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pythonpandasscikit-learnstatisticspca

Find out which features are in my components after PCA

I performed a PCA of my data. The data looks like the following:

df
Out[60]: 
        Drd1_exp1  Drd1_exp2  Drd1_exp3  ...  M7_pppp  M7_puuu  Brain_Region
0            -1.0       -1.0       -1.0  ...      0.0      0.0          BaGr

3            -1.0       -1.0       -1.0  ...      0.0      0.0          BaGr
4            -1.0       -1.0       -1.0  ...      0.0      0.0          BaGr
          ...        ...        ...  ...      ...      ...           ...
150475       -1.0       -1.0       -1.0  ...      0.0      0.0          BaGr
150478       -1.0       -1.0       -1.0  ...      0.0      0.0          BaGr
150479       -1.0       -1.0       -1.0  ...      0.0      0.0          BaGr

I know used every row until 'Brain Regions' as features. I also standardized them. These features are different experiments, that give me information about a 3D image of a brain. I'll show you my code:

from sklearn.preprocessing import StandardScaler
x = df.loc[:, listend1].values
y= df.loc[:, 'Brain_Region'].values

x = StandardScaler().fit_transform(x)

from sklearn.decomposition import PCA

pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents
             , columns = ['principal component 1', 'principal component 2'])

finalDf = pd.concat([principalDf, df[['Brain_Region']]], axis = 1)

I then plotted finalDF:

enter image description here

My question now is: How can I find out, which features contribute to my Components? How can I find out, to interpret the data?

Solution

  • You can use pca.components_ (or pca.components depending on the sklearn version). It has shape (n_components, n_features), in your case (2, n_features) and represents the directions of maximum variance in the data, which reflects the magnitude of the corresponding values in the eigenvectors (higher magnitude - higher importance). You will have something like this:

    [[0.522 0.26 0.58 0.56],
 [0.37 0.92 0.02 0.06]]

    implying that for the first component (first row) the first, third and last features have an higher importance, while for the second component only the second feature is important.

    Have a look to sklern PCA attributes description or to this post.

    By the way, you can also use a Random Forest Classifier including the labels, and after the training you can explore the feature importance, e.g. this post.