def test_score(self):
"""
Assert returns R2 score
"""
visualizer = ResidualsPlot(Ridge(random_state=8893))
visualizer.fit(self.data.X.train, self.data.y.train)
score = visualizer.score(self.data.X.test, self.data.y.test)
assert score == pytest.approx(0.9999888484, rel=1e-4)
assert visualizer.train_score_ == pytest.approx(0.9999906, rel=1e-4)
assert visualizer.test_score_ == score
def test_either_hist_or_QQ_plot(self):
"""
Setting both hist=True and qqplot=True raises exception.
"""
with pytest.raises(YellowbrickValueError,
match="Set either hist or qqplot to False"):
ResidualsPlot(LinearRegression(), hist=True, qqplot=True)
def test_residuals_plot(self):
"""
Image similarity of residuals plot on random data with OLS
"""
_, ax = plt.subplots()
visualizer = ResidualsPlot(LinearRegression(), ax=ax)
visualizer.fit(self.data.X.train, self.data.y.train)
visualizer.score(self.data.X.test, self.data.y.test)
visualizer.finalize()
self.assert_images_similar(visualizer)
def test_hist_matplotlib_version(self, mock_toolkit):
"""
ValueError is raised when matplotlib version is incorrect and hist=True
"""
with pytest.raises(ImportError):
from mpl_toolkits.axes_grid1 import make_axes_locatable
assert not make_axes_locatable
with pytest.raises(YellowbrickValueError,
match="requires matplotlib 2.0.2"):
ResidualsPlot(LinearRegression(), hist=True)
def test_residuals_plot_no_histogram(self):
"""
Image similarity test when hist=False
"""
_, ax = plt.subplots()
model = MLPRegressor(random_state=19)
visualizer = ResidualsPlot(model, ax=ax, hist=False)
visualizer.fit(self.data.X.train, self.data.y.train)
visualizer.score(self.data.X.test, self.data.y.test)
visualizer.finalize()
self.assert_images_similar(visualizer, tol=1, remove_legend=True)
def test_no_hist_matplotlib_version(self, mock_toolkit):
"""
No error is raised when matplotlib version is incorrect and hist=False
"""
with pytest.raises(ImportError):
from mpl_toolkits.axes_grid1 import make_axes_locatable
assert not make_axes_locatable
try:
ResidualsPlot(LinearRegression(), hist=False)
except YellowbrickValueError as e:
self.fail(e)
def test_residuals_plot_numpy(self):
"""
Test NumPy real world dataset with image similarity on Lasso
"""
_, ax = plt.subplots()
# Load the occupancy dataset from fixtures
data = load_energy(return_dataset=True)
X, y = data.to_numpy()
# Create train/test splits
splits = tts(X, y, test_size=0.2, random_state=231)
X_train, X_test, y_train, y_test = splits
visualizer = ResidualsPlot(Lasso(random_state=44), ax=ax)
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.finalize()
self.assert_images_similar(visualizer, tol=1.5)
def test_alpha_param(self, mock_sca):
"""
Test that the user can supply an alpha param on instantiation
"""
# Instantiate a prediction error plot, provide custom alpha
visualizer = ResidualsPlot(Ridge(random_state=8893),
train_alpha=0.3,
test_alpha=0.75,
hist=False)
alphas = {"train_point": 0.3, "test_point": 0.75}
# Test param gets set correctly
assert visualizer.alphas == alphas
visualizer.ax = mock.MagicMock()
visualizer.fit(self.data.X.train, self.data.y.train)
visualizer.score(self.data.X.test, self.data.y.test)
# Test that alpha was passed to internal matplotlib scatterplot
_, scatter_kwargs = visualizer.ax.scatter.call_args
assert "alpha" in scatter_kwargs
assert scatter_kwargs["alpha"] == 0.75
def test_residuals_with_fitted(self):
"""
Test that ResidualsPlot properly handles an already-fitted model
"""
X, y = load_energy(return_dataset=True).to_numpy()
model = Ridge().fit(X, y)
with mock.patch.object(model, "fit") as mockfit:
oz = ResidualsPlot(model)
oz.fit(X, y)
mockfit.assert_not_called()
with mock.patch.object(model, "fit") as mockfit:
oz = ResidualsPlot(model, is_fitted=True)
oz.fit(X, y)
mockfit.assert_not_called()
with mock.patch.object(model, "fit") as mockfit:
oz = ResidualsPlot(model, is_fitted=False)
oz.fit(X, y)
mockfit.assert_called_once_with(X, y)