def test_integrated_mini_batch_kmeans_silhouette(self):
"""
Test no exceptions for mini-batch kmeans silhouette visualizer
"""
# NOTE see #182: cannot use occupancy dataset because of memory usage
# Generate a blobs data set
X, y = make_blobs(n_samples=1000,
n_features=12,
centers=8,
shuffle=False,
random_state=0)
try:
fig = plt.figure()
ax = fig.add_subplot()
visualizer = SilhouetteVisualizer(MiniBatchKMeans(random_state=0),
ax=ax)
visualizer.fit(X)
visualizer.poof()
self.assert_images_similar(visualizer)
except Exception as e:
self.fail("error during silhouette: {}".format(e))
def test_colormap_as_colors_silhouette(self):
"""
Test no exceptions for modifying the colors in a silhouette visualizer
by using a matplotlib colormap as colors
"""
# Generate a blobs data set
X, y = make_blobs(n_samples=1000,
n_features=12,
centers=8,
shuffle=False,
random_state=0)
try:
fig = plt.figure()
ax = fig.add_subplot()
visualizer = SilhouetteVisualizer(MiniBatchKMeans(random_state=0),
ax=ax,
colors="cool")
visualizer.fit(X)
visualizer.finalize()
tol = (3.2 if sys.platform == "win32" else 0.01
) # Fails on AppVeyor with RMS 3.143
self.assert_images_similar(visualizer, remove_legend=True, tol=tol)
except Exception as e:
self.fail("error during silhouette: {}".format(e))
def test_colors_silhouette(self):
"""
Test no exceptions for modifying the colors in a silhouette visualizer
with a list of color names
"""
# Generate a blobs data set
X, y = make_blobs(n_samples=1000,
n_features=12,
centers=8,
shuffle=False,
random_state=0)
try:
fig = plt.figure()
ax = fig.add_subplot()
visualizer = SilhouetteVisualizer(
MiniBatchKMeans(random_state=0),
ax=ax,
colors=["red", "green", "blue", "indigo", "cyan", "lavender"],
)
visualizer.fit(X)
visualizer.finalize()
self.assert_images_similar(visualizer, remove_legend=True)
except Exception as e:
self.fail("error during silhouette: {}".format(e))
def test_integrated_mini_batch_kmeans_silhouette(self):
"""
Test no exceptions for mini-batch kmeans silhouette visualizer
"""
# NOTE see #182: cannot use occupancy dataset because of memory usage
# Generate a blobs data set
X, y = make_blobs(
n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0
)
try:
fig = plt.figure()
ax = fig.add_subplot()
visualizer = SilhouetteVisualizer(MiniBatchKMeans(random_state=0), ax=ax)
visualizer.fit(X)
visualizer.poof()
self.assert_images_similar(visualizer)
except Exception as e:
self.fail("error during silhouette: {}".format(e))
def test_integrated_yb_colormap(self):
"""
Assert silhouette plot colormap can be set with a yellowbrick palette
"""
# Generate a blobs data set
X, y = make_blobs(n_samples=1000,
n_features=12,
centers=8,
shuffle=False,
random_state=0)
visualizer = SilhouetteVisualizer(KMeans(random_state=0),
colormap="neural_paint")
visualizer.fit(X)
visualizer.finalize()
tol = (3.2 if sys.platform == "win32" else 0.01
) # Fails on AppVeyor with RMS 3.143
self.assert_images_similar(visualizer, remove_legend=True, tol=tol)
def test_integrated_mini_batch_kmeans_silhouette(self):
"""
Test no exceptions for mini-batch kmeans silhouette visualizer
See #182: cannot use occupancy dataset because of memory usage
"""
# Generate a blobs data set
X, y = make_blobs(
n_samples=1000,
n_features=12,
centers=8,
shuffle=True,
)
try:
visualizer = SilhouetteVisualizer(MiniBatchKMeans())
visualizer.fit(X)
visualizer.poof()
except Exception as e:
self.fail("error during silhouette: {}".format(e))
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)
