import pylab as plt
import numpy as np

from sklearn.datasets import load_iris
from sklearn.manifold import ClassicalMDS, MDS
from sklearn.decomposition import PCA

X, y = load_iris(return_X_y=True)

Z1 = PCA(n_components=2).fit_transform(X)
Z2 = ClassicalMDS(n_components=2, dissimilarity="euclidean").fit_transform(X)
Z3 = ClassicalMDS(n_components=2, dissimilarity="cosine").fit_transform(X)
Z4 = ClassicalMDS(n_components=2, dissimilarity="manhattan").fit_transform(X)

fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(6, 6), layout="constrained")

axs.flat[0].scatter(Z1[:,0], Z1[:,1], c=y)
axs.flat[0].set_title("PCA")

axs.flat[1].scatter(Z2[:,0], Z2[:,1], c=y)
axs.flat[1].set_title("Classical MDS, Euclidean dist.")

axs.flat[2].scatter(-Z3[:,0], Z3[:,1], c=y)
axs.flat[2].set_title("Classical MDS, cosine dist.")

axs.flat[3].scatter(Z4[:,0], Z4[:,1], c=y)
axs.flat[3].set_title("Classical MDS, Manhattan dist.")