Skip to content

plot_components

method plot_components(show=None, title=None, legend="lower right", figsize=None, filename=None, display=True)[source]

Plot the explained variance ratio per component.

Kept components are colored and discarded components are transparent. This plot is available only when feature selection was applied with strategy="pca".

Parameters show: int or None, default=None
Number of components to show. None to show all.

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 or None, default=None
Figure's size in pixels, format as (x, y). If None, it adapts the size to the number of components shown.

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_components-go.Figure or None} go.Figure or None
Plot object. Only returned if display=None.


See Also

plot_pca

Plot the explained variance ratio vs number of components.

plot_rfecv

Plot the rfecv results.


Example

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

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.feature_selection("pca", n_features=5)
>>> atom.plot_components(show=10)