def test_reproduce_fit_regression(self):
metric = Metric({"name": "rmse"})
prev_loss = None
for _ in range(3):
model = BaselineRegressorAlgorithm({"ml_task": "regression"})
model.fit(self.X, self.y)
y_predicted = model.predict(self.X)
loss = metric(self.y, y_predicted)
if prev_loss is not None:
assert_almost_equal(prev_loss, loss)
prev_loss = loss
def test_save_and_load(self):
metric = Metric({"name": "rmse"})
dt = BaselineRegressorAlgorithm({"ml_task": "regression"})
dt.fit(self.X, self.y)
y_predicted = dt.predict(self.X)
loss = metric(self.y, y_predicted)
with tempfile.NamedTemporaryFile() as tmp:
dt.save(tmp.name)
dt2 = BaselineRegressorAlgorithm({"ml_task": "regression"})
dt2.load(tmp.name)
y_predicted = dt2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_save_and_load(self):
metric = Metric({"name": "rmse"})
dt = BaselineRegressorAlgorithm({"ml_task": "regression"})
dt.fit(self.X, self.y)
y_predicted = dt.predict(self.X)
loss = metric(self.y, y_predicted)
filename = os.path.join(tempfile.gettempdir(), os.urandom(12).hex())
dt.save(filename)
dt2 = BaselineRegressorAlgorithm({"ml_task": "regression"})
dt2.load(filename)
# Finished with the file, delete it
os.remove(filename)
y_predicted = dt2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_is_fitted(self):
model = BaselineRegressorAlgorithm({"ml_task": "regression"})
self.assertFalse(model.is_fitted())
model.fit(self.X, self.y)
self.assertTrue(model.is_fitted())