def test_score(self):
"""
Assert returns R2 score
"""
visualizer = PredictionError(LinearRegression())
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(0.9999983124154965)
assert visualizer.score_ == score
def test_peplot_no_lines(self):
"""
Test image similarity with no lines drawn on the plot
"""
visualizer = PredictionError(Lasso(random_state=23, alpha=10),
bestfit=False,
identity=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=1.0, remove_legend=True)
def test_peplot_shared_limits(self):
"""
Test shared limits on the peplot
"""
visualizer = PredictionError(LinearRegression(), shared_limits=False)
visualizer.fit(self.data.X.train, self.data.y.train)
visualizer.score(self.data.X.test, self.data.y.test)
visualizer.finalize()
xlim = tuple(map(int, visualizer.ax.get_xlim()))
ylim = tuple(map(int, visualizer.ax.get_ylim()))
assert xlim == ylim
def test_prediction_error(self):
"""
Test image similarity of prediction error on random data
"""
_, ax = plt.subplots()
model = MLPRegressor(random_state=229)
visualizer = PredictionError(model, 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, tol=1, remove_legend=True)
def test_peplot_no_shared_limits(self):
"""
Test image similarity with no shared limits on the peplot
"""
visualizer = PredictionError(Ridge(random_state=43),
shared_limits=False)
visualizer.fit(self.data.X.train, self.data.y.train)
visualizer.score(self.data.X.test, self.data.y.test)
visualizer.finalize()
xlim = tuple(map(int, visualizer.ax.get_xlim()))
ylim = tuple(map(int, visualizer.ax.get_ylim()))
assert not xlim == ylim
self.assert_images_similar(visualizer, tol=1.0, remove_legend=True)
def test_prediction_error_pandas(self):
"""
Test Pandas real world dataset with image similarity on Ridge
"""
_, ax = plt.subplots()
# Load the occupancy dataset from fixtures
data = load_energy(return_dataset=True)
X, y = data.to_pandas()
# Create train/test splits
splits = tts(X, y, test_size=0.2, random_state=8873)
X_train, X_test, y_train, y_test = splits
visualizer = PredictionError(Ridge(random_state=22), ax=ax)
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.finalize()
self.assert_images_similar(visualizer, tol=1, remove_legend=True)
def test_alpha_param(self):
"""
Test that the user can supply an alpha param on instantiation
"""
# Instantiate a sklearn regressor
model = Lasso(random_state=23, alpha=10)
# Instantiate a prediction error plot, provide custom alpha
visualizer = PredictionError(model,
bestfit=False,
identity=False,
alpha=0.7)
# Test param gets set correctly
assert visualizer.alpha == 0.7
# Mock ax and fit the visualizer
visualizer.ax = mock.MagicMock(autospec=True)
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"] == 0.7
def test_peplot_with_fitted(self):
"""
Test that PredictionError 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 = PredictionError(model)
oz.fit(X, y)
mockfit.assert_not_called()
with mock.patch.object(model, "fit") as mockfit:
oz = PredictionError(model, is_fitted=True)
oz.fit(X, y)
mockfit.assert_not_called()
with mock.patch.object(model, "fit") as mockfit:
oz = PredictionError(model, is_fitted=False)
oz.fit(X, y)
mockfit.assert_called_once_with(X, y)