PCA and t-SNE for dimensionality reduction and inspection

                  from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler

X_scaled = StandardScaler().fit_transform(X)

pca = PCA(n_components=10, random_state=42)
X_pca = pca.fit_transform(X_scaled)
print('explained variance:', pca.explained_variance_ratio_.sum())

tsne = TSNE(n_components=2, perplexity=30, init='pca', learning_rate='auto', random_state=42)
X_tsne = tsne.fit_transform(X_scaled[:2000])
print(X_tsne[:5])