def test_learning_curve_model_only(self):
"""
Test learning curve with inputting model only.
"""
try:
visualizer = LearningCurveVisualizer(LinearSVC())
visualizer.fit(X, y)
visualizer.poof()
except Exception as e:
self.fail("error during learning curve: {}".format(e))
def test_learning_curve_bad_trainsize(self):
"""
Test learning curve with bad input for training size.
"""
with self.assertRaises(YellowbrickError):
visualizer = LearningCurveVisualizer(LinearSVC(),
train_sizes=10000,
cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0))
visualizer.fit(X, y)
visualizer.poof()
def test_learning_curve_model_cv_only(self):
"""
Test learning curve with inputting model and cv only.
"""
try:
visualizer = LearningCurveVisualizer(LinearSVC(),
cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0))
visualizer.fit(X, y)
visualizer.poof()
except Exception as e:
self.fail("error during learning curve: {}".format(e))
def test_learning_curve_comprehensive(self):
"""
Test learning curve with all parameters with visual unit test.
"""
try:
visualizer = LearningCurveVisualizer(LinearSVC(random_state=0), train_sizes=np.linspace(.1, 1.0, 5),
cv=ShuffleSplit(n_splits=100, test_size=0.2, random_state=0),
n_jobs=4)
visualizer.fit(X, y)
visualizer.poof()
except Exception as e:
self.fail("error during learning curve: {}".format(e))
self.assert_images_similar(visualizer)