9UxdV0IVaBSqJhHs3hp8TVXg3EnbDjkQEOXywS9QhhXeMJov7y4rgwWw0pZjDhzxqQn\nkrYA47t9Ul0aklFY2MziUhI1q49pBDQCGoGCREArhAX2WBhUH5oyVZyHDomzDpY5mxJYuEi8f3sF\ncYiwEmbobmOzi5hiTDhM955uArKcyNChMYcZV0h3ngNG9OfWGugapGMK9sKX4bgObieChXThFDCp\npunzEHJWrdhSIk+AmIBKYRHcjJhHsRCFeSLrJdZqaF4nkzIzJVi8UFn29CP32fjr1981AhqB/oGA\ngRcQLX/uTRs7Lzg8fQYUwk/Fvf5jCc2e03lcMI659sFSOGZM17EM94YixppszlTsrEJX0UtPbZJ1\ne4rkzQ2lQkKaqSP8iEPsHpNNNtKjiEtsDTiR6D7UL9IcWe9V72sENAIaAbsIaIXQLlK9XC4yYgQS\n2DvByHbYVs/KSlhaJr4nH0fgg0v8yy5CbGGNrbpZFUJSeWddXfeqcA2KVwhZiLEck0dEk8k3tYVh\n0CxCTsEwGMeD0gjLFgdcqzBXVDjPBjm6EqUTuh+NHdIIkhpOHNxqxXhPvV9KvLh2i2LrxGSHmxEp\nkYVT2wouRnELUnUkErr10nVWi0ZAI6ARyDsCYKxmEnqV8xadMbVRcO4CxMC/IyRFo8JoirPpuESy\nYB4169Oixxy6hxtJbhY7vvDcSchxy1hCKobPI8Zw3JCAisGmFXF0XGpfFVfo92Aciyivl+rSPA9O\n5k3oT42ARkAj0EsIaIWwl4DOppsIFDoHXWwYjG9D2j93iTiCIfH+/VXxvfwiFEpa4uCas/hMiSDv\nYa8I3VwDcAkCUU4yKYfrZWWpIcGgIUhOkdAVkwM4B2ED1898UX0pbnRv5kEcA+IWpowg3XkxlEJT\nYBgVN8xtKzd5kXbDKRed3GyeKujPNiji2w93j9VxOpzSAD2xDjm+rML8XpxMadEIaAQ0AtkgEB4/\nXtxgFMUAoKqHp0wRZ32duDZvRN7boUox5AkVv37gAOINx3Wmp8i0v3IseHHbsM+bMD/uEOTWPefE\nFriStsGa6JLd9V557N0KmTk2JGdN757LlozQ3Oqake4C70I3vF+0aAQ0AhqBgYCAVggL+SlCoYsM\nGw72UZuDDkhpSEwTOO8CYbJfiueD1eJd8bZEysokcP6FHUpiHm8aCqELJDRkiosXKrhRJTX+TPfv\npViJLe3QKZlegcQ0JnMcXYCCIAjoCzlhdGJ3US/u1+t1SGVRqzz9Qbk8vLyLOe9UpMGYOab75KIv\nrj++T1phTVyt55gf0YV80vHn2oOOpAohFWc9QbKiqPc1AhqBbgjQdXT8BHFt3wa65KilLTRzpnhW\nLBff889I+1XX4EUSXYgiUZpKWj82e6WQ/U+Cd0pTm1N5o1gZm81ro8WPGz1CZtW2yxOrK6Sy2Cdz\nxsbGzpvluVi55YBXxSoy/65mIzWR0Z8aAY1Af0VAK4QF/uToUhMZOkyc5WUSOdYoYbpqIok9LYfJ\nhHWEG4QMbo7jjeJ95SXxvv1m1HqH4+FJUySMWMV8CJnkEqprPtwL3IIyFeaZGoqVXG6UViiEe2C5\noqWOq8iFJCQfuH5RI9xhowhsPOCTt5ETkXGIjKO8+JQmhU1/tbIlcz3lMxhWgbQeuH8tGgGNgEYg\nFQJcMGQqI45lFAPhDlzI9L3wnPheeVn8Fy7rtAoyLl65j1ZWpWoy5Tkf2Ed9HWNIMyx8e+rcne/o\n+Iq0Gl48NyDPrylWSiRjx8leGi9BDEeH4I7agDjFkUhpVAXiLi0aAY2ARqC/IqAVwn7w5Jhr0EFX\nGrjxGVAIDTceG5VCG66kKk8h6ivrYCAgDihQrr17xPPRGnHBHSeAJMEJ2UbygIujoQHhjU6J9DA1\nRgli/8gMd6zVidiQ7u6kXAkO9KFeYmUjXVTWqtyRjre5lOXwV6/VqPyMnz+9UaW+IMxUeAeCtCPt\nRT0suabwtvQkyURDf2oENAJWBFQYA9I/OA8ejB7GKpkfXixFTz/VLWm9c/9+MeiXP7znudnK4H0y\nBRZDKoXxHhDm9U0eEZbL5zXJa5+UyMYDlSpFRbLFrgAW/3ajrcMgn2HbXLhMpECabetPjYBGQCNQ\niAhohbAQn0qaa6KVzYnB045CaDZFym9TIhhUnftGiGflCnGXlkjo1Hnmqbx+OpphHUN8oTG6Fi5B\n+OlBue2JVJVEVEqL+Dbay6CYgHGTaRjoytN9bTe+Rv6+0xJYCkKdUl9Ivnp2AyImHfIEYlSYJNmU\nM6a1yOyxhelSal6jnc+mNgdW1LteKfAM0wqhHeB0GY3AIEUgUjNEnI2NnSEOgtyD/rPPEd9LL4iB\nc50kM1zIPHwIvp+TcoIUmZVJpnW0ya2sfIkIzIbD4+Hahcflna3F8syacpCLBeWcE1pUXsNEF9EO\nz5V2EI0xvIHxhQx70KIR0AhoBPoLAl2zt/5yxfo6FQIRpKdwcsU0nLlSRZa38JRpUM4QFL/mfVB7\njxMjByuvth4NrJSunTugJZVGYyPHjI0qh7Yq2ytU5ImAQpxbSJoR8nes1S31BUCkyRg7qqe0DpoT\nkPd3FCsG0+VwKzWlAiQIx0FMY5WzZrTKTKTK6G9CooZ0EoTW3GKjnLWdUmCk02VYEdH7GoH+iUB4\n+Ahx79kNhuaoAmUgRCK48HTxvPUGQgwqxBgyJHpjyIUbgXeL9NDDxIrSUKSSqCqJxqhzAdGPGGlk\nmoiRhVPaVJoivqP/uLxKuf6fP7s5qWLId/u2Qx4ZATfSEQgT0KIR0AhoBPoDAloh7A9PKdE1YiVV\nSopFbLiNJqrOY6EZJ4gTA6z3/fckXFsrBmM0cKy3xAABjXvPHglNnJi3LkkUUFESljJPSLn15K2j\nDBqm1TCqHIqcNrlNJUtOVf29bcXCjfToFFqHXQ63XDa3qbOdVPX76hznd3SlSifFcP1ts1gW05Xn\n+fFDg8gJFjdzs1NRl9EIaAQKCgHmzQ3Ba4ShDKZwwZIhDZ51ayVwzrnmYdIew1J4RMJYwExEXNZV\n0P4e38WMf+ZGZZBWw6a43IUMA1h2UrO0tDtUTPjj8PKYM86f8t3N+EK2x9hxvvO1aAQ0AhqBQkYg\n/WytkK9+kF+bUVSckdtoN7jg0xc8da64wfbG4H7nxg0wYGEWP3Fyt6J5O9DWKu5PP+ls3sFryrFS\nSksSY9m4+RHvsb8++rMnuUBfc9JwosBk96mESuM0xEya4oN1+LGVXnlrU2kMq+eYmhDcoLrKmeX1\np0ZAI6ARKGQEjIqKbmEEwZNOFt8z/yeu3buQemK8unxlRWxphrIYktD4CTm/JYYYDAdraDkWK7cm\n4G0rLTLkwjktWOjyyN/WlyIvrUct6o0dktgSeOS4S0JgxGabvjTv+ZzfjG5QI6AR0AhkgIBWCDMA\nq9CKRqqqxHXEXuL6ZNeu3HPgoiNIAOyB+6jr7bfE5Q9IaPoMpDDsg2VNaGjubduSXW7C4wYspeFR\noxOeiz9ItjlSi1PqmrGCC/03nRzBinGoDwnkOJHgKrMpxcUeWTKjBWyrXW6lnHhsAg26VghNlPSn\nRkAj0J8QCE2aDEVvf+cipwEW0tDceeJ583WRs5YgVcXErtsh8yhyFzIGMR8yrNKQ5taQHGiA+TCB\njEM84XULG0E445OX1pXDYyEgpyC90LAOJmxrFbKQHmvxwqOB3iqMe+/DwcR6YXpfI6AR0AhYEADp\nZF/bSCxXk2K3udl+om/mUCsBs2YLBo18C/PPBRAXl2/h/bQzhiKRBoMVVEGqh1yIc9UKiXy8TuT0\nRYjzK4Nv3gTbuQMz6d8Dl1f+9EI9JJZhnw48A04eEokTS778PYQZ2FFdLVIMN9sMpRluQgePOeU4\n8vIlE5fLhRSLrl75LbhByBOP247DTvn7xx756tLcxBkSM1oi+ZvLtyS6n3R9ThqBiVVpasuqtQ3+\n3VTQCqFFI9CPERjo46AjiLF0+w4x/Jb3znvvijDu/JLLxFdZiXz2weg4iHeUY+RIxBgOzTlTNscn\n9tMEK+FhuH42pnj31zc75P1tbvl0r0tG10RkMt5Ncyd1LeDF/9yGVRgyZgjoxbDeyncs303sK5/i\nhOdNdBzMrzLKdzm33hg3emPuNdDmknoczOdfWf9vu99YCJsyiJXjxLwIefgyqZPtoywH42dv9FMM\nRYYKbrwiwOt2QtlxpshLmMm9FS1cJJF2KBU7dojz8GEJgPEtUjsmkyZsleUAxXvxwzLZY+G9k6ku\ngXBw4ktdTSIwyTAshAQGlUOOyjZkaLFDauAudOS4W1kW46v4oJQ6HF41EOZ7jYW/hba4512M3BVN\n7V556G/e+EtT308a154Rk6mpEMb3k7DxHh5MdD/pmmxuDoor0eJIkoqceGnRCPR3BDIZa/rrOOio\nhufLzp0IX+hY8Jk2XXxUCJ97Rozrb1Dv2DBcOpVs3y4RjCGRHuQojDYU+39ZWVl0QRnXMATv/hDY\nQ0mQFUrgUVIMI+KZ00ROGOUCu7ULJGElsu9oRJac0Jow/cRuDFf7jkR12IoytxQjvj0cbEWS+whS\nENlf5Iq94tTfqOByl9InNwAAQABJREFUzM3JeJuiK77LB9Lci5hxMT6Tv7sU8KQ81RtzST0OpnwE\ng/5kv1EIB/2TSgEAXUdVgt8cWSqDsA5SqfG++YaK7wuQgdSTWNFIcVkFd4oKrlVCmGio9BfWg0n2\nOVDTeYjMpdzipbjYiYHQLTv3BWUvYhRNFtH4cvn6Tneka087nrDfDft8sqfek5CVkzEzdDM1SW7y\ndX26XY2ARkAjYAcBsmBHYPlzglRGCRZzAuedL76/PiGRHdvBABObi9B55Iha6DNItJYH4buf73wm\nrKe1sKmdcYHdO2L+QW4jq0Ly6sel8hiIZy45tUmseWnNWhwfuLWD1bQZ6Xr8frccgJMP48nJTloG\nFuV8KYfmNehPjYBGQCNgRUArhFY0+us+rGARuEw6j2LZMYcSmjYNSuHr4lm7VoLz5uew5cJoys1V\n5xQWQsa0pDqf6C4qER8SxGSBK8WmcNDvDakGaU4imTwioGJddhzprtQfOIYVauTLGgtCGi0aAY2A\nRqAQEIhU16i0SI66o+pySKAWAsmM443XRK64OvYSmcpoL9iqx46zvcAX24C9b4w/J3lMANbJQ3hv\nMjYwkVABvGJ+E2ILy+T/PiiXy8EGXY5jdoRjxa6j9GqJMiknUibttKPLaAQ0AhqBTBHQCmGmiBVo\n+UgVFMLjcJvMkZWQtxkBUUvo5FPF/e4qCY8dK4bV7c7rw6psV+68AoUl9WWlwcpx/LiQ9dQqEbgS\npVMSzZVis95mkL1QAmCby8DL0aze489kVk02/Nf3ymXd7iLkzYp1VeKEBF6wWLkujel/6ki/1Caw\nkMYUGuBfdu3aJa+++qp89atftX2n2dSx3bguqBEYgAhEhg0TV0M9BqKoMhWaOUu8B/eL538fFsep\np0rohJlddw0Xejdi6UNkI8UCaT7FC9ZqJqkv9roRQuBSC4Dx/fH9ed6sZnl1fZk882G5zJ0QZYqm\nR4YdobfsziMe5blRWRyWmvIIXEvtKZV22tdlNAI9RSCbMS2bOj29Tl3fPgKxs1379XTJQkOAChqS\n+OZamKvQgIsOLYW+V16Kbi+/KL5nn851VwXXnmvfXnEiYbJ1U2k5MrxSpozgVg3roQ9eTYXknjl/\ncrtS8Mh8F7/VQPe1Hmtqc2KS0t3KmCEc/b74qlWrhFsmkk2dTNrXZTUCAw0BA4txESSt7xRoWR5Y\nB0PnLBX3+6vFERcCgODCnHvJdPadYIdJ7aeP9qv8hYy5jhe+5y+c0yyzxvhl9Y5i+d8VlfL2JpDd\n+buXja9rfqdrKtmwtx70yMFGN+oiLVNiRxCziv7UCPQKAtmMadnU6ZWb0Z0oBFw/hPQHLDJhVzMD\ngXuDZZRBur3BMlqK5L0k+EjIMmo+QK6MOjEKeeBykiVZC0lYOgP2O9oNT50moZmzO7fwlKni/mS9\nMKGwIOWDA+QwXRtGK15HGiFDGO8lvq801TI+zd8CB+uUuGXQKsl7lJUwznLIoP107Gp0/6H1kHkR\nm/0uJJjH48JmcifYvQz2lYhcyG59aznGHjLmJX4bVRWWKWOKpMrX3HnuOBTCfQ1uXHv0k/vW7TAI\nd7JJwpzN/VThutPlb7TeJ58NCQ8yEcbR3n777Wp78MEHVVztrFmz5LrrrpOPP/5Y/a5IVvRv//Zv\ncuedd8rWrVtlxIgRcuWVV8qPf/xj+fnPfy6TJk2S2tramDpTp06VG264Qe6++2659957he+2JUuW\nZHJpuuwgRWCwjYP0SnFgnHB0kGiRsCTAv2MQn3nWr5MQxiawpXT+Ghzw+jBA9GZnDOqslGCH45Md\n5k/qgeWI96suCyMmEOQz2JiM3hSe5zvxhNEBkMaE4Q7qlbXwyKDXRpHXEA9i8w3k/rUzDlIZbACx\nTV2zR+rhrsr8hkWwGoJPLK2QYIjjoJ1+0jaWooCdcTBF9YxO9cbci5hx7pXJ311GN2Ep3Bv3o8dB\nC+B6txsC6Wfu3aroA4WKgIHBkpsDbJpUhJRwMIXrYy6FfUQmTBDPyuXdm0V/wTPOkvDkKd3PDYQj\nYHpVyjYGCgODbDZSVcqUCVF2VU4e1u/xZdNMr9eZMCwogRAYW7ElkjU7imTGKD9cqSwzokQF+8kx\nKn0rV66UbciLeQxpXW666Sa59dZblSJHl9FvfvOb8uKLL8oTTzyhytXU1KjzVBCXLl0qjz/+uNxz\nzz1y8cUXx9T52c9+Jqeccoo89dRTsn//fqU0fuMb3xAyG2rRCGgELAhgHAuPHCWu1lZxWFLgBPH3\n44QHB71WAqedjtQTHfkIEd+n4gknTU7r2m/ppce7phspG6JFbz8Wy6wLfV7EH04YGpTxcDV9a1Op\n/HV1BRRHh7hBWDOi0g1l0aXcUEdjcY4LhqmEC1V8B9Nd9Shy5BZ74XmC9odVhDNaJEvVhz6nETAR\n0OOgicTA/9QK4QB8xoavSMKja9Wd0XLnpFLIeLk0MXOZQBFYfKYItzihayn7HMji2rVTTUDCI0b2\n+DZpIZwJl6JUwonDhn1976rJpMvDysGZnkTW78VCAeYy1okQV8j7q9C6R0Xws5/9rFx++eXy0EMP\nKStw/P3MnTtXTjzxRHWYyuFbb70l999/vyxfvlyOJ1iMueuuu5SF8de//rWsWbNGrdzTm0ErhPHI\n6u8agSgC4doxKkawEw+3R/yfvUi8K97G9pb4P3dZlwII7xj3po0ShmU+H2EUndeQZGcILIFUAPc3\n4Brj0gvyfbhkRoucMa1FKYQRZ4lsOyhyFOH/r31Sqix+JK7hwhrbSff+pHLYCsthK4aQY61uWAsN\nWCzJUmpIMpKxJJetD2sEEiKgx8GEsAzIgzacDQbkfQ+amzLoAopA+zCT95IUxtzSjTRZIkTqbyfZ\nOwe4OLBi7Tp8qHNzHMSoDpp06zFnPQgRbAhdflJtXEUeNzTUuU0cwcG+8AgGOAn64/Jq+Z/XauQP\nyyvV5ydIedFfhQoaV0dvvPFGeeWVV2TmzJl4xAe63U4lkmWbsmzZMuUqymO0KHLCFi/33XefapNu\nSDfffLMw/1SicvH19HeNwKBFAOMWSWZiBMeCpy8WR0sr0iOtjzlFIhrXoUMCv8/Y4730jW6kE4YF\nEDPe/e+fl8D3PS2BjNM+ZUJIzprRKjeddUx91jW55Ok15SCkKVVEZHYvme8QxhzSrXRPnRtEYV7x\nJ/HmsNumLqcR0OPg4PkNaAvhIHnWRnW1hLCZ4t62NTpYYuDMpYSmzxDf889KEZjg4iWM2KnggoXx\nh/vnd8S0mHEt6gYQc2Jgc2CS3ylkYYUbYU+FujvJXUwphzLY0gzXXMSQWKUVZAXW+BXrud7Y/9IZ\nxyQMa6YSzINWbinBCnnsNfbGdeSqj/Xr1yuXz0cffVSuvfZamTdvnnIfZXxRorjhdri0vfnmm1JX\nV6eSGT/wwAOdsZ7WOs8995yKHTz//PNl3bp1cgR51HIVE5qre9ftaAQKDYFIVbWoFWzLO9ZAPGHg\njDPFi3QURkmZhCdO7LpseMQwtVAEeXRznbi+q5Pke3TjHA830d11XmmHg44dIYNzLdJUNLc75OkP\nK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X3Cxi0HqQAy7rG+OaqtHoMlsRIpM7ywHpIIrtgbxr6lgt7tewT0ODjgx0EH/MCTO5v3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\nymIm7KW0aB6DG6kpj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qikzDS4/YhH3t5UojbznCFFMm5IUC46Of/kdGafvf2px8HCHQdt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\noMfBwh0HHZgodPnmFPCv8cCBA7avjhOnI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71Oc3PzUE5R1bHZbNa6tbS0VLV/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\nAAEEEEi1wP8DXCKwo49UceMAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 900 -h 363 -u px\n",
    "grid.arrange(plotty(female_fit, \"Female\"), plotty(male_fit, \"Male\"),ncol=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As these plots show, the Compas score treats a High risk women the same as a Medium risk man."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}