PCA vs IncrementalPCA

isIncrementalPCAGood.py

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import load_digits
from sklearn.decomposition import PCA, IncrementalPCA

iris = load_iris()
X = iris.data
y = iris.target

n_components = 2
ipca = IncrementalPCA(n_components=n_components, batch_size=3)
X_ipca = ipca.fit_transform(X)

pca = PCA(n_components=n_components)
X_pca = pca.fit_transform(X)


rev_ipca = ipca.inverse_transform(X_ipca)
rev_pca = pca.inverse_transform(X_pca)

print(X.shape)
print("icrementalPCA.inv(x) MAE with X: ", np.abs(rev_ipca - X).mean())
print("PCA.inv(x) MAE with X:  ", np.abs(rev_pca - X).mean())


# icrementalPCA.inv(x) MAE with X:  2.4990431209038713
# PCA.inv(x) MAE with X:            2.479100754678159

colors = ['navy', 'turquoise', 'darkorange']

for X_transformed, title in [(X_ipca, "Incremental PCA"), (X_pca, "PCA")]:
    plt.figure(figsize=(8, 8))
    for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):
        plt.scatter(X_transformed[y == i, 0], X_transformed[y == i, 1],
                    color=color, lw=2, label=target_name)

    if "Incremental" in title:
        err = np.abs(X_pca - X_ipca).mean()
        plt.title(title + " of iris dataset\nMean absolute error "
                  "%.6f" % err)
    else:
        plt.title(title + " of iris dataset")
    plt.legend(loc="best", shadow=False, scatterpoints=1)
    plt.axis([-4, 4, -1.5, 1.5])

plt.show()