erdogant
commented
4 years ago Dear Rendiere,
Its great to read your enthusiasm using the pca library! Thank you for pointing out these issues. I fixed the length of the arrow, number of features and included some of the missing docstrings.
Update the library with: pip install -U pca The version should be >= 1.07. Check the version with:
import pca
pca.__version__Example on the arrest dataset:
from pca import pca
import pandas as pd
model = pca(normalize=True)
# Dataset
df = pd.read_csv('usarrest.txt')
# Setup dataset
X = df[['Murder','Assault','UrbanPop','Rape']].astype(float)
X.index = df['state'].values
# Fit transform
out = model.fit_transform(X)
out['topfeat']
# Make plot
ax = model.biplot(n_feat=4, legend=False)
ax = model.biplot3d(n_feat=4, legend=False)
Rendiere
I'm recreating figure 10.1 from the book Introduction to Statistical Learning with this library, specifically creating a 2-d biplot from the USA Arrests dataset.
However, when creating a biplot only the first 2 loading vectors are displayed irrespective of what I pass to
n_features. In addition, although a separate issue, the loading vector label is plotted outside the scale of the plot.From a quick scan of the code it looks like the issue is in the
compute_topfeatmethod, wheren_featnever gets taken into account, but rathern_pcsgets iterated over twice.P.S - great work on this library. Exactly what I was looking for when googling "PCA biplots python". For that reason, I wouldn't mind helping out with maintaining this library if needs be.