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plot_timeline

method plot_timeline(models=None, title=None, legend="lower right", figsize=(900, 600), filename=None, display=True)[source]

Plot the timeline of a study.

This plot is only available for models that ran hyperparameter tuning.

Parameters models: int, str, Model, segment, sequence or None, default=None
Models to plot. If None, all models that used hyperparameter tuning are selected.

title: str, dict or None, default=None
Title for the plot.

legend: str, dict or None, default="lower right"
Legend for the plot. See the user guide for an extended description of the choices.

  • If None: No legend is shown.
  • If str: Position to display the legend.
  • If dict: Legend configuration.

figsize: tuple, default=(900, 600)
Figure's size in pixels, format as (x, y)

filename: str, Path or None, default=None
Save the plot using this name. Use "auto" for automatic naming. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as html. If None, the plot is not saved.

display: bool or None, default=True
Whether to render the plot. If None, it returns the figure.

Returns{#plot_timeline-go.Figure or None} go.Figure or None
Plot object. Only returned if display=None.


See Also

plot_edf

Plot the Empirical Distribution Function of a study.

plot_slice

Plot the parameter relationship in a study.

plot_terminator_improvement

Plot the potentials for future objective improvement.


Example

>>> from atom import ATOMClassifier
>>> from optuna.pruners import PatientPruner
>>> from sklearn.datasets import make_classification

>>> X, y = make_classification(n_samples=1000, flip_y=0.2, random_state=1)

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.run(
...     models="LGB",
...     n_trials=15,
...     ht_params={"pruner": PatientPruner(None, patience=2)},
... )
>>> atom.plot_timeline()