When I was preparing to teach my Fall course, I was concerned about AI cheaters, and whether my lazy approach to getting students to do the reading would be totally outdated.  I came up with a “AI statement” for my syllabus that said students can use AI, but they have to tell me how they used it, and they have to take responsibility for the text they turn in, even if they used an AI in the process of generating it. Now that the fall quarter has come and gone, it seems like a good time to reflect on things.  On third of the UW School of Public Health courses last fall had AI statemen ..read more

Last June, I happened to attend an ACM Tech Talk about LLMs in Intro Programing which left me very optimistic about the prospects of AI-assisted programming for my Introduction to Epidemic Modeling course. I read the book that the tech talk speakers were writing and decided that it was not really what my epi students needed. But it left me hopeful that someone is working on that book, too. In case no one writes it soon, I’ve also been trying to teach myself how to use AI to do disease modeling and data science tasks. I just wrapped up my disease modeling course for the quarter, though, and I d ..read more

Over the last year, I’ve been hard at work making simulated data. I love making simulated data, and I finally put a minimal blog about it up (https://healthyalgorithms.com/2023/11/19/introducing-pseudopeople-simulated-person-data-in-python/) I have a persistent challenge when I use pseudopeople in my work: configuring the noise requires a deeply nested python dictionary, and I can never remember what goes in it. After reading a recent dispatch from Simon Willison, I thought that maybe the new “GPTs” affordances from OpenAI could help me deal with this. I’m very optimistic about the potential o ..read more

I’m still settling back into blogging as a custom, so perhaps that is why it has taken me six months to think of announcing our new python package here! Without further ado, let me introduce you to pseudopeople. It is a Python package that generates realistic simulated data about a fictional United States population, designed for use in testing entity resolution methods or other data science algorithms at scale. To see it for yourself, here is a three-line quickstart, suitable for using in a Google Colab or a Jupyter Notebook: !pip install pseudopeople import pseudopeople as psp psp.genera ..read more

Barbara Muffoletto and I figured out how to export verbal autopsy data from KoboToolbox in a format suitable for running through SmartVA-Analyze. It was not too hard, but it was not too easy, either! She recorded a 4.5 minute video of how to do it, so that it will be easier for others in the future, which I share with you here: I hope everyone who needs this finds it ..read more

I haven’t seen a question like this before today. I wonder what the answers have been like ..read more

does this blog still work ..read more

I’ve been appreciating the Python debugger lately, and I want everyone who does data science work in Python to have a chance to appreciate it, too. When I went looking for a good place to refer colleagues who want to learn this tool, I decided this extensive tutorial might be best: https://realpython.com/python-debugging-pdb/ But if you have something else you think they could use, please let me know. I thought that there was a Software Carpentry lesson on this as well, but I’m not sure where the definitive source is (perhaps this is close?), and I think Wes McKinney’s book has a very practica ..read more

A paper I helped with is now in print, Comfort et al, Association Between Subnational Vaccine Coverage, Migration, and Incident Cases of Measles, Mumps, and Rubella in Iraq, 2001–2016. Figure 1. (A) Measles incidence per 100,000 persons in Iraq by governorate, 2001–2016 It is a good chance to test out a new python package for regression modeling that I have been excited about, the BAyesian Model-Building Interface (Bambi). In the past, it has sometimes been too much work to include random effects in a regression model in Python. The heart of the methods section in this paper, for exampl ..read more

I have had my eye on a python package called Bambi for a while now, because I often need a regression model that is a little more complicated that sklearn.linear_model.LinearRegression but not complicated enough to make a whole new PyMC model. Here is a minimal example (adapted from Awesome Open Source): import bambi as bmb import numpy as np, pandas as pd, arviz as az data = pd.DataFrame({ "y": np.random.normal(size=50), "x1": np.random.normal(size=50), "x2": np.random.normal(size=50) }) model = bmb.Model("y ~ x1 + x2", data) results = model.fit() az.summary(results) One cool ..read more