bff.plot.plot_pca_explained_variance_ratio

bff.plot.plot_pca_explained_variance_ratio(pca, label_x='Number of components', label_y='Cumulative explained variance', title='PCA explained variance ratio', hline=None, ax=None, lim_x=None, lim_y=None, grid=None, figsize=10, 4, dpi=80, style='default', **kwargs)

Plot the explained variance ratio of PCA.

PCA must be already fitted.

Parameters

  • pca (sklearn.decomposition.PCA) – PCA object to plot.

  • label_x (str, default 'Number of components') – Label for x axis.

  • label_y (str, default 'Cumulative explained variance') – Label for y axis.

  • title (str, default 'PCA explained variance ratio') – Title for the plot (axis level).

  • hline (float, optional) – Horizontal line (darkorange) to draw on the plot (e.g. at 0.8 to see the number of components needed to keep 80% of the variance).

  • ax (plt.axes, optional) – Axes from matplotlib, if None, new figure and axes will be created.

  • lim_x (Tuple[TNum, TNum], optional) – Limit for the x axis.

  • lim_y (Tuple[TNum, TNum], optional) – Limit for the y axis.

  • grid (str, optional) – Axis where to activate the grid (‘both’, ‘x’, ‘y’). To turn off, set to None.

  • figsize (Tuple[int, int], default (10, 4)) – Size of the figure to plot.

  • dpi (int, default 80) – Resolution of the figure.

  • style (str, default 'default') – Style to use for matplotlib.pyplot. The style is use only in this context and not applied globally.

  • **kwargs – Additional keyword arguments to be passed to the plt.plot function from matplotlib.

Returns

Axes returned by the plt.subplots function.

Return type

plt.axes

Examples

>>> from sklearn.decomposition import PCA
>>> pca = PCA(n_components=20)
>>> pca.fit(data)
>>> plot_pca_explained_variance_ratio(pca)