def test_parameters_access(self):
self._init_ray()
from sklearn import datasets
params = {"updater": "grow_gpu_hist", "subsample": 0.5, "n_jobs": -1}
clf = RayXGBClassifier(n_estimators=1000, **params)
assert clf.get_params()["updater"] == "grow_gpu_hist"
assert clf.get_params()["subsample"] == 0.5
assert clf.get_params()["n_estimators"] == 1000
clf = RayXGBClassifier(n_estimators=1, nthread=4)
X, y = datasets.load_iris(return_X_y=True)
clf.fit(X, y)
config = json.loads(clf.get_booster().save_config())
assert int(config["learner"]["generic_param"]["nthread"]) == 4
clf.set_params(nthread=16)
config = json.loads(clf.get_booster().save_config())
assert int(config["learner"]["generic_param"]["nthread"]) == 16
clf.predict(X)
config = json.loads(clf.get_booster().save_config())
assert int(config["learner"]["generic_param"]["nthread"]) == 16
def test_multiclass_classification(self):
self._init_ray()
from sklearn.datasets import load_iris
from sklearn.model_selection import KFold
def check_pred(preds, labels, output_margin):
if output_margin:
err = sum(1 for i in range(len(preds))
if preds[i].argmax() != labels[i]) / float(
len(preds))
else:
err = sum(1 for i in range(len(preds))
if preds[i] != labels[i]) / float(len(preds))
assert err < 0.4
iris = load_iris()
y = iris["target"]
X = iris["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().fit(X[train_index], y[train_index])
if hasattr(xgb_model.get_booster(), "num_boosted_rounds"):
assert (xgb_model.get_booster().num_boosted_rounds() ==
xgb_model.n_estimators)
preds = xgb_model.predict(X[test_index])
# test other params in XGBClassifier().fit
preds2 = xgb_model.predict(
X[test_index], output_margin=True, ntree_limit=3)
preds3 = xgb_model.predict(
X[test_index], output_margin=True, ntree_limit=0)
preds4 = xgb_model.predict(
X[test_index], output_margin=False, ntree_limit=3)
labels = y[test_index]
check_pred(preds, labels, output_margin=False)
check_pred(preds2, labels, output_margin=True)
check_pred(preds3, labels, output_margin=True)
check_pred(preds4, labels, output_margin=False)
cls = RayXGBClassifier(n_estimators=4).fit(X, y)
assert cls.n_classes_ == 3
proba = cls.predict_proba(X)
assert proba.shape[0] == X.shape[0]
assert proba.shape[1] == cls.n_classes_
# custom objective, the default is multi:softprob
# so no transformation is required.
cls = RayXGBClassifier(
n_estimators=4, objective=softprob_obj(3)).fit(X, y)
proba = cls.predict_proba(X)
assert proba.shape[0] == X.shape[0]
assert proba.shape[1] == cls.n_classes_
def test_XGBClassifier_resume(self):
self._init_ray()
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import log_loss
with TemporaryDirectory() as tempdir:
model1_path = os.path.join(tempdir, "test_XGBClassifier.model")
model1_booster_path = os.path.join(tempdir,
"test_XGBClassifier.booster")
X, Y = load_breast_cancer(return_X_y=True)
model1 = RayXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8)
model1.fit(X, Y)
pred1 = model1.predict(X)
log_loss1 = log_loss(pred1, Y)
# file name of stored xgb model
model1.save_model(model1_path)
model2 = RayXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8)
model2.fit(X, Y, xgb_model=model1_path)
pred2 = model2.predict(X)
log_loss2 = log_loss(pred2, Y)
assert np.any(pred1 != pred2)
assert log_loss1 > log_loss2
# file name of 'Booster' instance Xgb model
model1.get_booster().save_model(model1_booster_path)
model2 = RayXGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8)
model2.fit(X, Y, xgb_model=model1_booster_path)
pred2 = model2.predict(X)
log_loss2 = log_loss(pred2, Y)
assert np.any(pred1 != pred2)
assert log_loss1 > log_loss2
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)
