PCA biplot

This example will plot PCA scores along two principal axes and also show the loadings. In this example we add axis for the loadings so that their values can be read directly.

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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_2d_scores
plt.style.use('seaborn-talk')

data_set = load_breast_cancer()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_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]
class_data = data_set['target']
class_names = dict(enumerate(data_set['target_names']))
data = scale(data)

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

loading_settings = {
    'add_text': True,
    'add_legend': False,
    'biplot': True,
    'text': {
        'fontsize': 'x-large',
        'alpha': 0.8,
        'outline': {'linewidth': 1.5}
    },
}

pca_2d_scores(
    pca,
    scores,
    xvars=xvars,
    class_data=class_data,
    class_names=class_names,
    select_components={(1, 2)},
    loading_settings=loading_settings,
    s=150,
    alpha=.5,
    cmap_loadings='plasma',
)

loading_settings = {
    'add_text': False,
    'add_legend': True,
    'biplot': True,
}

pca_2d_scores(
    pca,
    scores,
    xvars=xvars,
    class_data=class_data,
    class_names=class_names,
    select_components={(1, 2)},
    loading_settings=loading_settings,
    s=150,
    alpha=.5,
    cmap_class='Spectral',
)
plt.show()