/HyperInteractive

Interactive exploration of hyperparameter tuning results with ipywidget and plotly in jupyter notebook.

Primary LanguagePython

HyperInteractive

Interactive ipywidget and plotly framework for exploring hyperparameter tuning results

Hyper Explore Demo

Requirements

plotly == 4.12.0
ipywidgets == 7.5.1

Getting started

Clone repo and cd into the project directory

$ git clone https://github.com/oaoni/HyperInteractive.git
$ cd HyperInteractive

Launch in a classic jupyter notebook

$ jupyter notebook

Usage

import pandas as pd
from interactivehyper import hyperExplore

data = pd.read_csv('./demo/modeltune.csv')

initial_axis = ['best_test_loss','best_test_corr']
initial_surface_axis = ['mu','alpha','best_test_corr']
legend_group = 'model'
hover_items = ['learning_rate','alpha','mu']

tab = hyperExplore(data,initial_axis,initial_surface_axis,legend_group,hover_items)
tab