I love using apps, email, social networking and messaging. But I want to do use them on my own time. So many years ago I decided to not let my phone interrupt me.
I love using apps, email, social networking and messaging. But I want to do use them on my own time. So many years ago I decided to not let my phone interrupt me.
I explore MNIST digits generated by a Generative Adversarial Network trained on Google Colab using Pytorch Lightning.
I explore the automatic logging capabilities of MLFlow for Pytorch.
I explore the automatic logging capabilities of MLFlow for Scikit Learn. In the process I found a bug in MLFlow, reported it and wrote a pull request to fix it.
For the last few months I’ve been using MFlow in production, specifically its Tracking component. MLFlow is an open source project for lifecycle tracking and serving of ML models, coming out of Databricks. MLFlow is model agnostic, so you can use with SKLearn, XGBoost, Pytorch, Tensorflow, FBProphet, anything.
After the 2020 US presidential election, the Trump campaign filed over 50 lawsuits and attacked the integrity of the elections by claiming there was voter fraud. One of the last lawsuits was filed in the Supreme Court of the United States by the state of Texas. Here I look at the statistical claims made in this lawsuit that were supposed to show irregularities in the Georgia vote.
In real-life, non-digital situations, it's often not feasible to run true A/B tests. In such cases, we can compare before and after rollout conversions at a treatment site, while using a similar control site to measure and correct for seasonality. The post discusses how to compute increasingly correct p-values and bayesian probabilities in such scenarios.
Sometimes we look at the top performers in a field and see obviously uneven representations of groups (gender, ethnicity, etc). There a multitude of factors that can lead to it, such as unfair bias in access to opportunities. Here I will show one unintuitive mathematical effect that can contribute to such unevenness in the case of normal distributions.
Most charts should be line charts or stacked area chart, because they communicate valuable trend information and are easy to parse for the human eyes and brain.
Format numbers for human consumption. What is more readable,
153,859? Showing numbers effectively on spreadsheets, charts, dashboards, reports is a basic ingredient for readability, like formatting code in programming.
Making clear, readable charts is part of the craftmanship minimum for any data related role. In part one, I look at how to present categorical data.
I use Monte Carlo simulations to explore the false positive rate of Multi-armed bandits.
Multi-armed bandits minimize regret when performing A/B tests, trading off between exploration and exploitation. Monte Carlo simulations shows that less exploration yields less statistical significance.
PlanOut is a framework for online field experiments. It was created by Facebook in 2014 to make it easy to run and iterate on sophisticated experiments in a statistically sound manner.
A/B tests go wrong all the time, even in sophisticated product teams. As this article shows, for a range of problems we can run automated validation checks to catch problems early, before they have too bad of an effect on customers or the business. These validation checks compare various statistical properties of the funnels A and B to catch likely problems. Large technology companies are running such validation checks automatically and continuously for their online experiments.
I show using Monte Carlo simulations that randomizing user assignments into A/B test experiments makes it possible to run multiple A/B tests at once and measure accurate lifts on the same metric, assuming the experiments are independent.
I compare probabilities from Bayesian A/B testing with Beta distributions to frequentist A/B tests using Monte Carlo simulations. Under a lot of circumstances, the bayesian probability of the action hypothesis being true and the frequentist p value are complementary.
The G-test for conversion A/B tests is similar to the Chi-squared test. Monte-Carlo simulations show that the two are indistinguishable in practice.
I use Monte Carlo simulations to explore how A/B testing on Watts–Strogatz random graphs depends on the degree distribution of the social network.
I use Monte Carlo simulations to show that experimentation on social networks is a beatiful statistical problem with unexpected nuances due to network effects.
Increased false positive rate due to early stopping is beautiful nuance of statistical testing. It is equivalent to running at an overall higher alpha. Data scientists need to be aware of this phenomenon so they can control it and keep their organizations honest about their experimental results.
Fisher’s exact test directly computes the same p value as the Chi-squared test, so it does not rely on the Central Limit Theorem to hold.
In an ealier post, I wrote about A/B testing conversion data with the Z-test. The Chi-squared test is a more general test for conversion data, because it can work with multiple conversion events and multiple funnels being tested (A/B/C/D/..).
The t-test is better than the z-test for timespent A/B tests, because it explicitly models the uncertainty of the variance due to sampling. Using Monte-Carlo simulations I show that around N=100, the t-test becomes the z-test.
I discuss the Z-test for A/B testing and show how to compute parameters such as sample size from first principles. I use Monte Carlo simulations to validate significance level and statistical power, and visualize parameter scaling behaviour.
In the previous post, I talked about the importance of the Central Limit Theorem (CLT) to A/B testing. Here we will explore cases when we cannot rely on the CLT to hold.
When working with hypothesis testing, the desciptions of the statistical method often has normality assumptions. For example, the Wikipedia page for the z-test starts like this: "A Z-test is any statistical test for which the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution". What does this mean? How do I know it’s a valid assumption for my data?
Sometimes I get to put on my Data Engineering hat for a few days. I enjoy this because I like to move up and down the Data Science stack and I try to keep myself sharp technically. Recently I was able to spend a few days optimizing our Airflow ETL for speed.
My list of SQL best practices for Data Scientists and Analysts, or, how I personally write SQL code. I picked this up at Facebook, and later improved it at Fetchr.
This is a simple post about SQL code formatting. Most of this comes from my time as a Data Engineer at Facebook.
I’ve worked at 5-10 different organizations, most of them were startups or startuppy companies. I’ve done a lot of planning in small teams, and also taken part in company-wide leadership planning. Here I will describe what has worked well for me in small team settings, focusing on time estimation.
The meta-goal of goaling is to stretch yourself to achieve more, and to feel good about what you’ve achieved. Whatever happened this year, it’s always possible to achieve a lot more and feel better about yourself next year. To hijack a Feynman quote, there is plenty of room at the top.
2019 was another big year for Pytorch, one of the most popular Deep Learning libraries out there. Pytorch has become the de facto deep learning library used for research thanks to it’s dynamic graph model which allows fast model experimentation. It’s also become production ready, with support for mobile and infrastructure tooling such as Tensorboard.
I look at some fundamental charts of Apple, Activision Blizzard and Intel.
I show calibration curves for four different binary classification Scikit-Learn models we built for delivery prediction at Fetchr, trained using real-world data:
I use simulated self-play by ranking episodes by summed reward. Game outcomes are divided in two by cutting at the median, winners are assigned +1 rewards, losers are assigned -1 rewards, like in games like Go and Chess. Unlike naive policy gradient descent used in previous posts, this version solves all OpenAI classic control problems, albeit slowly.
I try to generalize the policy gradient algorithm as introduced earlier to solve all the OpenAI classic control problems. It works for CartPole and Acrobot, but not for Pendulum and MountainCar environments.
Opportunities for automating, optimizing and enabling processes with ML at a delivery company such as Fetchr are plentiful. We put three families of ML models into production. These 3 areas are: Scheduling, Notifications and Operational choice.
The CartPole problem is the Hello World of Reinforcement Learning, originally described in 1985 by Sutton et al. The environment is a pole balanced on a cart. CartPole is one of the environments in OpenAI Gym, so we don't have to code up the physics. Here I walk through a simple solution using Pytorch.
The idea is simple: write a document which helps new and existing people—both managers and individual contributors—get an objective, metrics-based picture of the business. This is helpful when new people join, when people start working in new segments of the business, and to understand other parts of the company.
Using historic gameplay between strong Go players as training data, a CNN model is built to predict good Go moves on a standard 19x19 Go board.
While working on arabic-vs-rest classification, I was curious how good out-of-the-box models perform with publicly available data, and then compare that with what we can achieve with internal data / features derived from millions of deliveries. We train Scikit-learn and Pytorch models for this classification task and achieve 90% prediction accuracy on publicly available data and out-of-the-box models, while internally 99% is achievable.
I use PyMC3 to solve the food delivery toy problem and explore some alternative priors.
Most A/B testing tools default to α=0.05, meaning the expected false positive rate is 5%. In this post I explore the trade-offs between moving fast, ie. using higher α, versus being sure, ie. using lower α.
I was grabbing a burger at Shake Shack, Mall of the Emirates in Dubai, when I noticed this notebook on the counter. The staff is using it to track food deliveries and each service (Carriage, Talabat, UberEats, Deliveroo) has its own column with the order numbers. Let's assume this is the only page for the day, and ask ourselves: given this data, what is the probability that UberEats is the most popular food delivery service?.
The Collatz conjecture is a conjecture in mathematics that concerns a sequence defined as follows: start with any positive integer n. Then each term is obtained from the previous term as follows: if the previous term is even, the next term is one half the previous term. If the previous term is odd, the next term is 3 times the previous term plus 1. The conjecture is that no matter what value of n, the sequence will always reach 1.
It’s easy to build a CNN that does well on MNIST digit classification. How easy is it to break it, to distort the images and cause the model to misclassify?
CIFAR-10 is a classic image recognition problem, consisting of 60,000 32x32 pixel RGB images (50,000 for training and 10,000 for testing) in 10 categories: plane, car, bird, cat, deer, dog, frog, horse, ship, truck. Convolutional Neural Networks (CNN) do really well on CIFAR-10, achieving 99%+ accuracy. The Pytorch distribution includes an example CNN for solving CIFAR-10, at 45% accuracy. I will use that and merge it with a Tensorflow example implementation to achieve 75%. We use torchvision to avoid downloading and data wrangling the datasets. Like in the MNIST example, I use Scikit-Learn to calculate goodness metrics and plots.
MNIST is a classic image recognition problem, specifically digit recognition. It contains 70,000 28x28 pixel grayscale images of hand-written, labeled images, 60,000 for training and 10,000 for testing. Convolutional Neural Networks (CNN) do really well on MNIST, achieving 99%+ accuracy. The Pytorch distribution includes a 4-layer CNN for solving MNIST. Here I will unpack and go through this example. We use torchvision to avoid downloading and data wrangling the datasets. Finally, instead of calculating performance metrics of the model by hand, I will extract results in a format so we can use SciKit-Learn's rich library of metrics.
I use the standard Iris dataset for supervised learning with a Support Vector Machine model using Pytorch's autograd.
A PyTorch model is trained on public Hacker News data, embedding posts and comments into a high-dimensional vector space, using the mean squared error (MSE) of dot products as the loss function. The resulting model is reasonably good at finding similar posts and recommending posts for users.
rxe is a thin wrapper around Python's
re module. The various
rxe functions are wrappers around corresponding
re patterns. For example,
rxe.digit().one_or_more('a').whitespace() corresponds to
rxe uses parentheses but wants to avoid unnamed groups, the internal (equivalent) representation is actually
\d(?:a)+\s. This pattern can always be retrieved with
I will show how to solve the standard A x = b matrix equation with PyTorch. This is a good toy problem to show some guts of the framework without involving neural networks.
Over a period of 6 months, we rolled out a Machine Learning model to predict a customer’s delivery (latitude, longitude). During the recent holiday peak, this ML model handled most of Fetchr’s order scheduling.
2018 was a hot year for Data Science and AI. Here we picked out 5 highlights, which in our opinion shaped the field in the past year.
Sometimes, the seven gods of data science, Pascal, Gauss, Bayes, Poisson, Markov, Shannon and Fisher, all wake up in a good mood, and things just work out. Recently we had such an occurence at Fetchr, when the Operational Excellence team posed the following question: if we could pick our Saudi warehouse locations, where would be put them? What is the ideal number of warehouses, and, what does ideal even mean? Also, what should our “delivery radius” be?
Previously I wrote two articles about data infra and data engineering at Fetchr. This time I want to move up the stack and talk about a simple piece of metrics engineering that proved to be very impactful: Growth Accounting and Backtraced Growth Accounting.
A description of our Analytics+ML cluster running on AWS, using Presto, Airflow and Superset.
Warren Buffett says deciding what not to spend time on is just as important as deciding what to spend time on.
When working with averages, we have to be careful. There are pitfalls lurking to pollute our statistics and results reported.
We used Hive/Presto on AWS together with Airflow to rapidly build out the Data Science Infrastructure at Fetchr in less than 6 months.
I used to think that a good analogy for using data is the instrumentation of a cockpit in an airliner. Lots of instruments, and if they fail, the pilot can’t fly the plane and bad things happen. There’s no autopilot for companies. The problem with this analogy is that planes aren’t built in mid-air. Product teams and companies constantly need to build and ship new products.
I gave this talk at the O’Reilly Strata Conference London in 2016 June, mostly based on what I learned at Prezi from 2012-2016.
I read this book on my first vacation after I started working at Facebook and thus became a semi-regular Hack/HHVM user. I highly recommend reading (parts of) it. But not to learn Hack/PHP, which is irrelevant to most people. Instead, it’s to learn about how Facebook improved it’s www codebase and performance without rewriting the old PHP code in one big effort, and thus avoided the famous Second-system effect.
This post is about the amazing success of Einstein's general theory of relativity. The theory predicts, among other things the accelerating Universe, black holes, gravitational lensing and gravitational waves. The real shocker is to remember that Einstein didn't invent general relativity to explain these. He didn’t know about these, they didn't exist at that time!Continue reading
Most bets businesses take, be it hiring, features, products or strategy don't work out. Still, many businesses are successful despite setbacks. A negative attitude---even when the analysis of the situation is in fact correct---may be missing the bigger picture.Continue reading
For the past 2 months I've been using Cloud9 for writing code in the cloud, and I can wholeheartedly recommend it: it just works for me. It's basically Docker plus an IDE: you get a Docker container running Ubuntu that you can access over a web IDE.Continue reading
A spreadsheet comparing the three opensource workflow tools for ETL.
Pinball is an ETL tool written by Pinterest. Like Airflow, it supports defining tasks and dependencies as Python code, executing and scheduling them, and distributing tasks across worker nodes. It supports calendar scheduling (hourly/daily jobs, also visualized on the web dashboard). Unfortunately, I found Pinball has very little documentation, very few recent commits in the Github repo and few meaningful answers to Github issues by maintainers, while it's architecture is complicated and undocumented.Continue reading
Make a simple blog with Github Pages and Pelican.Continue reading
Airflow is a workflow scheduler written by Airbnb. It supports defining tasks and dependencies as Python code, executing and scheduling them, and distributing tasks across worker nodes. It supports calendar scheduling (hourly/daily jobs, also visualized on the web dashboard), so it can be used as a starting point for traditional ETL. It has a nice web dashboard for seeing current and past task state, querying the history and making changes to metadata such as connection strings.
Thinking in Systems, written by the late Donella Meadows, is a book about how to think about systems, how to control systems and how systems change and control themselves. A system can be anything from a heating furnace to a social system. The gem of the book is the part about system traps. System traps are ways a system can go wrong; examples are drift to low performance, seeking the wrong goals, shifting the burden, etc.
I review Luigi, an execution framework for writing data pipes in Python code. It supports task-task dependencies, it has a simple central scheduler with an HTTP API and an extensive library of helpers for building data pipes for Hadoop, AWS, Mysql etc. It was written by Spotify for internal use and open sourced in 2012. A number of companies use it, such as Foursquare, Stripe, Asana.Continue reading
Cargo cult data is when you're collecting and looking at data when making decisions, but you're only following the forms and outside appearances of scientific investigation and missing the essentials, so it doesn't work.Continue reading