UMAP clearly explained
Basic UMAP Parameters
UMAP is a fairly flexible non-linear dimension reduction algorithm. It seeks to learn the manifold structure of your data and find a low dimensional embedding that preserves the essential topological structure of that manifold. In this notebook we will generate some visualisable 4-dimensional data, demonstrate how to use UMAP to provide a 2-dimensional representation of it, and then look at how various UMAP parameters can impact the resulting embedding. This documentation is based on the work of Philippe Rivière for visionscarto.net.
To start we’ll need some basic libraries. First numpy will be needed for basic array manipulation. Since we will be visualising the results we will need matplotlib and seaborn. Finally we will need umap for doing the dimension reduction itself.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import umap
%matplotlib inlinesns.set(style='white', context='poster', rc={'figure.figsize':(14,10)})
Next we will need some data to embed into a lower dimensional representation. To make the 4-dimensional data “visualisable” we will generate data uniformly at random from a 4-dimensional cube such that we can interpret a sample as a tuple of (R,G,B,a) values specifying a color (and translucency). Thus when we plot low dimensional representations each point can be colored according to its 4-dimensional value. For this we can use numpy. We will fix a…
