Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/koaning/human-learn

Natural Intelligence is still a pretty good idea.
https://github.com/koaning/human-learn

benchmark machine-learning scikit-learn

Last synced: 1 day ago
JSON representation

Natural Intelligence is still a pretty good idea.

Awesome Lists containing this project

README 

        
[![Downloads](https://pepy.tech/badge/human-learn/month)](https://pepy.tech/project/human-learn)

[![Version](https://img.shields.io/pypi/v/human-learn)](https://pypi.org/project/scikit-lego/)

![](https://img.shields.io/github/license/koaning/human-learn)

![](https://img.shields.io/pypi/pyversions/human-learn)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![DOI](https://zenodo.org/badge/278844314.svg)](https://zenodo.org/badge/latestdoi/278844314)






### Human Learn



> Machine Learning models should play by the rules, literally.



## Project Goal



Back in the old days, it was common to write rule-based systems. Systems that do;



![](docs/examples/rules.png)



Nowadays, it's much more fashionable to use machine learning instead. Something like;



![](docs/examples/ml.png)



We started wondering if we might have lost something in this transition. Sure,

machine learning covers a lot of ground but it is also capable of making bad

decisions. We need to remain careful about hype. We also shouldn't forget that 

many classification problems can be handled by natural intelligence too. If nothing

else, it'd sure be a sensible benchmark.



This package contains scikit-learn compatible tools that should make it easier

to construct and benchmark rule based systems that are designed by humans. You

can also use it in combination with ML models.



## Installation



You can install this tool via `pip`.



```python

python -m pip install human-learn

```



The project builds on top of a modern installation of [scikit-learn](https://scikit-learn.org/stable/index.html)

and [pandas](https://pandas.pydata.org/). It also

uses [bokeh](https://bokeh.org/) for interactive jupyter elements, [shapely](https://pypi.org/project/Shapely/) 

for the point-in-poly algorithms and [clumper](https://koaning.github.io/clumper/)

to deal with json datastructures. 



## Documentation



Detailed documentation of this tool can be found [here](https://koaning.github.io/human-learn/).



A free video course can be found on [calmcode.io](https://calmcode.io/human-learn/introduction.html).



## Features



This library hosts a couple of models that you can play with.



### Interactive Drawings



This tool allows you to draw over your datasets. These drawings can later

be converted to models or to preprocessing tools.



![](docs/draw-gif.gif)



### Classification Models



#### FunctionClassifier



This allows you to define a function that can make classification predictions. It's

constructed in such a way that you can use the arguments of the function as a parameter

that you can benchmark in a grid-search.



#### InteractiveClassifier



This allows you to draw decision boundaries in interactive charts to create a

model. You can create charts interactively in the notebook and export it as a

scikit-learn compatible model.



### Regression Models



#### FunctionRegressor



This allows you to define a function that can make regression predictions. It's

constructed in such a way that you can use the arguments of the function as a parameter

that you can benchmark in a grid-search.



### Outlier Detection Models



#### FunctionOutlierDetector



This allows you to define a function that can declare outliers. It's constructed in

such a way that you can use the arguments of the function as a parameter that you

can benchmark in a grid-search.



#### InteractiveOutlierDetector



This allows you to draw decision boundaries in interactive charts to create a

model. If a point falls outside of these boundaries we might be able to declare

it an outlier. There's a threshold parameter for how strict you might want to be.



### Preprocessing Models



#### PipeTransformer



This allows you to define a function that can handle preprocessing. It's

constructed in such a way that you can use the arguments of the function as a parameter

that you can benchmark in a grid-search. This is especially powerful in combination

with the pandas `.pipe` method. If you're unfamiliar with this amazing feature, you

may appreciate [this tutorial](https://calmcode.io/pandas-pipe/introduction.html).



#### InteractivePreprocessor



This allows you to draw features that you'd like to add to your dataset or

your machine learning pipeline. You can use it via `tfm.fit(df).transform(df)` and

`df.pipe(tfm)`.



### Datasets



#### Titanic



This library hosts the popular titanic survivor dataset for demo purposes. The goal of

this dataset is to predict who might have survived the titanic disaster.



#### Fish



The fish market dataset is also hosted in this library. The goal of this dataset

is to predict the weight of fish. However, it can also be turned into a classification problem

by predicting the species.



## Contribution



We're open to ideas for the repository but please discuss any feature you'd like to add 

before working on a PR. This way folks will know somebody is working on a feature and the

implementation can be discussed with the maintainer upfront. 



If you want to quickly get started locally you can run the following command to set the 

local development environment up. 



```

make develop

```



If you want to run all the tests/checks locally you can run.



```

make check

```



This will run flake8, black, pytest and test the documentation pages.