This PR fixes #1303, which reported that they could not use GMM as a clustering model with Silhouette Visualizer.
They received this traceback:
yellowbrick.exceptions.YellowbrickTypeError: The supplied model is not a clustering estimator; try a classifier or regression score visualizer instead!

Once I resolved the above issue, I encountered another problem with GMM not having a n_clusters attribute on the estimator.

I have made the following changes:

1. I added a new is_density function to the utils/types file
2. I then used the is_density function with the ClusteringScoreVisualizer Class to allow for DensityEstimators to be used by this class
   3. I fixed the attribute error by using a try/except clause to setself.n_clusters_ equal to self.estimator.n_components in silhouette.py file
3. Checked if self.estimator has the n_components attribute that the Density Estimator possesses and set self.n_clusters_ to self.estimator.n_components

Sample Code

from sklearn.mixture import GaussianMixture as GMM

from yellowbrick.cluster import SilhouetteVisualizer
from sklearn.datasets import make_blobs

X, y = make_blobs(
n_samples=1000, n_features=12, centers=5, shuffle=False, random_state=0
)

Instantiate the clustering model and visualizer
model = GMM(n_components = 5, random_state=0)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')

visualizer.fit(X) # Fit the data to the visualizer
visualizer.show() # Finalize and render the figure

PLOT

Questions for the @DistrictDataLabs/team-oz-maintainers:

• Is the try/except clause a viable solution for missing attributes? I foresee this being an issue because I came across a different attribute error with a different clustering estimator. This could get unwieldy.
• [ ]

CHECKLIST