Putting the science back in data science

Advanced computation for Data Science, Southern Connecticut University


Professor at the U. Granada

Sebastian Cobb, monorail scientist

Programming since 1983 github.com/JJ

García-Sánchez P, Velez-Estevez A, Julián Merelo J, Cobo MJ (2021)

The Simpsons did it: Exploring the film trope space and its large scale structure.

PLoS ONE 16(3): e0248881.

our paper

In the beginning, we had tropes

From tvtropes.org

previously on

A trope is a repeated motif or pattern in fiction

From Tropes

... through average reviews ...

... to predicted cohesiveness and quality

Train a neural net to predict rating from tropes

Use it to optimize "trope bag"

García‐Ortega, RH, García‐Sánchez, P, Merelo‐Guervós, JJ. StarTroper, a film trope rating optimizer using machine learning and evolutionary algorithms. Expert Systems. 2020; 37:e12525. https://doi.org/10.1111/exsy.12525

Houston, we have a problem, taken from

Science has a



Of course, there's also COVID-19

Data science has solved that problem

as done in the industry


Because software development had a problem

And it was solved using the agile mindset

Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan

Data science in industry works because it's agile

Let's make science Agile

I never remember where's the paper accepted in a conference.

I use GitHub search now

First hypothesis

Open over closed

Interaction with the work via pull requests

Also issues, forks...

Free vulnerability scanning

A call from Hollywood

It should be easy to respond to evolving requirements

Second hypothesis

Stakeholder collaboration over vertical chains-of-command

Where are all my tropes?

García-Ortega, R.H., García-Sánchez, P., & Guervós, J.J. (2020). Tropes in films: an initial analysis. ArXiv, abs/2006.05380.

Sources change

Third hypothesis

Testing at all levels over hypotheses proved once

I needed to change \ref\cite

Easy, amirite?

✓ It was open

✓ It was developed using issues

✓ It was tested

Dude, where's my data?

import pandas as pd
import pygraphviz as pgv
from IPython.display import display, Latex
from scipy import stats

DBTROPES_GENERATED_FILE_PATH = '/Users/phd/Downloads/dbtropes/dbtropes-20160701.nt'
TROPESCRAPER_GENERATED_FILE_PATH = '/Users/phd/workspace/made/tropescraper/bin/tvtropes.json'

4th, last, hypothesis

Reproducibility and replicability over publishability

Doing science in XXI

Just like it used to be

We need to prove those hypotheses

Science, heal itself

➀ The product is a workflow

Papers and reports are side effects

☑ Reproducibility ☑ Openness ☑ Tests ☑ Collaboration

And opens up many possibilities

Dashboards, interactive charts, APIs...

Code the workflow

# tasks.py
def build_paper_latex_arxiv(context):
    print("Building latex file and figures through pweave ...")
    command = 'cd papers && pweave -f texminted tropescraper_arxiv.texw'
    run(command, hide=False, warn=True)

Using knitr/pweave/rmarkdown

We would need to find out first if this method really works....
<<generations.fs.table,echo=FALSE, results="asis">>=

generations <- read.csv("data/ng-spambase1-generations.csv")
ggplot(generations,aes(x=Generation,y=Average.F2,group=Generation,color="Average F2"))+geom_boxplot()+geom_boxplot(aes(x=Generation,y=Max.F2,group=Generation, color="Max F2", fill="Max F2"))+theme_tufte()
\caption{Boxplot of the best F2 (filled) and average F2 (clear, transparent) over 15 different runs for the spambase1 dataset partition.}

➁ You have the idea, you own the product

🔲 Reproducibility ☑ Openness ☑ Tests ☑ Collaboration

Doctoral student owning his thesis

kanban board for Ruben's thesis

➂ Use common software development tools and practices

☑ Reproducibility ☑ Openness ☑ Tests ☑ Collaboration

As well as common (industrial) data science best practices

CI/CD workflows, MLFlow...

Avoid smells

    runs-on: ubuntu-18.04
    name: Python source lint
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
          python-version: 3.7
      - name: Export neccesary variables
        run: |
          echo "::set-env name=PYTHONPATH::${{github.workspace}}"
          echo "::set-env name=KERAS_BACKEND::theano"
      - name: Install necessary tools
        run: pip install nox
      - name: Perform linting
        working-directory: ${{ github.workspace }}
        run: nox -e lint

Biggest hurdle?

Changing from pay-to-publish to pay-to-deploy-workflow

Join the Agile Science Manifesto

To make science more

✓ Open

✓ Adaptive

✓ Sustainable

Let's leave waterfalls for rainbows

Let's make science agile

Just like data science