Cox with interaction term and independent variables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# filter dplyr warnings\n",
    "%load_ext rpy2.ipython\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"cox\">Cox with interaction term</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 8775\n",
       "Call:\n",
       "coxph(formula = f, data = data_filtered)\n",
       "\n",
       "  n= 11182, number of events= 2988 \n",
       "\n",
       "                                              coef exp(coef)  se(coef)      z\n",
       "race_factorAfrican-American               0.244810  1.277378  0.088817  2.756\n",
       "race_factorAsian                         -1.096574  0.334014  0.651876 -1.682\n",
       "race_factorHispanic                       0.056236  1.057847  0.143650  0.391\n",
       "race_factorNative American               -5.041392  0.006465  2.465844 -2.044\n",
       "race_factorOther                         -0.191859  0.825423  0.167680 -1.144\n",
       "decile_score                              0.163530  1.177661  0.013044 12.537\n",
       "age_factorGreater than 45                -0.239126  0.787316  0.055567 -4.303\n",
       "age_factorLess than 25                    0.265289  1.303807  0.046989  5.646\n",
       "gender_factorFemale                      -0.400712  0.669843  0.052588 -7.620\n",
       "priors_count                              0.060688  1.062567  0.003777 16.067\n",
       "crime_factorM                            -0.067832  0.934417  0.040851 -1.660\n",
       "race_factorAfrican-American:decile_score -0.042704  0.958195  0.015104 -2.827\n",
       "race_factorAsian:decile_score             0.207554  1.230664  0.105559  1.966\n",
       "race_factorHispanic:decile_score         -0.044443  0.956530  0.027928 -1.591\n",
       "race_factorNative American:decile_score   0.648801  1.913245  0.286302  2.266\n",
       "race_factorOther:decile_score             0.041924  1.042815  0.034548  1.213\n",
       "                                         Pr(>|z|)    \n",
       "race_factorAfrican-American               0.00585 ** \n",
       "race_factorAsian                          0.09253 .  \n",
       "race_factorHispanic                       0.69544    \n",
       "race_factorNative American                0.04091 *  \n",
       "race_factorOther                          0.25254    \n",
       "decile_score                              < 2e-16 ***\n",
       "age_factorGreater than 45                1.68e-05 ***\n",
       "age_factorLess than 25                   1.64e-08 ***\n",
       "gender_factorFemale                      2.54e-14 ***\n",
       "priors_count                              < 2e-16 ***\n",
       "crime_factorM                             0.09682 .  \n",
       "race_factorAfrican-American:decile_score  0.00469 ** \n",
       "race_factorAsian:decile_score             0.04927 *  \n",
       "race_factorHispanic:decile_score          0.11154    \n",
       "race_factorNative American:decile_score   0.02344 *  \n",
       "race_factorOther:decile_score             0.22494    \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               1.277378     0.7829 1.073e+00\n",
       "race_factorAsian                          0.334014     2.9939 9.309e-02\n",
       "race_factorHispanic                       1.057847     0.9453 7.983e-01\n",
       "race_factorNative American                0.006465   154.6852 5.148e-05\n",
       "race_factorOther                          0.825423     1.2115 5.942e-01\n",
       "decile_score                              1.177661     0.8491 1.148e+00\n",
       "age_factorGreater than 45                 0.787316     1.2701 7.061e-01\n",
       "age_factorLess than 25                    1.303807     0.7670 1.189e+00\n",
       "gender_factorFemale                       0.669843     1.4929 6.042e-01\n",
       "priors_count                              1.062567     0.9411 1.055e+00\n",
       "crime_factorM                             0.934417     1.0702 8.625e-01\n",
       "race_factorAfrican-American:decile_score  0.958195     1.0436 9.302e-01\n",
       "race_factorAsian:decile_score             1.230664     0.8126 1.001e+00\n",
       "race_factorHispanic:decile_score          0.956530     1.0454 9.056e-01\n",
       "race_factorNative American:decile_score   1.913245     0.5227 1.092e+00\n",
       "race_factorOther:decile_score             1.042815     0.9589 9.745e-01\n",
       "                                         upper .95\n",
       "race_factorAfrican-American                 1.5203\n",
       "race_factorAsian                            1.1985\n",
       "race_factorHispanic                         1.4018\n",
       "race_factorNative American                  0.8119\n",
       "race_factorOther                            1.1466\n",
       "decile_score                                1.2082\n",
       "age_factorGreater than 45                   0.8779\n",
       "age_factorLess than 25                      1.4296\n",
       "gender_factorFemale                         0.7426\n",
       "priors_count                                1.0705\n",
       "crime_factorM                               1.0123\n",
       "race_factorAfrican-American:decile_score    0.9870\n",
       "race_factorAsian:decile_score               1.5135\n",
       "race_factorHispanic:decile_score            1.0103\n",
       "race_factorNative American:decile_score     3.3533\n",
       "race_factorOther:decile_score               1.1159\n",
       "\n",
       "Concordance= 0.697  (se = 0.005 )\n",
       "Rsquare= 0.122   (max possible= 0.99 )\n",
       "Likelihood ratio test= 1461  on 16 df,   p=0\n",
       "Wald test            = 1555  on 16 df,   p=0\n",
       "Score (logrank) test = 1740  on 16 df,   p=0\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(survival)\n",
    "library(ggfortify)\n",
    "library(dplyr)\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",
    "\n",
    "grp <- data[!duplicated(data$id),]\n",
    "nrow(grp)\n",
    "f <- Surv(start, end, event, type=\"counting\") ~ race_factor * decile_score + age_factor + gender_factor +\n",
    "                                                 priors_count + crime_factor\n",
    "felonies <- c('(F1)','(F2)', '(F3)','(F5)','(F6)','(F7)')\n",
    "data_filtered <-  mutate(data, crime_factor = factor(ifelse(c_charge_degree %in% felonies, 'F', 'M'))) %>%\n",
    "                  mutate(age_factor = as.factor(age_cat)) %>%\n",
    "                  within(age_factor <- relevel(age_factor, ref = 1)) %>%\n",
    "                  mutate(gender_factor = factor(sex, labels= c(\"Female\",\"Male\"))) %>%\n",
    "                  within(gender_factor <- relevel(gender_factor, ref = 2)) %>%\n",
    "                  filter(days_b_screening_arrest <= 30) %>%\n",
    "                  filter(days_b_screening_arrest >= -30) %>%\n",
    "                  filter(is_recid != -1)\n",
    "grp <- data_filtered[!duplicated(data_filtered$id),]\n",
    "print(nrow(grp))\n",
    "model <- coxph(f, data=data_filtered)\n",
    "print(summary(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   decile_score black_vs_white_hazard_ratio\n",
       "1             1                   1.2239777\n",
       "2             2                   1.1728093\n",
       "3             3                   1.1237800\n",
       "4             4                   1.0768003\n",
       "5             5                   1.0317847\n",
       "6             6                   0.9886509\n",
       "7             7                   0.9473203\n",
       "8             8                   0.9077176\n",
       "9             9                   0.8697704\n",
       "10           10                   0.8334096\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "d <- c(1:10)\n",
    "data.frame(decile_score=d, black_vs_white_hazard_ratio=exp(0.244810 - 0.042704 * d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"logistic\">Logistic regression without two year recidivism term</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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, family = \"binomial\", data = df)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-3.0121  -0.8145  -0.3530   0.8730   2.6669  \n",
       "\n",
       "Coefficients:\n",
       "                            Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)                 -1.26216    0.07299 -17.292  < 2e-16 ***\n",
       "gender_factorFemale          0.15947    0.07840   2.034 0.041955 *  \n",
       "age_factorGreater than 45   -1.44095    0.09880 -14.585  < 2e-16 ***\n",
       "age_factorLess than 25       1.39347    0.07461  18.678  < 2e-16 ***\n",
       "race_factorAfrican-American  0.47752    0.06857   6.964 3.31e-12 ***\n",
       "race_factorAsian            -0.33630    0.47811  -0.703 0.481813    \n",
       "race_factorHispanic         -0.44192    0.12702  -3.479 0.000503 ***\n",
       "race_factorNative American   1.25136    0.74518   1.679 0.093096 .  \n",
       "race_factorOther            -0.82409    0.16043  -5.137 2.79e-07 ***\n",
       "priors_count                 0.29502    0.01103  26.736  < 2e-16 ***\n",
       "crime_factorM               -0.33652    0.06575  -5.118 3.08e-07 ***\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: 6282.7  on 6161  degrees of freedom\n",
       "AIC: 6304.7\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "raw_data <- read.csv(\"./compas-scores-two-years.csv\")\n",
    "\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",
    "\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, family=\"binomial\", data=df)\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 1.420297\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "control <- exp(-1.26216) / (1 + exp(-1.26216))\n",
    "exp(0.47752) / (1 - control + (control * exp(0.47752)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   GVIF Df GVIF^(1/(2*Df))\n",
       "gender_factor  1.030437  1        1.015104\n",
       "age_factor     1.195754  2        1.045708\n",
       "race_factor    1.029395  5        1.002901\n",
       "priors_count   1.235550  1        1.111553\n",
       "crime_factor   1.015181  1        1.007562\n",
       "two_year_recid 1.046079  1        1.022780\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(car)\n",
    "vif(model <- glm(score_factor ~ gender_factor + age_factor + race_factor +\n",
    "                            priors_count + crime_factor + two_year_recid, family=\"binomial\", data=df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R -w 900 -h 900 -u px\n",
    "library(pROC)\n",
    "raw_data <- read.csv(\"./compas-scores-two-years.csv\")\n",
    "raw_data <- mutate(raw_data, score_factor = factor(score_text != \"Low\", labels = c(\"LowScore\",\"HighScore\")))\n",
    "w <- filter(raw_data, race==\"Caucasian\")\n",
    "wroc <- roc(two_year_recid ~ decile_score, data=w)\n",
    "b <- filter(raw_data, race==\"African-American\")\n",
    "broc <- roc(two_year_recid ~ decile_score, data=b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"roc-curves\">Unsmoothed ROC Curves</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "roc.formula(formula = two_year_recid ~ decile_score, data = b)\n",
       "\n",
       "Data: decile_score in 1795 controls (two_year_recid 0) < 1901 cases (two_year_recid 1).\n",
       "Area under the curve: 0.6918\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oW7euMxt4xx13uNk1z7RNGPRMKZLVEQJhsrg4GAEEEEAAAQQQQCAtBcxQSOas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7vO4jHjxo3TlClTVL58eV1xxRVKS0vLPQmuhMD/C3Tr1k35YrN5Dz74YMIm9nvvy3oittdgnZoy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\n+C4fWLmcN+ELEAbDt2NLBBBAA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ImJQggggAACCCCAAAIIIICA+wQI\nhO6bU0aEAAIIIIAAAggggAACCIQkQCAMiYlCCCCAAAIIIIAAAggggID7BAiE7ptTRoQAAggggAAC\nCCCAAAIIhCRAIAyJiUIIIIAAAggggAACCCCAgPsE/j/5hhjqhg+ErQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R  -w 900 -h 900 -u px\n",
    "library(gridExtra)\n",
    "w <- filter(raw_data, race==\"Caucasian\")\n",
    "wroc <- roc(two_year_recid ~ decile_score, data=w)\n",
    "b <- filter(raw_data, race==\"African-American\")\n",
    "broc <- roc(two_year_recid ~ decile_score, data=b)\n",
    "plot(wroc)\n",
    "plot(broc, add=TRUE, col=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "roc.formula(formula = two_year_recid ~ decile_score, data = b,     smooth = TRUE)\n",
       "\n",
       "Data: decile_score in 1795 controls (two_year_recid 0) < 1901 cases (two_year_recid 1).\n",
       "Smoothing: binormal \n",
       "Area under the curve: 0.6935\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Bd5LHDX9HAuDq1asVcraFu82JeyXTZ1SvzFl04z+2Mv4b/mxn33Gz+C0J4e+Wm2UUu1np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\nvRhZNEugE3xSwmCCO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8MZDAal+/6D8l3nbEtqJwxawkqhCCCAAAIIIIAAAgj4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\nyyIAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 900 -h 900 -u px\n",
    "wroc <- roc(two_year_recid ~ decile_score, data=w, smooth=TRUE)\n",
    "broc <- roc(two_year_recid ~ decile_score, data=b, smooth=TRUE)\n",
    "plot(wroc)\n",
    "plot(broc, col=\"red\", add=TRUE)"
   ]
  },
  {
   "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
}