UMAP

import numpy as np
from sklearn.datasets import load_iris, load_digits
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
sns.set(style='white', context='notebook', rc={'figure.figsize':(14,10)})

iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = pd.Series(iris.target).map(dict(zip(range(3),iris.target_names)))
sns.pairplot(iris_df, hue='species')

import umap
reducer = umap.UMAP()

embedding = reducer.fit_transform(iris.data)
embedding.shape

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)