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An interactive widget for Jupyter notebooks to explore the parameters of t-SNE.



pip install outerspace

Additionally, if you use jupyter notebook:

jupyter nbextension enable --py widgetsnbextension

... or for jupyter lab:

jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install @bokeh/jupyter_bokeh


Run t-SNE on the digits data set (see result in the image above):

from outerspace import tsne_playground
from sklearn.datasets import load_digits
digits = load_digits()
X, y =,
tsne_playground(X, y)

Show the actual digit images in a tooltip:

from outerspace import tsne_playground, array2d_to_html_img
from sklearn.datasets import load_digits

digits = load_digits()
X, y, images =,, digits.images

images = 256 - images * 16      # convert range and invert
images = images.clip(0, 255)    # clip values at 255
images = images.astype('uint8') # convert to uint8
images = [array2d_to_html_img(image, resize=(32,32))
          for image in images]  # convert to HTML images

tsne_playground(X, y,
                tooltips='@images{safe}') # safe = don't escape HTML

Further examples

Evaluating the chemical space of a set of molecules (with molecule images as tooltip):

from outerspace import tsne_playground, pil_to_html_img
from rdkit.Chem import SDMolSupplier, Draw, AllChem
import requests
import numpy as np

url = (''
response = requests.get(url)

supplier = SDMolSupplier()
ms = [m for m in supplier]

X = np.array([list(AllChem.GetMACCSKeysFingerprint(m)) for m in ms])
y = [m.GetProp('SOL_classification') for m in ms]
images = [pil_to_html_img(Draw.MolToImage(m, size=(150, 150))) 
          for m in ms]

tsne_playground(X, y, 

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