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_pandas_input(self):
self._init_ray()
import pandas as pd
from sklearn.calibration import CalibratedClassifierCV
rng = np.random.RandomState(self.seed)
kRows = 100
kCols = 6
X = rng.randint(low=0, high=2, size=kRows * kCols)
X = X.reshape(kRows, kCols)
df = pd.DataFrame(X)
feature_names = []
for i in range(1, kCols):
feature_names += ["k" + str(i)]
df.columns = ["status"] + feature_names
target = df["status"]
train = df.drop(columns=["status"])
model = RayXGBClassifier()
model.fit(train, target)
clf_isotonic = CalibratedClassifierCV(
model, cv="prefit", method="isotonic")
clf_isotonic.fit(train, target)
assert isinstance(
clf_isotonic.calibrated_classifiers_[0].base_estimator,
RayXGBClassifier,
)
self.assertTrue(
np.allclose(np.array(clf_isotonic.classes_), np.array([0, 1])))
def test_validation_weights_xgbclassifier(self):
self._init_ray()
from sklearn.datasets import make_hastie_10_2
# prepare training and test data
X, y = make_hastie_10_2(n_samples=2000, random_state=42)
labels, y = np.unique(y, return_inverse=True)
X_train, X_test = X[:1600], X[1600:]
y_train, y_test = y[:1600], y[1600:]
# instantiate model
param_dist = {
"objective": "binary:logistic",
"n_estimators": 2,
"random_state": 123,
}
clf = RayXGBClassifier(**param_dist)
# train it using instance weights only in the training set
weights_train = np.random.choice([1, 2], len(X_train))
clf.fit(
X_train,
y_train,
sample_weight=weights_train,
eval_set=[(X_test, y_test)],
eval_metric="logloss",
verbose=False,
)
# evaluate logloss metric on test set *without* using weights
evals_result_without_weights = clf.evals_result()
logloss_without_weights = evals_result_without_weights["validation_0"][
"logloss"]
# now use weights for the test set
np.random.seed(0)
weights_test = np.random.choice([1, 2], len(X_test))
clf.fit(
X_train,
y_train,
sample_weight=weights_train,
eval_set=[(X_test, y_test)],
sample_weight_eval_set=[weights_test],
eval_metric="logloss",
verbose=False,
)
evals_result_with_weights = clf.evals_result()
logloss_with_weights = evals_result_with_weights["validation_0"][
"logloss"]
# check that the logloss in the test set is actually different
# when using weights than when not using them
assert all((logloss_with_weights[i] != logloss_without_weights[i]
for i in [0, 1]))
def test_sklearn_get_default_params(self):
self._init_ray()
from sklearn.datasets import load_digits
digits_2class = load_digits(n_class=2)
X = digits_2class["data"]
y = digits_2class["target"]
cls = RayXGBClassifier()
assert cls.get_params()["base_score"] is None
cls.fit(X[:4, ...], y[:4, ...])
assert cls.get_params()["base_score"] is not None
def test_select_feature(self):
self._init_ray()
from sklearn.datasets import load_digits
from sklearn.feature_selection import SelectFromModel
digits = load_digits(n_class=2)
y = digits["target"]
X = digits["data"]
cls = RayXGBClassifier()
cls.fit(X, y)
selector = SelectFromModel(cls, prefit=True, max_features=1)
X_selected = selector.transform(X)
assert X_selected.shape[1] == 1
def test_sklearn_api_gblinear(self):
self._init_ray()
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
tr_d, te_d, tr_l, te_l = train_test_split(
iris.data, iris.target, train_size=120)
classifier = RayXGBClassifier(
booster="gblinear", n_estimators=100, random_state=self.seed)
classifier.fit(tr_d, tr_l)
preds = classifier.predict(te_d)
labels = te_l
err = (sum([1 for p, l in zip(preds, labels)
if p != l]) * 1.0 / len(te_l))
assert err < 0.5
def run_boost_from_prediction(self, tree_method):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
model_0 = RayXGBClassifier(
learning_rate=0.3,
random_state=0,
n_estimators=4,
tree_method=tree_method,
)
model_0.fit(X=X, y=y)
margin = model_0.predict(X, output_margin=True)
model_1 = RayXGBClassifier(
learning_rate=0.3,
random_state=0,
n_estimators=4,
tree_method=tree_method,
)
model_1.fit(X=X, y=y, base_margin=margin)
predictions_1 = model_1.predict(X, base_margin=margin)
cls_2 = RayXGBClassifier(
learning_rate=0.3,
random_state=0,
n_estimators=8,
tree_method=tree_method,
)
cls_2.fit(X=X, y=y)
predictions_2 = cls_2.predict(X)
assert np.all(predictions_1 == predictions_2)
def test_classification_with_custom_objective(self):
self._init_ray()
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
def logregobj(y_true, y_pred):
y_pred = 1.0 / (1.0 + np.exp(-y_pred))
grad = y_pred - y_true
hess = y_pred * (1.0 - y_pred)
return grad, hess
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(objective=logregobj)
xgb_model.fit(X[train_index], y[train_index])
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
# Test that the custom objective function is actually used
class XGBCustomObjectiveException(Exception):
pass
def dummy_objective(y_true, y_preds):
raise XGBCustomObjectiveException()
xgb_model = RayXGBClassifier(objective=dummy_objective)
# TODO figure out how to assertRaises XGBCustomObjectiveException
with self.assertRaises(RuntimeError):
xgb_model.fit(X, y)
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