def test_timings(self):
"""
Test the twinx double axes with k-elbow timings
"""
visualizer = KElbowVisualizer(
KMeans(random_state=0), k=5, timings=True
)
visualizer.fit(X)
# Check that we kept track of time
assert len(visualizer.k_timers_) == 4
assert all([t > 0 for t in visualizer.k_timers_])
# Check that we plotted time on a twinx
assert hasattr(visualizer, "axes")
assert len(visualizer.axes) == 2
# delete the timings axes and
# overwrite k_timers_, k_values_ for image similarity Tests
visualizer.axes[1].remove()
visualizer.k_timers_ = [
0.01084589958190918, 0.011144161224365234,
0.017028093338012695, 0.010634183883666992
]
visualizer.k_values_ = [2, 3, 4, 5]
# call draw again which is normally called in fit
visualizer.draw()
visualizer.poof()
self.assert_images_similar(visualizer)
def test_timings(self):
"""
Test the twinx double axes with k-elbow timings
"""
visualizer = KElbowVisualizer(KMeans(random_state=0),
k=5,
timings=True,
locate_elbow=False)
visualizer.fit(self.clusters.X)
# Check that we kept track of time
assert len(visualizer.k_timers_) == 4
assert all([t > 0 for t in visualizer.k_timers_])
# Check that we plotted time on a twinx
assert hasattr(visualizer, "axes")
assert len(visualizer.axes) == 2
# delete the timings axes and
# overwrite k_timers_, k_values_ for image similarity Tests
visualizer.axes[1].remove()
visualizer.k_timers_ = [
0.01084589958190918,
0.011144161224365234,
0.017028093338012695,
0.010634183883666992,
]
visualizer.k_values_ = [2, 3, 4, 5]
# call draw again which is normally called in fit
visualizer.draw()
visualizer.finalize()
self.assert_images_similar(visualizer)
def test_set_colors_manually(self):
"""
Test the silhouette metric of the k-elbow visualizer
"""
oz = KElbowVisualizer(
KMeans(random_state=0),
k=5,
)
oz.metric_color = "r"
oz.timing_color = "y"
oz.vline_color = "c"
# Create artificial "fit" data for testing purposes
oz.k_values_ = [1, 2, 3, 4, 5, 6, 7, 8]
oz.k_timers_ = [6.2, 8.3, 10.1, 15.8, 21.2, 27.9, 38.2, 44.9]
oz.k_scores_ = [.8, .7, .55, .48, .40, .38, .35, .30]
oz.elbow_value_ = 5
oz.elbow_score_ = 0.40
# Execute drawing
oz.draw()
oz.finalize()
self.assert_images_similar(oz, tol=3.2)
