A lot of tabular modeling gets easier the moment you stumble onto the right derived quantity. Not something mysterious or “deep.” It’s usually something you can name: a ratio that turns two raw columns into a rate; a difference that becomes a margin; a simple count that captures what a bunch of messy fields were hinting at.

I couldn’t find any instructions online so I thought I’d post them here for anyone who goes through a similar struggle.

Introduction

This post kicks off the second part of a hands-on series about program synthesis. We’ll apply the previously explored technique (an enumerative bottom-up search) to a slightly more realistic problem: automatically generating features for the Iris dataset.

There’s been a lot of work in the Haskell ecosystem that has made it easier to write interactive Kaggle-like scripts. I’d like to showcase the synergy between 3 such tools: dataframe (my own creation), hasktorch, and IHaskell.

Introduction

This post kicks off a hands-on series about program synthesis—the art of teaching machines how to generate code. We’ll build a tiny, FlashFill-style synthesiser that learns to turn strings like “Joshua Nkomo” into “J. Nkomo” from input/output pairs. We’ll see how to define a tiny string-manipulation language, write an interpreter, and search the space of programs to find one that solves our toy problem.

My fondness for alternative Haskells

I’ve been working on a dataframe implementation in Haskell for about a year now. While my focus has been on ergonomics the question of performance has inevitably come up. I haven’t made significant performance investments but I thought it might be worth snapshotting the performance to establish a baseline.