Lensed UMAP
This lensed_umap package provides three methods that apply lens-functions
to a UMAP model. Lens functions can be used to untangle embeddings along a particular
dimension. This dimension may be part of the data, or come from another information
source. Using lens functions, analysts can update their UMAP models to the questions
they are investigating, effectively viewing their data from different perspectives.
Usage example:
import numpy as np
import pandas as pd
from umap import UMAP
import lensed_umap as lu
import matplotlib.pyplot as plt
# Load data and extract lens
df = pd.read_csv("./data/five_circles.csv", header=0)
lens = np.log(df.hue)
# Compute initial UMAP model
projector = UMAP(
repulsion_strength=0.1, # To avoid tears in projection that
negative_sample_rate=2, # are not in the modelled graph!
).fit(df[["x", "y"]])
# Draw intial model
x, y = lu.extract_embedding(projector)
plt.scatter(x, y, 2, lens, cmap="viridis")
plt.axis("off")
plt.show()
# Apply a global lens
lensed = lu.apply_lens(projector, lens, resolution=6)
x, y = lu.extract_embedding(lensed)
plt.scatter(x, y, 2, lens, cmap="viridis")
plt.axis("off")
plt.show()
