watex.utils.plot_pca_components#

watex.utils.plot_pca_components(components, *, feature_names=None, cmap='viridis', savefig=None, **kws)[source]#

Visualize the coefficient of principal component analysis (PCA) as a heatmap

Parameters:

  • components – Ndarray, shape (n_components, n_features)or PCA object Array of the PCA compoments or object from watex.analysis.dimensionality.nPCA. If the object is given it is not necessary to set the feature_names

  • feature_names – list or str, optional list of the feature names to locate in the map. Feature_names and the number of eigen vectors must be the same length. If PCA object is passed as components arguments, no need to set the feature_names. The name of features is retreived automatically.

  • cmap – str, default=’viridis’ the matplotlib color map for matshow visualization.

  • kws – dict, Additional keywords arguments passed to matplotlib.pyplot.matshow

Examples:

(1)-> with PCA object

>>> from watex.datasets import fetch_data
>>> from watex.utils.plotutils import plot_pca_components
>>> from watex.analysis import nPCA
>>> X, _= fetch_data('bagoue pca')
>>> pca = nPCA (X, n_components=2, return_X =False)# to return object
>>> plot_pca_components (pca)

(2)-> use the components and features individually

>>> components = pca.components_
>>> features = pca.feature_names_in_
>>> plot_pca_components (components, feature_names= features,
                         cmap='jet_r')