sklearn-smithy 0.1.0

Toolkit to forge scikit-learn compatible estimators.

Scikit-learn Smithy

Scikit-learn smithy is a tool that helps you to forge scikit-learn compatible estimator with ease.

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WebUI | Documentation | Repository | Issue Tracker

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How can you use it?

✅ Directly from the browser via a Web UI.

• Available at sklearn-smithy.streamlit.app
• It requires no installation.
• Powered by streamlit

✅ As a CLI (command line interface) in the terminal.

• Available via the smith forge command.
• It requires installation: python -m pip install sklearn-smithy
• Powered by typer.

✅ As a TUI (terminal user interface) in the terminal.

• Available via the smith-tui command.
• It requires installing extra dependencies: python -m pip install "sklearn-smithy[textual]"
• Powered by textual.

All these tools will prompt a series of questions regarding the estimator you want to create, and then it will generate the boilerplate code for you.

Why ❓

Writing scikit-learn compatible estimators might be harder than expected.

While everyone knows about the fit and predict, there are other behaviours, methods and attributes that scikit-learn might be expecting from your estimator depending on:

• The type of estimator you're writing.
• The signature of the estimator.
• The signature of the .fit(...) method.

Scikit-learn Smithy to the rescue: this tool aims to help you crafting your own estimator by asking a few questions about it, and then generating the boilerplate code.

In this way you will be able to fully focus on the core implementation logic, and not on nitty-gritty details of the scikit-learn API.

Sanity check

Once the core logic is implemented, the estimator should be ready to test against the somewhat official parametrize_with_checks pytest compatible decorator:

from sklearn.utils.estimator_checks import parametrize_with_checks

@parametrize_with_checks([
    YourAwesomeRegressor,
    MoreAwesomeClassifier,
    EvenMoreAwesomeTransformer,
])
def test_sklearn_compatible_estimator(estimator, check):
    check(estimator)

and it should be compatible with scikit-learn Pipeline, GridSearchCV, etc.

Official guide

Scikit-learn documentation on how to develop estimators.

Supported estimators

The following types of scikit-learn estimator are supported:

• ✅ Classifier
• ✅ Regressor
• ✅ Outlier Detector
• ✅ Clusterer
• ✅ Transformer
  • ✅ Feature Selector
• 🚧 Meta Estimator

Installation

sklearn-smithy is available on pypi, so you can install it directly from there:

python -m pip install sklearn-smithy

Remark: The minimum Python version required is 3.10.

This will make the smith command available in your terminal, and you should be able to run the following:

smith version

sklearn-smithy=...

Extra dependencies

To run the TUI, you need to install the textual dependency as well:

python -m pip install "sklearn-smithy[textual]"

User guide 📚

Please refer to the dedicated user guide documentation section.

Origin story

The idea for this tool originated from scikit-lego #660, which I cannot better explain than quoting the PR description itself:

So the story goes as the following:

• The CI/CD fails for scikit-learn==1.5rc1 because of a change in the check_estimator internals
• In the scikit-learn issue I got a better picture of how to run test for compatible components
• In particular, rolling your own estimator suggests to use parametrize_with_checks, and of course I thought "that is a great idea to avoid dealing manually with each test"
• Say no more, I enter a rabbit hole to refactor all our tests - which would be fine
• Except that these tests failures helped me figure out a few missing parts in the codebase