def test_save_and_load(self):
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)
with tempfile.NamedTemporaryFile() as tmp:
cat.save(tmp.name)
cat2 = CatBoostAlgorithm(self.params)
self.assertTrue(cat.uid != cat2.uid)
self.assertTrue(cat2.model is not None)
cat2.load(tmp.name)
y_predicted = cat2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_save_and_load(self):
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)
filename = os.path.join(tempfile.gettempdir(), os.urandom(12).hex())
cat.save(filename)
cat2 = CatBoostAlgorithm(self.params)
self.assertTrue(cat.uid != cat2.uid)
self.assertTrue(cat2.model is not None)
cat2.load(filename)
# Finished with the file, delete it
os.remove(filename)
y_predicted = cat2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_save_and_load(self):
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)
json_desc = cat.save()
cat2 = CatBoostAlgorithm({})
self.assertTrue(cat.uid != cat2.uid)
self.assertTrue(cat2.model is not None)
cat2.load(json_desc)
self.assertTrue(cat.uid == cat2.uid)
y_predicted = cat2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
