A framework for feature exploration in Data Science

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.

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

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)

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

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

dataloader functions to load file-based data convert to necessary pandas dataframe vice versa. necessary pandas dataframe:
original df
numerical cols
categorical cols

eevee is a string parser that converts string to json format

One Experiment should represent one episode.
Experiment memorizes the tranformation of data.
Experiment governs all the Models.
Can be used to pick which model is best in an Experiment.

Journal governs all the Experiments.
BeeJournalhive is used to print all the metrics across Experiments (or episodes)
Can also be used to pick new features from the last or best Experiments for the next episode.

python setup.py sdist twine upload dist/*