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https://github.com/fbruzzesi/sklearn-smithy

Toolkit to forge scikit-learn compatible estimators
https://github.com/fbruzzesi/sklearn-smithy

cli data-science machine-learning python scikit-learn webui

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Toolkit to forge scikit-learn compatible estimators

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README 

        



# 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](https://sklearn-smithy.streamlit.app/) | [Documentation](https://fbruzzesi.github.io/sklearn-smithy) | [Repository](https://github.com/fbruzzesi/sklearn-smithy) | [Issue Tracker](https://github.com/fbruzzesi/sklearn-smithy/issues)



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How can you use it?



✅ Directly from the browser via a Web UI. 







- Available at [sklearn-smithy.streamlit.app](https://sklearn-smithy.streamlit.app/)

- It requires no installation.

- Powered by [streamlit](https://streamlit.io/)






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







- Available via the `smith forge` command.

- It requires [installation](#installation): `python -m pip install sklearn-smithy`

- Powered by [typer](https://typer.tiangolo.com/).






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







- Available via the `smith forge-tui` command.

- It requires installing [extra dependencies](#extra-dependencies): `python -m pip install "sklearn-smithy[textual]"`

- Powered by [textual](https://textual.textualize.io/).






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`](https://scikit-learn.org/dev/modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html#sklearn.utils.estimator_checks.parametrize_with_checks)

pytest compatible decorator:



```py

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](https://scikit-learn.org/dev/developers/develop.html#developing-scikit-learn-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](https://pypi.org/project/sklearn-smithy), so you can install it directly from there:



```bash

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:



```bash

smith version

```



> sklearn-smithy=...



### Extra dependencies



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



```bash

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

```



## User guide 📚



Please refer to the dedicated [user guide](https://fbruzzesi.github.io/sklearn-smithy/user-guide/) documentation section.



## Origin story



The idea for this tool originated from [scikit-lego #660](https://github.com/koaning/scikit-lego/pull/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](https://github.com/scikit-learn/scikit-learn/issues/28966) I got a better picture of how to run test for compatible components

> - In particular, [rolling your own estimator](https://scikit-learn.org/dev/developers/develop.html#rolling-your-own-estimator) suggests to use [`parametrize_with_checks`](https://scikit-learn.org/dev/modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html#sklearn.utils.estimator_checks.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