Rethinking machine learning pipelines a bit.
I was wondering if there might be an easier way to construct scikit-learn pipelines. Don't get me wrong, scikit-learn is amazing when you want elaborate pipelines (exibit A, exibit B) but maybe there is also a place for something more lightweight and playful. This library is all about exploring that.
Imagine that you are dealing with the titanic dataset.
import pandas as pd
df = pd.read_csv("https://calmcode.io/static/data/titanic.csv")
df.head()Here's what the dataset looks like.
| survived | pclass | name | sex | age | fare | sibsp | parch |
|---|---|---|---|---|---|---|---|
| 0 | 3 | Braund, Mr. Owen Harris | male | 22 | 7.25 | 1 | 0 |
| 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Thayer) | female | 38 | 71.2833 | 1 | 0 |
| 1 | 3 | Heikkinen, Miss. Laina | female | 26 | 7.925 | 0 | 0 |
| 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35 | 53.1 | 1 | 0 |
| 0 | 3 | Allen, Mr. William Henry | male | 35 | 8.05 | 0 | 0 |
The goal of this dataset is to predict who survived, so survived is the target column for a classification task. But in order to make the right predictions you would need to encode the features in the right way. So to do that, you might construct a preprocessing pipeline like this:
from sklearn.pipeline import make_union, make_pipeline
from sklearn.preprocessing import OneHotEncoder
from skrub import SelectCols
pipe = make_union(
SelectCols(["age", "fare", "sibsp", "parch"]),
make_pipeline(
SelectCols(["sex", "pclass"]),
OneHotEncoder()
)
)This pipeline takes the age, fare, sibsp and parch features as-is. These features are already numeric so these do not need to be changed. But the sex and pclass features are candidates to one-hot encode first. These are categorical features, so it helps to encode them as such.
The pipeline works, and it's fine, but you could wonder if this is easy. After all, you do need to know scikit-learn fairly well in order to build a pipeline this way and you may also need to appreciate Python. There's some nesting happening in here as well, so for a novice or somebody who just immediately wants to make a quick model ... there's some stuff that gets in the way. All of this is fine when you consider that scikit-learn needs to allow for elaborate pipelines ... but if you just want something dead simple ... then you may appreciate another syntax instead.
Playtime offers an API that allows you to declare the aforementioned pipeline by doing this instead:
from playtime import feats, onehot
formula = feats("age", "fare", "sibsp", "parch") + onehot("sex", "pclass")This forumla object is just an object that can accumulate components.
# This object is a scikit-learn pipeline but with operator support!
formulaIt's pretty much the same pipeline as before, but it's a lot easier to go ahead and declare. You're mostly dealing with column names and how to encode them, instead of thinking about how scikit-learn constructs a pipeline.
This is what scikit-playtime is all about, but this is just the start of what it can do. If that sounds interest you can read more on the documentation page.
Alternative you may also explore this tool by installing it via:
python -m pip install scikit-playtime
This project was originally part of my work over at calmcode labs but my employer probabl has been very supportive and has allowed me to work on this project during my working hours. This was super cool and I wanted to make sure I recognise them for it.