# Are the Magnitudes of PCA Weights Reflective of their Importance?

I have a data matrix with N entries and n features.

I wanted to find which features are the most important in explaining the data.

So, I started with PCA, but PCA components are linear combination of the original features and a lot of features have non-zero weights.

So, I tried Sparse PCA and when I get the components:

from sklearn.decomposition import SparsePCA

pca = SparsePCA(n_components=2)
x_reduced = pca.fit_transform(x)

print(pca.components_)

[[  0.00000000e+00  -5.51883148e-01   0.00000000e+00  -4.48853814e+03
0.00000000e+00   0.00000000e+00   0.00000000e+00  -7.00452241e+05
-1.32201606e+04  -9.07503173e+01  -4.13760727e+01  -7.59535971e+00
-3.73699760e+02   0.00000000e+00   0.00000000e+00   0.00000000e+00
0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
0.00000000e+00  -2.57843033e+04  -6.00876332e+04  -2.83214559e+00
-5.71307312e+00  -8.64860741e+03   0.00000000e+00  -1.10025048e+02
-1.31741530e-01   0.00000000e+00  -3.35139193e+02  -3.19273051e+00
0.00000000e+00  -1.55725943e+04   0.00000000e+00  -9.43423387e+01
0.00000000e+00  -1.63657513e+02   0.00000000e+00  -3.02014818e+04
-3.72207930e+02  -1.00095772e+04]
[ -1.06618508e+01  -4.98409303e+00   0.00000000e+00   2.39169477e+01
-2.69867091e+00   0.00000000e+00   0.00000000e+00   1.21823612e+04
-2.46473015e+05  -1.32679881e+03  -7.32920896e+02  -1.43417641e+02
-3.96088636e-02  -1.45871975e+00   0.00000000e+00   0.00000000e+00
0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
0.00000000e+00  -1.25468810e+02  -6.56146675e+02  -7.98195673e+01
-1.67858364e+01  -1.30710862e+05  -3.69910267e+01  -3.59490984e+02
-6.82808350e+01   0.00000000e+00  -1.01658352e+03  -1.36297880e+02
-4.01496541e-02  -2.58388818e+05  -1.82709863e+00  -2.60664817e+02
0.00000000e+00  -6.50151510e+02   0.00000000e+00   1.53823591e+03
8.10467754e+00  -8.98170228e+03]]


Now, my question is:

1. Is the magnitude of a dimension's weight in the Principal component a measure of its importance for the overall data?

Further, I don't think the sign of the weight matters but any comments are welcome.

1. Is there any way I can find how much an individual feature is responsible for explaining the variance? I guess this is just a corollary of 1. I can go through top components and sum their overall importance in terms of percentage.

2. Any methods I can use to infer the importance of the dimensions in the feature space, knowing the feature's weights in the principal components' space?