PCA Scores (1D) with loadings

This example will plot PCA scores along one principal axis and show the loadings for the same component.

from matplotlib import pyplot as plt
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from psynlig import pca_1d_scores
plt.style.use('seaborn-talk')

data_set = load_breast_cancer()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_names'])
class_data = data_set['target']
class_names = dict(enumerate(data_set['target_names']))
xvars = [
    'mean radius',
    'mean texture',
    'mean perimeter',
    'mean area',
    'mean smoothness',
    'mean compactness',
    'mean concavity',
    'mean concave points',
    'mean symmetry',
    'mean fractal dimension',
]
data = data[xvars]
data = scale(data)

pca = PCA()
reduced_features = pca.fit_transform(data)

pca_1d_scores(
    pca,
    reduced_features,
    xvars=xvars,
    class_data=class_data,
    class_names=class_names,
    select_components={2},
    add_loadings=True,
    s=200,
    alpha=.8
)

plt.show()