Category: machine learning

Wordle with NLP for Data Scientists

Wordle with NLP for Data Scientists

I have played my fair share of Wordle.

I’m not necessarily good at it, but most days I get to solve the puzzle.

The experience is completely different with Semantle — a Wordle-inspired puzzle in which you also need to guess the word of the day.

Unlike in Wordle, Semantle gives you unlimited guesses though. And, boy, you will need many!

Like Wordle, Semantle gives you hints as to how close your guesses were to the secret word of the day.

However, where Wordle shows you how good your guesses were in terms of the letters used, Semantle evaluates the semantic similarity of your guesses to the secret word. For the 1000 most similar words to the secret word, it will show you its closeness like in the picture above.

This semantic similarity comes from the domain of Natural Language Processing NLP — and this basically reflects how often words are used in similar contexts in natural language.

For instance, the words “love” and “hate” may seem like opposites, but they will often score similarly in grammatical sentences. According to the semantle FAQ the actual opposite of “love” is probably something like “Arizona Diamondbacks”, or “carburetor”.

Another example is last day’s solution (15 March 2022), when the secret word was circle. The ten closest words you could have guessed include circles and semicircle, but more distinctive words such as corner and clockwise.

Further downfield you could have guessed relatively close words like saucer, dot, parabola, but I would not have expected words like outwaited, weaved, and zipped.

The creator of Semantle scored the semantic similarity for almost all words used in the English language, by training a so-called word2vec model based on a very large dataset of news articles (GoogleNews-vectors-negative300.bin from late 2021).

Now, every day, one word is randomly selected as the secret word, and you can try to guess which one it is. I usually give up after 300 to 400 guesses, but my record was 76 guesses for uncovering the secret word world.

Try it out yourself: https://semantle.novalis.org/

And do share your epic wins and fails!

Vox: Are We Automating Racism?

Vox: Are We Automating Racism?

In Glad You Asked, Vox dives deep into timely questions around the impact of systemic racism on our communities and in our daily lives.

In this video, they look into the role of tech in societal discrimination. People assume that tech and data are neutral, and we have turned to tech as a way to replace biased human decision-making. But as data-driven systems become a bigger and bigger part of our lives, we see more and more cases where they fail. And, more importantly, that they don’t fail on everyone equally.

Why do we think tech is neutral? How do algorithms become biased? And how can we fix these algorithms before they cause harm? Find out in this mini-doc:

Understanding Machine Learning (free e-book)

Understanding Machine Learning (free e-book)

Shai Shalev-Shwartz and Shai Ben-David of the Hebrew University of Jerusalem made their machine learning book free to download.

The book covers the basic foundations up to advanced theory and algorithms. I copied the table of contents below. It’s kind of math heavy, but well explained with visual examples and pseudo-code.

Moreover, the book contains multiple exercises for you to internalize the knowledge and skills.

As an added bonus, the professors teach a number of machine learning courses, the lecture slides and materials of which you can also access for free via the book’s website.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

About the book

If you want to reward the professors for their efforts, please do buy a hardcopy version of book.

Table of contents

Part I: Foundations

  • A gentle start
  • A formal learning model
  • Learning via uniform convergence
  • The bias-complexity trade-off
  • The VC-dimension
  • Non-uniform learnability
  • The runtime of learning

Part II: From Theory to Algorithms

  • Linear predictors
  • Boosting
  • Model selection and validation
  • Convex learning problems
  • Regularization and stability
  • Stochastic gradient descent
  • Support vector machines
  • Kernel methods
  • Multiclass, ranking, and complex prediction problems
  • Decision trees
  • Nearest neighbor
  • Neural networks

Part III: Additional Learning Models

  • Online learning
  • Clustering
  • Dimensionality reduction
  • Generative models
  • Feature selection and generation

Part IV: Advanced Theory

  • Rademacher complexities
  • Covering numbers
  • Proof of the fundamental theorem of learning theory
  • Multiclass learnability
  • Compression bounds
  • PAC-Bayes

Appendices

  • Technical lemmas
  • Measure concentration
  • Linear algebra
ppsr: An R implementation of the Predictive Power Score

ppsr: An R implementation of the Predictive Power Score

Update March, 2021: My R package for the predictive power score (ppsr) is live on CRAN!
Try install.packages("ppsr") in your R terminal to get the latest version.

A few months ago, I wrote about the Predictive Power Score (PPS): a handy metric to quickly explore and quantify the relationships in a dataset.

As a social scientist, I was taught to use a correlation matrix to describe the relationships in a dataset. Yet, in my opinion, the PPS provides three handy advantages:

  1. PPS works for any type of data, also nominal/categorical variables
  2. PPS quantifies non-linear relationships between variables
  3. PPS acknowledges the asymmetry of those relationships

Florian Wetschoreck came up with the PPS idea, wrote the original blog, and programmed a Python implementation of it (called ppscore).

Yet, I work mostly in R and I was very keen on incorporating this powertool into my general data science workflow.

So, over the holiday period, I did something I have never done before: I wrote an R package!

It’s called ppsr and you can find the code here on github.

Installation

# You can get the official version from CRAN:
install.packages("ppsr")

## Or you can get the development version from GitHub:
# install.packages('devtools')
# devtools::install_github('https://github.com/paulvanderlaken/ppsr')

Usage

The ppsr package has three main functions that compute PPS:

  • score() – which computes an x-y PPS
  • score_predictors() – which computes X-y PPS
  • score_matrix() – which computes X-Y PPS

Visualizing PPS

Subsequently, there are two main functions that wrap around these computational functions to help you visualize your PPS using ggplot2:

  • visualize_predictors() – producing a barplot of all X-y PPS
  • visualize_matrix() – producing a heatmap of all X-Y PPS
PPS matrix for iris

Note that Species is a nominal/categorical variable, with three character/text options.

A correlation matrix would not be able to show us that the type of iris Species can be predicted extremely well by the petal length and width, and somewhat by the sepal length and width. Yet, particularly sepal width is not easily predicted by the type of species.

Correlation matrix for iris

Exploring mtcars

It takes about 10 seconds to run 121 decision trees with visualize_matrix(mtcars). Yet, the output is much more informative than the correlation matrix:

  • cyl can be much better predicted by mpg than the other way around
  • the classification of vs can be done well using nearly all variables as predictors, except for am
  • yet, it’s hard to predict anything based on the vs classification
  • a cars’ am can’t be predicted at all using these variables
PPS matrix for mtcars

The correlation matrix does provides insights that are not provided by the PPS matrix. Most importantly, the sign and strength of any linear relationship that may exist. For instance, we can deduce that mpg relates strongly negatively with cyl.

Yet, even though half of the matrix does not provide any additional information (due to the symmetry), I still find it hard to derive the most important relations and insights at a first glance.

Moreover, the rows and columns for vs and am are not very informative in this correlation matrix as it contains pearson correlations coefficients by default, whereas vs and am are binary variables. The same can be said for cyl, gear and carb, which contain ordinal categories / integer data, so you can discuss the value of these coefficients depicted here.

Correlation matrix for mtcars

Exploring trees

In R, there are many datasets built in via the datasets package. Let’s explore some using the ppsr::visualize_matrix() function.

datasets::trees has data on 31 trees’ girth, height and volume.

visualize_matrix(datasets::trees) shows that both girth and volume can be used to predict the other quite well, but not perfectly.

Let’s have a look at the correlation matrix.

The scores here seem quite higher in general. A near perfect correlation between volume and girth.

Is it near perfect though? Let’s have a look at the underlying data and fit a linear model to it.

You will still be pretty far off the real values when you use a linear model based on Girth to predict Volume. This is what the original PPS of 0.65 tried to convey.

Actually, I’ve run the math for this linaer model and the RMSE is still 4.11. Using just the mean Volume as a prediction of Volume will result in 16.17 RMSE. If we map these RMSE values on a linear scale from 0 to 1, we would get the PPS of our linear model, which is about 0.75.

So, actually, the linear model is a better predictor than the decision tree that is used as a default in the ppsr package. That was used to generate the PPS matrix above.

Yet, the linear model definitely does not provide a perfect prediction, even though the correlation may be near perfect.

Conclusion

In sum, I feel using the general idea behind PPS can be very useful for data exploration.

Particularly in more data science / machine learning type of projects. The PPS can provide a quick survey of which targets can be predicted using which features, potentially with more complex than just linear patterns.

Yet, the old-school correlation matrix also still provides unique and valuable insights that the PPS matrix does not. So I do not consider the PPS so much an alternative, as much as a complement in the toolkit of the data scientist & researcher.

Enjoy the R package, or the Python module for that matter, and let me know if you see any improvements!

Guidelines for Ethical AI

Guidelines for Ethical AI

As AI systems become more prevalent in society, we face bigger and tougher societal challenges. Given many of these challenges have not been faced before, practitioners will face scenarios that will require dealing with hard ethical and societal questions.

There has been a large amount of content published which attempts to address these issues through “Principles”, “Ethics Frameworks”, “Checklists” and beyond. However navigating the broad number of resources is not easy.

This repository aims to simplify this by mapping the ecosystem of guidelines, principles, codes of ethics, standards and regulation being put in place around artificial intelligence.

github.com/EthicalML/awesome-artificial-intelligence-guidelines/
🔍 High Level Frameworks & Principles🔏 Processes & Checklists🔨 Interactive & Practical Tools
📜 Industry standards initiatives📚 Online Courses🤖 Research and Industry Newsletters
⚔ Regulation and Policy
Links to Awesome Artificial Intelligence Guidelines

This overview of ethical guidelines for Artificial Intelligence is by the same author of the repository of Machine Learning production resources shared earlier this year.

Google’s Responsible AI Practices

Google’s Responsible AI Practices

As a company that uses a lot of automation, optimization, and machine learning in their day-to-day business, Google is set on developing AI in a socially responsible way.

Fortunately for us, Google decided to share their principles and best practices for us to read.

Google’s Objectives for AI applications

The details behind the seven objectives below you can find here.

  1. Be socially beneficial.
  2. Avoid creating or reinforcing unfair bias.
  3. Be built and tested for safety.
  4. Be accountable to people.
  5. Incorporate privacy design principles.
  6. Uphold high standards of scientific excellence.
  7. Be made available for uses that accord with these principles.

Moreover, there are several AI technologies that Google will not build:

Google’s best practices for Responsible AI

For the details behind these six best practices, read more here.

  1. Use a Human-centered approach (see also here)
  2. Identify multiple metrics to assess training and monitoring
  3. When possible, directly examine your raw data
  4. Understand the limitations of your dataset and model
  5. Test, Test, Test,
  6. Continue to monitor and update the system after deployment