Coding Sessions: Meeting Record

Meeting Record 1

Date: 2021-06-01

Participants: PMR, Shweata, Chaitanya, Sagar, Radhu

Key Points

• Changes were made to ami_gui.py and search_lib.py in order to add functionality to the pre-existing code.
• PMR talked about the importance of debugging and logging in python.
• Different development methodologies were discussed, pair programming being the primary one. Test-driven development was also discussed briefly.
• ami_gui.py was tested with different ami dictionaries (such as plant activity and plant compound). A python dictionary was created to store the data about hits/matches and where they occur in different ctrees and sections, which was then printed out. We used icecream, a python package to pretty-print the python dictionary we created earlier.
• The next step would be to store the data on hits in a .xml or .json format.

Immediate Tasks

• Radhu and others: Smoke test the latest ami_gui.py
• Chaitanya: Explore pygetpapers

Meeting Record 2

Date: 2021-06-07

Attendees: PMR, Kanishka, Radhu, Sagar, Shweata, Bhavini, Chaitanya

Key Points:

• For those interns, whose internship is about to come to an end, focus on your deliverables and thesis. Also, prepare a 2-3 minute video presentation explaining your miniproject /literature for interns who join us in the future(Keep it simple, so that it can be understood easily by people from all domains). "Your research is only as important as your documentation of it." -PMR
• Initiated the process of preparing a workflow for the machine learning miniproject which aims to classify text, develop labels and cluster similar scientific literature.
• Standard machine learning methods will be used as scientific literature is relatively much more structured as compared to some other text online, such as tweets(which might require deep learning models).Tools such as TF-IDF and count vectorizer might be potentially used for our purposes.
• Step 1 of workflow: Come up with a list of useful labels.
• TO DO: Improve documentation of pyami. Emphasis on debugging.
• Jupyter Notebooks can come in handy for the machine learning project. Merits: Ease of packaging.