Doing dimensionality reduction with manifolds – t-SNE

Getting ready

This is a short and practical recipe.

If you read the rest of the chapter, we have been doing a lot of dimensionality reduction with the iris dataset. Let's continue the pattern for additional easy comparisons. Load the iris dataset:

from sklearn.datasets import load_iris
iris = load_iris()
iris_X = iris.data
y = iris.target

Load PCA and some classes from the manifold module:

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE, MDS, Isomap

#Load visualization library
import matplotlib.pyplot as plt
%matplotlib inline

How to do it...

1. Run all the transforms on iris_X. One of the transforms is t-SNE:

iris_pca = PCA(n_components = 2).fit_transform(iris_X)
iris_tsne = TSNE(learning_rate=200).fit_transform(iris_X)

iris_MDS = MDS(n_components = 2).fit_transform(iris_X)
iris_ISO = Isomap(n_components = 2).fit_transform(iris_X)

2. Plot the results:

plt.figure(figsize=(20, 10))
plt.subplot(221)
plt.title('PCA')
plt.scatter...