plot_pca_component_variance#

scikitplot.api.decomposition.plot_pca_component_variance(clf, *, target_explained_variance=0.75, model_type=None, title='Cumulative Explained Variance Ratio by Principal Components', title_fontsize='large', text_fontsize='medium', x_tick_rotation=0, **kwargs)[source]#

Plots PCA components’ explained variance ratios. (new in v0.2.2)

Added in version 0.2.2.

Parameters:
clfobject

PCA instance that has the explained_variance_ratio_ attribute.

target_explained_variancefloat, optional, default=0.75

Looks for the minimum number of principal components that satisfies this value and emphasizes it on the plot.

titlestr, optional, default=’Cumulative Explained Variance Ratio by Principal Components’

Title of the generated plot.

title_fontsizestr or int, optional, default=’large’

Font size for the plot title. Use e.g., “small”, “medium”, “large” or integer-values.

text_fontsizestr or int, optional, default=’medium’

Font size for the text in the plot. Use e.g., “small”, “medium”, “large” or integer-values.

x_tick_rotationint, optional, default=0

Rotates x-axis tick labels by the specified angle.

Added in version 0.3.9.

**kwargs: dict

Generic keyword arguments.

Returns:
axmatplotlib.axes.Axes

The axes on which the plot was drawn.

Other Parameters:
axmatplotlib.axes.Axes, optional, default=None

The axis to plot the figure on. If None is passed in the current axes will be used (or generated if required).

figmatplotlib.pyplot.figure, optional, default: None

The figure to plot the Visualizer on. If None is passed in the current plot will be used (or generated if required).

figsizetuple, optional, default=None

Width, height in inches. Tuple denoting figure size of the plot e.g. (12, 5)

nrowsint, optional, default=1

Number of rows in the subplot grid.

ncolsint, optional, default=1

Number of columns in the subplot grid.

plot_stylestr, optional, default=None

Check available styles with “plt.style.available”. Examples include: [‘ggplot’, ‘seaborn’, ‘bmh’, ‘classic’, ‘dark_background’, ‘fivethirtyeight’, ‘grayscale’, ‘seaborn-bright’, ‘seaborn-colorblind’, ‘seaborn-dark’, ‘seaborn-dark-palette’, ‘tableau-colorblind10’, ‘fast’].

Added in version 0.4.0.

show_figbool, default=True

Show the plot.

save_figbool, default=False

Save the plot.

save_fig_filenamestr, optional, default=’’

Specify the path and filetype to save the plot. If nothing specified, the plot will be saved as png inside result_images under to the current working directory. Defaults to plot image named to used func.__name__.

verbosebool, optional

If True, prints debugging information.

Examples

>>> from sklearn.decomposition import PCA
>>> from sklearn.datasets import load_digits as data_10_classes
>>> import scikitplot as skplt
>>> X, y = data_10_classes(return_X_y=True, as_frame=False)
>>> pca = PCA(random_state=0).fit(X)
>>> skplt.decomposition.plot_pca_component_variance(
...     pca, target_explained_variance=0.95
... )

(Source code, png)

PCA Components Variances 
>>> from sklearn.discriminant_analysis import (
...     LinearDiscriminantAnalysis,
... )
>>> from sklearn.datasets import load_digits as data_10_classes
>>> import scikitplot as skplt
>>> X, y = data_10_classes(return_X_y=True, as_frame=False)
>>> clf = LinearDiscriminantAnalysis().fit(X, y)
>>> skplt.decomposition.plot_pca_component_variance(clf)

(Source code, png)

LDA Components Variances