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plot_trials

method plot_trials(models=None, metric=None, title=None, legend="upper left", figsize=(900, 800), filename=None, display=True)[source]

Plot the hyperparameter tuning trials.

Creates a figure with two plots: the first plot shows the score of every trial and the second shows the distance between the last consecutive steps. The best trial is indicated with a star. This is the same plot as produced by ht_params={"plot": True}. 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.

metric: int, str, sequence or None, default=None
Metric to plot (only for multi-metric runs). Add + between options to select more than one. If None, all metrics are selected.

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

legend: str, dict or None, default="upper left"
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, 800)
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_trials-go.Figure or None} go.Figure or None
Plot object. Only returned if display=None.


See Also

plot_evals

Plot evaluation curves.

plot_hyperparameters

Plot hyperparameter relationships in a study.

plot_results

Compare metric results of the models.


Example

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import make_classification

>>> X, y = make_classification(n_samples=100, flip_y=0.2, random_state=1)

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.run(["ET", "RF"], n_trials=15)
>>> atom.plot_trials()