PCA inverse transform manually
1) transform is not data * pca.components_.
Firstly, * is not dot product for numpy array. It is element-wise multiplication. To perform dot product, you need to use np.dot.
Secondly, the shape of PCA.components_ is (n_components, n_features) while the shape of data to transform is (n_samples, n_features), so you need to transpose PCA.components_ to perform dot product.
Moreover, the first step of transform is to subtract the mean, therefore if you do it manually, you also need to subtract the mean at first.
The correct way to transform is
data_reduced = np.dot(data - pca.mean_, pca.components_.T)2) inverse_transform is just the inverse process of transform
data_original = np.dot(data_reduced, pca.components_) + pca.mean_If your data already has zero mean in each column, you can ignore the pca.mean_ above, for example
import numpy as npfrom sklearn.decomposition import PCApca = PCA(n_components=3)pca.fit(data)data_reduced = np.dot(data, pca.components_.T) # transformdata_original = np.dot(data_reduced, pca.components_) # inverse_transform