Demetri Pananos

Demetri Pananos

Data Scientist at Royal Bank of Canada. Part time PhD Candidate in Biostatsitics.

GSoC 2019: Testing an API

1 minute read

Published:

This is a really short update. Here are a couple things I have been working on since the last blog post.

1) Continuing to write the API so that it is intuitive

2) Writing unit tests

3) Benchmarking

When I last made a blog post about the API, users were to pass the ode parameters and initial conditions as a single vector. That was just to get things off the ground. Now that API is working, I can go back and make that a little more user friendly. The ODEModel (the meat and potatoes of the API) inherits from the theano.Op class, so in the make_node method I changed


    def make_node(self, x):
        x = theano.tensor.as_tensor_variable(x)

        return theano.Apply(self, [x], [x.type()])

to

    def make_node(self, odeparams, initial_condition):

        if np.ndim(odeparams)>1:
            odeparams = np.ravel(odeparams)
        if np.ndim(initial_condition) > 1:
            odeparams = np.ravel(initial_condition)


        odeparams = tt.as_tensor_variable(odeparams)
        initial_condition = tt.as_tensor_variable(initial_condition)
        x = tt.concatenate([odeparams,initial_condition])

        return theano.Apply(self, [x], [x.type()])

The little bit of logic at the beginning is to get around some shape problems with theano. So now to use the API, instead of doing

  all_params = pm.math.stack([<parameters here>, <initial conditions here>])
  forward = ode_model(all_params)

you can do


  forward = ode_model(odeparams = [<parameters here>], initial_condition = [<initial conditions here>])

which I think is much better. Explicit is better than implicit. This change actually makes some models run much faster which is interesting.

You May Also Enjoy

Gym Data

4 minute read

Published:

Prior to the pandemic, my school’s gym would tweet out how many people were in the weight room at a given time. This was useful for an instantaneous understanding of how busy the gym was, but was not useful if you wanted to answer how busy the gym might be later. During my PhD, I managed to create a little python script to scrape some tweets, extract the number of people in the weight room, and model the data with a linear model. The model did OK, but I wanted to take it a step further and estimate the effects of days of weeks, months, special events, and even weather (because, as the gym’s manager told me when I met with her, the weather is a major factor in people going to the gym).

In Response To Vaccine Hesitancy From Tommy Caldwell

9 minute read

Published:

On September 21st, 2021 I received an email from Tommy Caldwell, author and owner of Hybrid Fitness in my hometown of London Ontario, with the subject line “COVID-19, Kids, and Vaccines: A difficult decision for parents to navigate”. The email was sent from Hybrid Fitness’ email address (which I had subscribed to sometime in 2018, in the first year of my PhD). In that email, Mr. Caldwell acknowledges that vaccinating is a “no-brainer decision” and professes he is an inexpert in COVID19, science, and research, yet continues on to interpret statistics from a clinical trial intended to evaluate the vaccine against COVID19 in persons aged 12-15 years.

Three Months Into Industry

6 minute read

Published:

I “left” (I say left in quotations because I’m still working on my Ph.D part time) approximately 3 months ago. That is a bit of a milestone. It is a quarter of a year working at a senior-ish level as a data scientist at a national bank. I know a lot of PhDs, especially in quantitative disciplines, are thinking of making the jump from academia to industry. This sequence of blog posts is not advice on how to make that jump, but rather to document one perspective on what that change entails.