'KMeans' object has no attribute 'k'

When trying to run example:

from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import KMeans
model = KElbowVisualizer(KMeans(), k=10)
model.fit(X)

The following error appears:

AttributeError: 'KMeans' object has no attribute 'k'

[nipnipj]

This example worked for me now:

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.cluster import KElbowVisualizer

X, y = make_blobs(n_samples=1000, n_features=12, centers=8, random_state=42)

model = KMeans()
visualizer = KElbowVisualizer(model, k=(4,12))

visualizer.fit(X)
visualizer.show()

[shayandavoodii]

@nipnipj @shayandavoodii glad to hear the v1.5 update fixed things!

@shayandavoodii Jupyter notebooks will automatically render figures that were created in the cell above; that’s why both the estimator description figure and the partial K-Elbow figure are visible. Some advice on how to prevent this can be found in this StackOverflow post. Note that without the call to show the Yellowbrick figure doesn’t have a title, axes names, legend etc. If you want to render these without calling show you can call finalize instead.

Thanks. Yes, I was aware of them. Actually, I don’t need the plot. Thanks.