Each time we call a function that's meant to perform some operation that could succeed or fail we are always left with the same dilemma. What should be the return value? Should I return nil if a failure happened? Or I should throw an exception? What does failure means anyway? Like every interesting question, the answer is […]
EuRuKo 2017 talks shortlist
Last week I attended the 2017 European Ruby Conference in Budapest. Here is a shortlist of the talks I enjoyed the most and some thoughts on the conference in general. The conference First and foremost this has been the biggest European Ruby conference in 15 years. A demonstration that the community is strong and working […]
Learning to rank with Python scikit-learn
If you run an e-commerce website a classical problem is to rank your product offering in the search page in a way that maximises the probability of your items being sold. For example if you are selling shoes you would like the first pair of shoes in the search result page to be the one […]
Making Ruby Hash#dig even more awesome. Introducing Hash#dig_and_collect
In this blog post I will introduce Hash#dig_and_collect , a simple utility method that is built on top of Hash#dig to help you navigate nested hashes mixed up with arrays.
Dockerized Rails Capybara tests on top of Selenium
If you use Docker to deploy your Rails application you may want to use the same infrastructure to run your tests. However the setup of your Selenium browser tests is far from obvious with Rails and Docker and may generate some confusion . The short answer is available in this repository on Github. For the long answer keep reading this blog post […]
How to isolate complex queries in an object oriented fashion
Building complex queries in ruby can make your code quite difficult to read, manage and reuse. In this blog post I'll present a simple method to decorate active record objects to make your queries fun again!
Create isolated Jupyter ipython kernels with pyenv and virtualenv
Everyone loves isolation. Makes our life easier and our systems much more robust. Isolating Jupyter notebooks makes no exception. Maybe you want to try some cutting edge scientific library, or more simply your latest project dependencies are not compatible with your current system setup. Whatever is your situation, follow me in this simple tutorial on how to […]
A/B Testing, from scratch
If you work in a diligent web development business you probably know what an A/B test is. However, its fascinating statistical theory is usually left behind. Understanding the basics can help you avoid common pitfalls, better design your experiments, and ultimately do a better job in improving the effectiveness of your website. Please hold tight, and enjoy a pleasant […]
Cross Validation done wrong
Cross validation is an essential tool in statistical learning to estimate the accuracy of your algorithm. Despite its great power it also exposes some fundamental risk when done wrong which may terribly bias your accuracy estimate. In this blog post I'll demonstrate - using the Python scikit-learn framework - how to avoid the biggest and […]
Data manipulation primitives in R and Python
Both R and Python are incredibly good tools to manipulate your data and their integration is becoming increasingly important. The latest tool for data manipulation in R is Dplyr whilst Python relies on Pandas. In this blog post I'll show you the fundamental primitives to manipulate your dataframes using both libraries highlighting their major advantages […]