Quick-start
Now, let’s go through a quick start!
TopOMetry functions around
From a data matrix data
(np.ndarray, pd.DataFrame or sp.csr_matrix), you can set up a TopoGraph
with default parameters:
import topo as tp
# Learn topological metrics and basis from data. The default is to use diffusion harmonics.
tg = tp.TopOGraph()
tg.fit(data)
After learning a topological basis, we can access topological metrics and basis in the TopOGraph
object, and build different
topological graphs.
# Learn a topological graph. Again, the default is to use diffusion harmonics.
tgraph = tg.transform(data)
Then, it is possible to optimize the topological graph layout. TopOMetry has 5 different layout options: tSNE, MAP, TriMAP, PaCMAP and MDE.
# Graph layout optimization
map_emb = tg.MAP()
mde_emb = tg.MDE()
pacmap_emb = tg.PaCMAP()
trimap_emb = tg.TriMAP()
tsne_emb = tg.tSNE()
We can also plot the embeddings:
tp.plot.scatter(map_emb)