def test_save_load_model(self):
self._init_ray()
with TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, "digits.model")
self.save_load_model(model_path)
with TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, "digits.model.json")
self.save_load_model(model_path)
from sklearn.datasets import load_digits
with TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, "digits.model.json")
digits = load_digits(n_class=2)
y = digits["target"]
X = digits["data"]
booster = xgb.train(
{
"tree_method": "hist",
"objective": "binary:logistic"
},
dtrain=xgb.DMatrix(X, y),
num_boost_round=4,
)
predt_0 = booster.predict(xgb.DMatrix(X))
booster.save_model(model_path)
cls = RayXGBClassifier()
cls.load_model(model_path)
proba = cls.predict_proba(X)
assert proba.shape[0] == X.shape[0]
assert proba.shape[1] == 2 # binary
predt_1 = cls.predict_proba(X)[:, 1]
assert np.allclose(predt_0, predt_1)
cls = xgb.XGBModel()
cls.load_model(model_path)
predt_1 = cls.predict(X)
assert np.allclose(predt_0, predt_1)
def test_estimator_type(self):
self._init_ray()
assert RayXGBClassifier._estimator_type == "classifier"
assert RayXGBRFClassifier._estimator_type == "classifier"
assert RayXGBRegressor._estimator_type == "regressor"
assert RayXGBRFRegressor._estimator_type == "regressor"
assert RayXGBRanker._estimator_type == "ranker"
from sklearn.datasets import load_digits
X, y = load_digits(n_class=2, return_X_y=True)
cls = RayXGBClassifier(n_estimators=2).fit(X, y)
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, "cls.json")
cls.save_model(path)
reg = RayXGBRegressor()
with self.assertRaises(TypeError):
reg.load_model(path)
cls = RayXGBClassifier()
cls.load_model(path) # no error
def save_load_model(self, model_path):
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
digits = load_digits(n_class=2)
y = digits["target"]
X = digits["data"]
kf = KFold(n_splits=2, shuffle=True, random_state=self.rng)
for train_index, test_index in kf.split(X, y):
xgb_model = RayXGBClassifier(use_label_encoder=False).fit(
X[train_index], y[train_index])
xgb_model.save_model(model_path)
xgb_model = RayXGBClassifier()
xgb_model.load_model(model_path)
assert xgb_model.use_label_encoder is False
assert isinstance(xgb_model.classes_, np.ndarray)
assert isinstance(xgb_model._Booster, xgb.Booster)
preds = xgb_model.predict(X[test_index])
labels = y[test_index]
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1
assert xgb_model.get_booster().attr("scikit_learn") is None
# test native booster
preds = xgb_model.predict(X[test_index], output_margin=True)
booster = xgb.Booster(model_file=model_path)
predt_1 = booster.predict(
xgb.DMatrix(X[test_index]), output_margin=True)
assert np.allclose(preds, predt_1)
with self.assertRaises(TypeError):
xgb_model = xgb.XGBModel()
xgb_model.load_model(model_path)
