def test_with_fitted(self):
"""
Test that visualizer properly handles an already-fitted model
"""
X, y = load_nfl(return_dataset=True).to_numpy()
model = MiniBatchKMeans().fit(X, y)
with mock.patch.object(model, "fit") as mockfit:
oz = SilhouetteVisualizer(model)
oz.fit(X, y)
mockfit.assert_not_called()
with mock.patch.object(model, "fit") as mockfit:
oz = SilhouetteVisualizer(model, is_fitted=True)
oz.fit(X, y)
mockfit.assert_not_called()
with mock.patch.object(model, "fit") as mockfit:
oz = SilhouetteVisualizer(model, is_fitted=False)
oz.fit(X, y)
mockfit.assert_called_once_with(X, y)
from yellowbrick.cluster import SilhouetteVisualizer
from yellowbrick.datasets import load_nfl
from sklearn.cluster import KMeans
# Load a clustering dataset
X, y = load_nfl()
# Specify the features to use for clustering
features = ['Rec', 'Yds', 'TD', 'Fmb', 'Ctch_Rate']
X = X.query('Tgt >= 20')[features]
# Instantiate the clustering model and visualizer
model = KMeans(5, random_state=42)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(X) # Fit the data to the visualizer
visualizer.show() # Finalize and render the figure