Note
Go to the end to download the full example code
PCA variable contributions (bubble version)ΒΆ
This example will plot contributions to the principal components from the original variables. Here, we show the absolute values of the coefficients in a bubble heat map.
from matplotlib import pyplot as plt
import pandas as pd
from sklearn.datasets import load_diabetes
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from psynlig import pca_loadings_map
plt.style.use('seaborn-talk')
data_set = load_diabetes()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_names'])
data = scale(data)
pca = PCA()
pca.fit_transform(data)
kwargs = {
'heatmap': {
'vmin': -1,
'vmax': 1,
},
}
# Plot the value of the coefficients:
pca_loadings_map(
pca,
data_set['feature_names'],
bubble=True,
annotate=False,
**kwargs
)
# Plot the absolute value of the coefficients:
kwargs['heatmap']['vmin'] = 0
pca_loadings_map(
pca,
data_set['feature_names'],
bubble=True,
annotate=False,
plot_style='absolute',
**kwargs
)
plt.show()
Total running time of the script: ( 0 minutes 1.842 seconds)