def test_save_and_load(self):
metric = Metric({"name": "logloss"})
params = {"objective": "binary:logistic", "eval_metric": "logloss"}
xgb = XgbAlgorithm(params)
xgb.fit(self.X, self.y)
y_predicted = xgb.predict(self.X)
loss = metric(self.y, y_predicted)
with tempfile.NamedTemporaryFile() as tmp:
xgb.save(tmp.name)
xgb2 = XgbAlgorithm(params)
self.assertTrue(xgb2.model is None)
xgb2.load(tmp.name)
y_predicted = xgb2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_save_and_load(self):
metric = Metric({"name": "logloss"})
params = {"objective": "binary:logistic", "eval_metric": "logloss"}
xgb = XgbAlgorithm(params)
xgb.fit(self.X, self.y)
y_predicted = xgb.predict(self.X)
loss = metric(self.y, y_predicted)
filename = os.path.join(tempfile.gettempdir(), os.urandom(12).hex())
xgb.save(filename)
xgb2 = XgbAlgorithm(params)
self.assertTrue(xgb2.model is None)
xgb2.load(filename)
# Finished with the file, delete it
os.remove(filename)
y_predicted = xgb2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
