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from typing import Tuple
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.updater import get_basescore
rng = np.random.RandomState(1994)
class TestEarlyStopping:
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping_nonparallel(self):
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
X, y = load_digits(n_class=2, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf1 = xgb.XGBClassifier(
learning_rate=0.1, early_stopping_rounds=5, eval_metric="auc"
)
clf1.fit(X_train, y_train, eval_set=[(X_test, y_test)])
clf2 = xgb.XGBClassifier(
learning_rate=0.1, early_stopping_rounds=4, eval_metric="auc"
)
clf2.fit(X_train, y_train, eval_set=[(X_test, y_test)])
# should be the same
assert clf1.best_score == clf2.best_score
assert clf1.best_score != 1
# check overfit
clf3 = xgb.XGBClassifier(
learning_rate=0.1, eval_metric="auc", early_stopping_rounds=10
)
clf3.fit(X_train, y_train, eval_set=[(X_test, y_test)])
base_score = get_basescore(clf3)
assert 0.53 > base_score > 0.5
clf3 = xgb.XGBClassifier(
learning_rate=0.1,
base_score=0.5,
eval_metric="auc",
early_stopping_rounds=10,
)
clf3.fit(X_train, y_train, eval_set=[(X_test, y_test)])
assert clf3.best_score == 1
@staticmethod
def assert_metrics_length(cv, expected_length):
for key, value in cv.items():
assert len(value) == expected_length
@pytest.mark.skipif(**tm.no_sklearn())
def test_cv_early_stopping(self) -> None:
from sklearn.datasets import load_digits
X, y = load_digits(n_class=2, return_X_y=True)
dm = xgb.DMatrix(X, label=y)
params = {
"max_depth": 2,
"eta": 1,
"objective": "binary:logistic",
"eval_metric": "error",
}
def evalerror(preds: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:
from sklearn.metrics import mean_squared_error
labels = dtrain.get_label()
return "rmse", mean_squared_error(labels, preds)
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=10)
self.assert_metrics_length(cv, 10)
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=5)
self.assert_metrics_length(cv, 3)
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, early_stopping_rounds=1)
self.assert_metrics_length(cv, 1)
cv = xgb.cv(
params,
dm,
num_boost_round=10,
nfold=10,
custom_metric=evalerror,
early_stopping_rounds=10,
)
self.assert_metrics_length(cv, 10)
cv = xgb.cv(
params,
dm,
num_boost_round=10,
nfold=10,
custom_metric=evalerror,
early_stopping_rounds=1,
)
self.assert_metrics_length(cv, 5)
cv = xgb.cv(
params,
dm,
num_boost_round=10,
nfold=10,
custom_metric=evalerror,
maximize=True,
early_stopping_rounds=1,
)
self.assert_metrics_length(cv, 1)
with pytest.raises(ValueError, match="`save_best`"):
cv = xgb.cv(
params,
dm,
num_boost_round=10,
nfold=10,
early_stopping_rounds=1,
callbacks=[xgb.callback.EarlyStopping(3, save_best=True)],
)
@pytest.mark.skipif(**tm.no_sklearn())
@pytest.mark.skipif(**tm.no_pandas())
def test_cv_early_stopping_with_multiple_eval_sets_and_metrics(self):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
dm = xgb.DMatrix(X, label=y)
params = {"objective": "binary:logistic"}
metrics = [
["auc"],
["error"],
["logloss"],
["logloss", "auc"],
["logloss", "error"],
["error", "logloss"],
]
num_iteration_history = []
# If more than one metrics is given, early stopping should use the last metric
for i, m in enumerate(metrics):
result = xgb.cv(
params,
dm,
num_boost_round=1000,
nfold=5,
stratified=True,
metrics=m,
early_stopping_rounds=20,
seed=42,
)
num_iteration_history.append(len(result))
df = result["test-{}-mean".format(m[-1])]
# When early stopping is invoked, the last metric should be as best it can be.
if m[-1] == "auc":
assert np.all(df <= df.iloc[-1])
else:
assert np.all(df >= df.iloc[-1])
assert num_iteration_history[:3] == num_iteration_history[3:]
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