Replicating Propublica’s COMPAS Audit#

Why COMPAS?#

Propublica started the COMPAS Debate with the article Machine Bias. With their article, they also released details of their methodology and their data and code. This presents a real data set that can be used for research on how data is used in a criminal justice setting without researchers having to perform their own requests for information, so it has been used and reused a lot of times.

import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
import seaborn as sns
import itertools
from sklearn.metrics import roc_curve
import warnings
warnings.filterwarnings('ignore')

propublica_data_url = 'https://github.com/propublica/compas-analysis/raw/master/compas-scores-two-years.csv'
df_pp = pd.read_csv(propublica_data_url,
                 header=0).set_index('id')

df_pp.head() # fist 5 rows 
# THIS is a comment, english in the code
name first last compas_screening_date sex dob age age_cat race juv_fel_count ... v_decile_score v_score_text v_screening_date in_custody out_custody priors_count.1 start end event two_year_recid
id
1 miguel hernandez miguel hernandez 2013-08-14 Male 1947-04-18 69 Greater than 45 Other 0 ... 1 Low 2013-08-14 2014-07-07 2014-07-14 0 0 327 0 0
3 kevon dixon kevon dixon 2013-01-27 Male 1982-01-22 34 25 - 45 African-American 0 ... 1 Low 2013-01-27 2013-01-26 2013-02-05 0 9 159 1 1
4 ed philo ed philo 2013-04-14 Male 1991-05-14 24 Less than 25 African-American 0 ... 3 Low 2013-04-14 2013-06-16 2013-06-16 4 0 63 0 1
5 marcu brown marcu brown 2013-01-13 Male 1993-01-21 23 Less than 25 African-American 0 ... 6 Medium 2013-01-13 NaN NaN 1 0 1174 0 0
6 bouthy pierrelouis bouthy pierrelouis 2013-03-26 Male 1973-01-22 43 25 - 45 Other 0 ... 1 Low 2013-03-26 NaN NaN 2 0 1102 0 0

5 rows × 52 columns

df_pp.tail() # bottom 5 rows 
# put anything here bro
name first last compas_screening_date sex dob age age_cat race juv_fel_count ... v_decile_score v_score_text v_screening_date in_custody out_custody priors_count.1 start end event two_year_recid
id
10996 steven butler steven butler 2013-11-23 Male 1992-07-17 23 Less than 25 African-American 0 ... 5 Medium 2013-11-23 2013-11-22 2013-11-24 0 1 860 0 0
10997 malcolm simmons malcolm simmons 2014-02-01 Male 1993-03-25 23 Less than 25 African-American 0 ... 5 Medium 2014-02-01 2014-01-31 2014-02-02 0 1 790 0 0
10999 winston gregory winston gregory 2014-01-14 Male 1958-10-01 57 Greater than 45 Other 0 ... 1 Low 2014-01-14 2014-01-13 2014-01-14 0 0 808 0 0
11000 farrah jean farrah jean 2014-03-09 Female 1982-11-17 33 25 - 45 African-American 0 ... 2 Low 2014-03-09 2014-03-08 2014-03-09 3 0 754 0 0
11001 florencia sanmartin florencia sanmartin 2014-06-30 Female 1992-12-18 23 Less than 25 Hispanic 0 ... 4 Low 2014-06-30 2015-03-15 2015-03-15 2 0 258 0 1

5 rows × 52 columns

WE can GET HELP from holding SHIFT + Tab inside perenthesis (in code)#

df_pp.shape

(7214, 52)

df_pp.head

<bound method NDFrame.head of                       name      first         last compas_screening_date  \
id                                                                         
1         miguel hernandez     miguel    hernandez            2013-08-14   
3              kevon dixon      kevon        dixon            2013-01-27   
4                 ed philo         ed        philo            2013-04-14   
5              marcu brown      marcu        brown            2013-01-13   
6       bouthy pierrelouis     bouthy  pierrelouis            2013-03-26   
...                    ...        ...          ...                   ...   
10996        steven butler     steven       butler            2013-11-23   
10997      malcolm simmons    malcolm      simmons            2014-02-01   
10999      winston gregory    winston      gregory            2014-01-14   
11000          farrah jean     farrah         jean            2014-03-09   
11001  florencia sanmartin  florencia    sanmartin            2014-06-30   

          sex         dob  age          age_cat              race  \
id                                                                  
1        Male  1947-04-18   69  Greater than 45             Other   
3        Male  1982-01-22   34          25 - 45  African-American   
4        Male  1991-05-14   24     Less than 25  African-American   
5        Male  1993-01-21   23     Less than 25  African-American   
6        Male  1973-01-22   43          25 - 45             Other   
...       ...         ...  ...              ...               ...   
10996    Male  1992-07-17   23     Less than 25  African-American   
10997    Male  1993-03-25   23     Less than 25  African-American   
10999    Male  1958-10-01   57  Greater than 45             Other   
11000  Female  1982-11-17   33          25 - 45  African-American   
11001  Female  1992-12-18   23     Less than 25          Hispanic   

       juv_fel_count  ...  v_decile_score  v_score_text  v_screening_date  \
id                    ...                                                   
1                  0  ...               1           Low        2013-08-14   
3                  0  ...               1           Low        2013-01-27   
4                  0  ...               3           Low        2013-04-14   
5                  0  ...               6        Medium        2013-01-13   
6                  0  ...               1           Low        2013-03-26   
...              ...  ...             ...           ...               ...   
10996              0  ...               5        Medium        2013-11-23   
10997              0  ...               5        Medium        2014-02-01   
10999              0  ...               1           Low        2014-01-14   
11000              0  ...               2           Low        2014-03-09   
11001              0  ...               4           Low        2014-06-30   

       in_custody  out_custody priors_count.1 start   end event two_year_recid  
id                                                                              
1      2014-07-07   2014-07-14              0     0   327     0              0  
3      2013-01-26   2013-02-05              0     9   159     1              1  
4      2013-06-16   2013-06-16              4     0    63     0              1  
5             NaN          NaN              1     0  1174     0              0  
6             NaN          NaN              2     0  1102     0              0  
...           ...          ...            ...   ...   ...   ...            ...  
10996  2013-11-22   2013-11-24              0     1   860     0              0  
10997  2014-01-31   2014-02-02              0     1   790     0              0  
10999  2014-01-13   2014-01-14              0     0   808     0              0  
11000  2014-03-08   2014-03-09              3     0   754     0              0  
11001  2015-03-15   2015-03-15              2     0   258     0              1  

[7214 rows x 52 columns]>

Data Cleaning#

clean_data_url = 'https://raw.githubusercontent.com/ml4sts/outreach-compas/main/data/compas_c.csv'

df= pd.read_csv(clean_data_url,
                 header= 0).set_index('id')
# *changed the "Propublicia to "clean", that changed the URL...to what I need

df_pp.shape
# first graph

(7214, 52)

df.shape
#second graph shape

# ^ look :0 the second graph is smaller = less people in second graph

(5278, 14)

race_counts = df['race'].value_counts()

  • age: defendant’s age

  • c_charge_degree: degree charged (Misdemeanor of Felony)

  • race: defendant’s race

  • age_cat: defendant’s age quantized in “less than 25”, “25-45”, or “over 45”

  • score_text: COMPAS score: ‘low’(1 to 5), ‘medium’ (5 to 7), and ‘high’ (8 to 10).

  • sex: defendant’s gender

  • priors_count: number of prior charges

  • days_b_screening_arrest: number of days between charge date and arrest where defendant was screened for compas score

  • decile_score: COMPAS score from 1 to 10 (low risk to high risk)

  • is_recid: if the defendant recidivized

  • two_year_recid: if the defendant within two years

  • c_jail_in: date defendant was imprisoned

  • c_jail_out: date defendant was released from jail

  • length_of_stay: length of jail stay

race_counts
# press tab when typing variable to auto-complete

race
African-American    3175
Caucasian           2103
Name: count, dtype: int64

race_counts.plot(kind= 'pie')

<Axes: ylabel='count'>
_images/7ea4332f7a2e802ad51523c23d6c9d4f348833ed3b7d3b0b1a3b960437a6a063.png