Solved – sklearn::PCA, Inverse transform(transform(X)) = X

pcascikit learn

I want to know why doing inverse_transform(transform(X)) $\ne$ X?
In the below code, I do the following:

I import the iris dataset, drop the target, select three samples. Fit the full data to a PCA with 2 components.
Then do a transform of the sample followed by an inverse transform.

The samples look like:

        sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)

         5.1000            3.5000             1.4000            0.2

         4.9000            3.0000             1.4000            0.2

         4.7000            3.2000             1.3000            0.2

Inverse transform looks like this: 

[[5.08303897 3.51741393 1.40321372 0.21353169]

 [4.7462619  3.15749994 1.46356177 0.24024592]

 [4.70411871 3.1956816  1.30821697 0.17518015]]

They dont appear the same. Specifically look at row 2, column 1. This doesnt seem like a rounding error. What am I doing wrong?

Thanks

    import pandas as pd
    import numpy as np
    from sklearn import datasets
    from sklearn.decomposition import PCA
    import seaborn as sns; sns.set()

    iris = datasets.load_iris()
    data = iris.data
    pd.options.display.float_format = '{:,.4f}'.format
    data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
    columns= iris['feature_names'] + ['target'])
    data1 = data1.iloc[:,0:4]
    print(data1.shape)
    samples = pd.DataFrame(data1.loc[[0,1,2]], columns = data1.keys()).reset_index(drop = True)
    print(samples)
    pca = PCA(n_components=2)
    pca.fit(data1)
    pca_data1 = pca.transform(data1)
    pca_samples = pca.transform(samples)
    print(pca.inverse_transform(pca_samples))

Best Answer

Expanding on the comments, the misunderstanding originates in the nature of data reduction. For some matrix with rank $r$, if you only retain the $k<r$ largest PCs, then you will not have a perfect reconstruction because the remaining $r-k$ dimensions are discarded. More information: Does PCA's reconstruction error get reduced with more PCs being used?

Therefore, the PCA transform and inverse_transform are only exactly inverses in the case that $k \ge r$; otherwise, data is irrevocably lost.

Intuitively, this makes sense. If we have data that exists in three dimensions (i.e. has rank 3), but we approximate it using only 2 PCs, then we will lose all variation in that third direction.

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