Importance

olympus.dashboard.plots.hyperparameter_importance.importance_heatmap_altair(fanova)

Outputs the importance of each hyper-parameter according to FANOVA

Parameters:

fanova: FANOVA

instance of FANOVA class

Examples

>>> from olympus.dashboard.analysis.hpfanova import FANOVA
>>> import pandas as pd
>>> data = [
...     dict(objective=0.12 / 0.08, uid=0, epoch=32, hp1=0.12, hp2=0.08),
...     dict(objective=0.14 / 0.09, uid=0, epoch=32, hp1=0.14, hp2=0.09),
...     dict(objective=0.15 / 0.10, uid=0, epoch=32, hp1=0.15, hp2=0.10),
...     dict(objective=0.16 / 0.11, uid=0, epoch=32, hp1=0.16, hp2=0.11),
...     dict(objective=0.17 / 0.12, uid=0, epoch=32, hp1=0.17, hp2=0.12)
... ]
>>> space = {
...     'hp1': 'uniform(0, 1)',
...     'hp2': 'uniform(0, 1)'
... }
>>> data = pd.DataFrame(data)
>>> fanova = FANOVA(
...    data,
...    hp_names=list(space.keys()),
...    objective='objective',
...    hp_space=space)
>>> chart = importance_heatmap_altair(fanova)

olympus.dashboard.plots.hyperparameter_importance.importance_heatmap_plotly(fanova, columns)

olympus.dashboard.plots.hyperparameter_importance.marginals_altair(fanova)

Outputs the marginal effect of each hyper-parameter according to FANOVA

Parameters:

fanova: FANOVA

instance of FANOVA class

Examples