utils.pca.get_loading_scores

utils.pca.get_loading_scores(pca, feature_names)

Computes the loading scores matrix for Principal Component Analysis (PCA).

Creates and returns a DataFrame showing how each original feature contributes to each principal component.

Parameters

Name	Type	Description	Default
pca	sklearn.decomposition.PCA	Fitted PCA object containing the components_ attribute with the principal components.	required
feature_names	list - like	Names of the original features, must match the order of features used in PCA fitting.	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: DataFrame containing the loading scores matrix with features as rows and principal components as columns.

Example

from sklearn.decomposition import PCA from sklearn.datasets import load_iris from spotpython.utils.pca import print_loading_scores,

Load and prepare iris dataset

iris = load_iris() X = iris.data feature_names = iris.feature_names

Fit PCA

pca = PCA() pca.fit(X)

Print loading scores

scores_df = print_loading_scores(pca, feature_names) print(scores_df)