def test_get_metric_name(self):
params = {"objective": "binary:logistic", "eval_metric": "logloss"}
model = XgbAlgorithm(params)
self.assertEqual(model.get_metric_name(), "logloss")
params = {"eval_metric": "rmse"}
model = XgbAlgorithm(params)
self.assertEqual(model.get_metric_name(), "rmse")
def test_copy(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)
xgb2 = XgbAlgorithm(params)
self.assertTrue(
xgb2.model is None) # model is set to None, while initialized
xgb2 = xgb.copy()
self.assertEqual(type(xgb), type(xgb2))
y_predicted = xgb2.predict(self.X)
loss2 = metric(self.y, y_predicted)
self.assertEqual(loss, loss2)
self.assertNotEqual(id(xgb), id(xgb2))
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)
def test_fit_predict(self):
metric = Metric({"name": "logloss"})
params = {"objective": "binary:logistic", "eval_metric": "logloss"}
xgb = XgbAlgorithm(params)
loss_prev = None
for _ in range(5):
xgb.fit(self.X, self.y)
y_predicted = xgb.predict(self.X)
loss = metric(self.y, y_predicted)
if loss_prev is not None:
self.assertTrue(loss + 0.001 < loss_prev)
loss_prev = loss
def test_restricted_characters_in_feature_name(self):
df = pd.DataFrame({
"y": np.random.randint(0, 2, size=100),
"[test1]": np.random.uniform(0, 1, size=100),
"test2 < 1": np.random.uniform(0, 1, size=100),
})
y = df.iloc[:, 0]
X = df.iloc[:, 1:]
metric = Metric({"name": "logloss"})
params = {"objective": "binary:logistic", "eval_metric": "logloss"}
xgb = XgbAlgorithm(params)
xgb.fit(X, y)
xgb.predict(X)
def test_reproduce_fit(self):
metric = Metric({"name": "logloss"})
params = {
"objective": "binary:logistic",
"eval_metric": "logloss",
"seed": 1
}
prev_loss = None
for _ in range(3):
xgb = XgbAlgorithm(params)
xgb.fit(self.X, self.y)
y_predicted = xgb.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_is_fitted(self):
params = {"objective": "binary:logistic", "eval_metric": "logloss"}
model = XgbAlgorithm(params)
self.assertFalse(model.is_fitted())
model.fit(self.X, self.y)
self.assertTrue(model.is_fitted())