Beehive

A framework for feature exploration in Data Science

Background

What do we do when we finish one episode of feature exploration in a jupyter notebook?
We overwrite the notebook or clone the notebook for the next episode of feature exploration.

I say this is inefficient, thus this project.

Purpose

To help documentation for grouping all episode of feature exploration in a single notebook.

Project Elaboration

terms

episode: one set of feature trained on a model.
e.g.
episode_1 = (sepal_length, sepal_width) | (target_class)
episode_2 = (sepal_length, sepal_width, petal_length, petal_width) | (target_class)
episode_3 = (sepal_length_normalized, sepal_width_normalized, petal_length_normalized, petal_width_normalized) | (target_class)

get to know the classes

Combee

One Combee should represent one model and its features includes: (metrics, confusion matrix, feature importances)
(sklearn-based)

CombeKeras

One Combee should represent one model and its features includes: (metrics, confusion matrix)
(keras-based)
needed because Keras has different features and functions compared to sklearn

Vespiqueen

One Vespiqueen should represent one episode.
Vespiqueen memorizes the tranformation of data.
Vespiqueen governs all the Combees.
Can be used to pick which model is best in a Vespiqueen.

Beehive

Beehive governs the whole Vespiqueen.
Beehive is used to print all the metrics across Vespiqueen (or episodes)
Can also be used to pick new features from the last or best vespiqueen for the next episode.

How to Use