Residual variance

This example will show the residual variance from a principal component analysis as a function of the number of principal components considered.

from matplotlib import pyplot as plt
import pandas as pd
from sklearn.datasets import load_wine
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from psynlig import pca_residual_variance
plt.style.use('ggplot')

data_set = load_wine()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_names'])
data = scale(data)

pca = PCA()
pca.fit_transform(data)

pca_residual_variance(pca, marker='o', markersize=16, alpha=0.8)

plt.show()