text: import numpy as np
  import seaborn as sns
  import umap.umap_ as umap
  import matplotlib.pyplot as plt
  from sklearn.datasets import load_iris
  
  # data
  iris = load_iris()
  
  # define model
  reducer = umap.UMAP()
  
  # train model
  embedding = reducer.fit_transform(iris.data)
  
  plt.scatter(embedding[:, 0], embedding[:, 1], c=[sns.color_palette()[x] for x in iris.target])
  plt.gca().set_aspect('equal', 'datalim')
  plt.title('UMAP projection of the Iris dataset', fontsize=24);
  plt.show()
  
  