Feature/Variable importance after a PCA analysis

Question

I have performed a PCA analysis over my original dataset and from the compressed dataset transformed by the PCA I have also selected the number of PC I want to keep (they ex

Answer 1

the pca library contains this functionality.

pip install pca

A demonstration to extract the feature importance is as following:

# Import libraries
import numpy as np
import pandas as pd
from pca import pca

# Lets create a dataset with features that have decreasing variance. 
# We want to extract feature f1 as most important, followed by f2 etc
f1=np.random.randint(0,100,250)
f2=np.random.randint(0,50,250)
f3=np.random.randint(0,25,250)
f4=np.random.randint(0,10,250)
f5=np.random.randint(0,5,250)
f6=np.random.randint(0,4,250)
f7=np.random.randint(0,3,250)
f8=np.random.randint(0,2,250)
f9=np.random.randint(0,1,250)

# Combine into dataframe
X = np.c_[f1,f2,f3,f4,f5,f6,f7,f8,f9]
X = pd.DataFrame(data=X, columns=['f1','f2','f3','f4','f5','f6','f7','f8','f9'])

# Initialize
model = pca()
# Fit transform
out = model.fit_transform(X)

# Print the top features. The results show that f1 is best, followed by f2 etc
print(out['topfeat'])

#     PC      feature
# 0  PC1      f1
# 1  PC2      f2
# 2  PC3      f3
# 3  PC4      f4
# 4  PC5      f5
# 5  PC6      f6
# 6  PC7      f7
# 7  PC8      f8
# 8  PC9      f9

Plot the explained variance

model.plot()

Make the biplot. It can be nicely seen that the first feature with most variance (f1), is almost horizontal in the plot, whereas the second most variance (f2) is almost vertical. This is expected because most of the variance is in f1, followed by f2 etc.

ax = model.biplot(n_feat=10, legend=False)

Biplot in 3d. Here we see the nice addition of the expected f3 in the plot in the z-direction.

ax = model.biplot3d(n_feat=10, legend=False)