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[breaking] Remove deprecated parameters in the skl interface. (#9986)

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This commit is contained in:
Jiaming Yuan
2024-01-15 20:40:05 +08:00
committed by GitHub
parent 2de85d3241
commit 0798e36d73
16 changed files with 418 additions and 462 deletions
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117
tests/python/test_with_sklearn.py
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@@ -30,8 +30,8 @@ def test_binary_classification():
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
for train_index, test_index in kf.split(X, y):
clf = cls(random_state=42)
xgb_model = clf.fit(X[train_index], y[train_index], eval_metric=['auc', 'logloss'])
clf = cls(random_state=42, eval_metric=['auc', 'logloss'])
xgb_model = clf.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))
@@ -101,10 +101,11 @@ def test_best_iteration():
def train(booster: str, forest: Optional[int]) -> None:
rounds = 4
cls = xgb.XGBClassifier(
n_estimators=rounds, num_parallel_tree=forest, booster=booster
).fit(
X, y, eval_set=[(X, y)], early_stopping_rounds=3
)
n_estimators=rounds,
num_parallel_tree=forest,
booster=booster,
early_stopping_rounds=3,
).fit(X, y, eval_set=[(X, y)])
assert cls.best_iteration == rounds - 1
# best_iteration is used by default, assert that under gblinear it's
@@ -112,9 +113,9 @@ def test_best_iteration():
cls.predict(X)
num_parallel_tree = 4
train('gbtree', num_parallel_tree)
train('dart', num_parallel_tree)
train('gblinear', None)
train("gbtree", num_parallel_tree)
train("dart", num_parallel_tree)
train("gblinear", None)
def test_ranking():
@@ -258,6 +259,7 @@ def test_stacking_classification():
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
clf.fit(X_train, y_train).score(X_test, y_test)
@pytest.mark.skipif(**tm.no_pandas())
def test_feature_importances_weight():
from sklearn.datasets import load_digits
@@ -474,7 +476,8 @@ def run_housing_rf_regression(tree_method):
rfreg = xgb.XGBRFRegressor()
with pytest.raises(NotImplementedError):
rfreg.fit(X, y, early_stopping_rounds=10)
rfreg.set_params(early_stopping_rounds=10)
rfreg.fit(X, y)
def test_rf_regression():
@@ -574,7 +577,7 @@ def test_classification_with_custom_objective():
return logregobj(y, p)
cls.set_params(objective=wrapped)
cls.predict(X) # no throw
cls.predict(X) # no throw
cls.fit(X, y)
assert is_called[0]
@@ -844,51 +847,65 @@ def run_validation_weights(model):
y_train, y_test = y[:1600], y[1600:]
# instantiate model
param_dist = {'objective': 'binary:logistic', 'n_estimators': 2,
'random_state': 123}
param_dist = {
"objective": "binary:logistic",
"n_estimators": 2,
"random_state": 123,
}
clf = model(**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)
clf.set_params(eval_metric="logloss")
clf.fit(
X_train,
y_train,
sample_weight=weights_train,
eval_set=[(X_test, y_test)],
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"]
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)
clf.set_params(eval_metric="logloss")
clf.fit(
X_train,
y_train,
sample_weight=weights_train,
eval_set=[(X_test, y_test)],
sample_weight_eval_set=[weights_test],
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]))
assert all((logloss_with_weights[i] != logloss_without_weights[i] for i in [0, 1]))
with pytest.raises(ValueError):
# length of eval set and sample weight doesn't match.
clf.fit(X_train, y_train, sample_weight=weights_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
sample_weight_eval_set=[weights_train])
clf.fit(
X_train,
y_train,
sample_weight=weights_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
sample_weight_eval_set=[weights_train],
)
with pytest.raises(ValueError):
cls = xgb.XGBClassifier()
cls.fit(X_train, y_train, sample_weight=weights_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
sample_weight_eval_set=[weights_train])
cls.fit(
X_train,
y_train,
sample_weight=weights_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
sample_weight_eval_set=[weights_train],
)
def test_validation_weights():
@@ -960,8 +977,7 @@ def test_XGBClassifier_resume():
# file name of stored xgb model
model1.save_model(model1_path)
model2 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8)
model2 = xgb.XGBClassifier(learning_rate=0.3, random_state=0, n_estimators=8)
model2.fit(X, Y, xgb_model=model1_path)
pred2 = model2.predict(X)
@@ -972,8 +988,7 @@ def test_XGBClassifier_resume():
# file name of 'Booster' instance Xgb model
model1.get_booster().save_model(model1_booster_path)
model2 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8)
model2 = xgb.XGBClassifier(learning_rate=0.3, random_state=0, n_estimators=8)
model2.fit(X, Y, xgb_model=model1_booster_path)
pred2 = model2.predict(X)
@@ -1279,12 +1294,16 @@ def test_estimator_reg(estimator, check):
):
estimator.fit(X, y)
return
if os.environ["PYTEST_CURRENT_TEST"].find("check_estimators_overwrite_params") != -1:
if (
os.environ["PYTEST_CURRENT_TEST"].find("check_estimators_overwrite_params")
!= -1
):
# A hack to pass the scikit-learn parameter mutation tests. XGBoost regressor
# returns actual internal default values for parameters in `get_params`, but those
# are set as `None` in sklearn interface to avoid duplication. So we fit a dummy
# model and obtain the default parameters here for the mutation tests.
# returns actual internal default values for parameters in `get_params`, but
# those are set as `None` in sklearn interface to avoid duplication. So we fit
# a dummy model and obtain the default parameters here for the mutation tests.
from sklearn.datasets import make_regression
X, y = make_regression(n_samples=2, n_features=1)
estimator.set_params(**xgb.XGBRegressor().fit(X, y).get_params())
@@ -1325,6 +1344,7 @@ def test_categorical():
def test_evaluation_metric():
from sklearn.datasets import load_diabetes, load_digits
from sklearn.metrics import mean_absolute_error
X, y = load_diabetes(return_X_y=True)
n_estimators = 16
@@ -1341,17 +1361,6 @@ def test_evaluation_metric():
for line in lines:
assert line.find("mean_absolute_error") != -1
def metric(predt: np.ndarray, Xy: xgb.DMatrix):
y = Xy.get_label()
return "m", np.abs(predt - y).sum()
with pytest.warns(UserWarning):
reg = xgb.XGBRegressor(
tree_method="hist",
n_estimators=1,
)
reg.fit(X, y, eval_set=[(X, y)], eval_metric=metric)
def merror(y_true: np.ndarray, predt: np.ndarray):
n_samples = y_true.shape[0]
assert n_samples == predt.size