def test_copy(self):
# train model #1
metric = Metric({"name": "logloss"})
cat = CatBoostAlgorithm(self.params)
cat.fit(self.X, self.y)
y_predicted = cat.predict(self.X)
loss = metric(self.y, y_predicted)
# create model #2
cat2 = CatBoostAlgorithm(self.params)
# model #2 is initialized in constructor
self.assertTrue(cat2.model is not None)
# do a copy and use it for predictions
cat2 = cat.copy()
self.assertEqual(type(cat), type(cat2))
y_predicted = cat2.predict(self.X)
loss2 = metric(self.y, y_predicted)
self.assertEqual(loss, loss2)
# fit model #1, there should be improvement in loss
cat.fit(self.X, self.y)
y_predicted = cat.predict(self.X)
loss3 = metric(self.y, y_predicted)
self.assertTrue(loss3 < loss)
# the loss of model #2 should not change
y_predicted = cat2.predict(self.X)
loss4 = metric(self.y, y_predicted)
assert_almost_equal(loss2, loss4)
def test_copy(self):
# train model #1
metric = Metric({"name": "logloss"})
cat = CatBoostAlgorithm(self.params)
cat.fit(self.X, self.y)
y_predicted = cat.predict(self.X)
loss = metric(self.y, y_predicted)
# create model #2
cat2 = CatBoostAlgorithm(self.params)
# model #2 is initialized in constructor
self.assertTrue(cat2.model is not None)
# do a copy and use it for predictions
cat2 = cat.copy()
self.assertEqual(type(cat), type(cat2))
y_predicted = cat2.predict(self.X)
loss2 = metric(self.y, y_predicted)
self.assertEqual(loss, loss2)