utils.pca.plot_loading_scores
utils.pca.plot_loading_scores(loading_scores, figsize=(12, 8))Creates a heatmap visualization of PCA loading scores.
Generates a heatmap showing the relationship between original features and principal components, with color intensity indicating the strength and direction of the relationship.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| loading_scores | pd.DataFrame | DataFrame containing the loading scores matrix with features as rows and principal components as columns. | required |
| figsize | tuple | Size of the figure as (width, height). Defaults to (12, 8). | (12, 8) |
Returns
| Name | Type | Description |
|---|---|---|
| None | None | The function creates and displays a matplotlib plot. |
Example
from sklearn.decomposition import PCA from sklearn.datasets import load_iris from spotpython.utils.pca import print_loading_scores, plot_loading_scores
Load and prepare iris dataset
iris = load_iris() X = iris.data feature_names = iris.feature_names
Fit PCA and get loading scores
pca = PCA() pca.fit(X) scores_df = print_loading_scores(pca, feature_names)
Create heatmap
plot_loading_scores(scores_df, figsize=(10, 6))