CSH, or Computer Science House, is a fantastic community at RIT that I am fortunate enough to be a member of. We all have a passion for learning and teaching in regards to anything we're passionate about. We try to help each other develop as professionals and individuals with whatever we do.
In order to join the organization, a prospective member goes through our intro process and attempts to meet the requirements we set out for them. They are as follows:
- Obtain a certain percentage of signatures from our current upperclassmen, which implies that they have spoken to you and think you would be a good fit for the organization.
- Attend a certain number of our House Meetings or general meetings, where we go over important updates and discussions for the organization
- Attend a certain number of our Directorship Meetings, where we discuss updates for each of our executive board positions
- Attend a certain number of our Technical Seminars, where we provide information on some topic, ranging from programming tips to AI math
- Attend a certain number of our social events, where we just get together and do something social
Once the Introductory Evaluations meeting occurs, each voting member uses these metrics to inform their decision on whether or not to welcome the applicant into the organization. I thought it would be a cool idea to create something that could predict how the organization would end up voting, so I did. The name that we came up with for it is, The Evaluator.
It is simply a tertiary classification model which takes in an applicant's intro data, which are the numerical representations of the requirements described above, and says whether or not CSH is more likely to pass, fail, or conditional that applicant.
- A copy of the CSH databases was acquired in the form of a raw NoSQL dump. A local SQL database was created from that dump. Various SQL queries were made to get the appropriate features and results.
- Various design considerations were taken into account when trying to figure out what model / approach to use, but in the end I settled with a basic logistic regression model using the One Vs. All approach. (Of course, I'm still toying around with other possibilities)
- Testing was done to find the optimal hypothesis function, and eventually I settled on one that has decent prediction accuracy of around 83%.
- The data available for this project was about 200 entries, so a traditional split of 60/30/10 (or whatever standard you like) was not very feasible. Thus, attempts are being made to find what others have done in similar situations and how they solved this issue.
Want to see whether or not you'd make it into CSH? Follow these steps (If you have any questions about this, message me on Slack or email me!)
- Go ahead and download ai_vote.py.
- Make sure you have numpy and scipy installed in your working directory.
- Get your conditional data from conditional.csh.rit.edu, or ask an RTP to grab your info from the server.
- Now run the program! You can do this in one of two ways :
- File mode : Simply run the file with '-f' and then the filename afterwards
- Ex : python ai_vote.py -f example_file.csv
- For convenience sake, make your file a .csv with the columns in the following format :
name,signatures_missed,house_meetings_missed,directorship_meetings_attended,technical_seminars_attended,socials - socials is just a 1 or 0 for if you did or did not do any social events.
- If you're confused about the formatting, just check my example file, 'example_file.csv'
- Command line mode : Supply all of the conditional data through command line argument in the below format
- python ai_vote.py s_m h_m d_a t_a s
- s_m : number of signatures missed
- h_m : number of house meetings missed
- d_a : number of directorship meetings attended
- t_a : number of technical seminars attended
- s : whether or not you attended social events. Put 1 if you attended more 0 events, put 0 otherwise.
- Ex : python ai_vote.py 0 0 60 20 1
- python ai_vote.py s_m h_m d_a t_a s
- File mode : Simply run the file with '-f' and then the filename afterwards
-
To retrieve all House Meetings missed for the 2019 class
select uid, count(attendance_status) from member_hm_attendance where attendance_status = 'Absent' and meeting_id in (select id from house_meetings where date <= '10-27-19' and date >= '08-25-19') and uid in (select uid from freshman_eval_data where eval_date >= '2019-11-01 00:00:00') group by uid order by uid
-
To get evaluation results select uid, freshman_eval_result case when freshman_eval_result = 'Pending' then 2 when freshman_eval_result = 'Passed' then 1 else 0 end