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Cleanup Python tests. (#7426)

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This commit is contained in:
Jiaming Yuan
2021-11-14 15:47:05 +08:00
committed by GitHub
parent 38ca96c9fc
commit 8cc75f1576
2 changed files with 27 additions and 116 deletions
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94
tests/python/test_with_sklearn.py
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@@ -17,16 +17,6 @@ pytestmark = pytest.mark.skipif(**tm.no_sklearn())
from sklearn.utils.estimator_checks import parametrize_with_checks
class TemporaryDirectory(object):
"""Context manager for tempfile.mkdtemp()"""
def __enter__(self):
self.name = tempfile.mkdtemp()
return self.name
def __exit__(self, exc_type, exc_value, traceback):
shutil.rmtree(self.name)
def test_binary_classification():
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
@@ -509,7 +499,7 @@ def test_classification_with_custom_objective():
assert is_called[0]
def test_sklearn_api():
def run_sklearn_api(booster, error, n_est):
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
@@ -517,30 +507,18 @@ def test_sklearn_api():
tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target,
train_size=120, test_size=0.2)
classifier = xgb.XGBClassifier(booster='gbtree', n_estimators=10)
classifier = xgb.XGBClassifier(booster=booster, n_estimators=n_est)
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 < 0.2
assert err < error
def test_sklearn_api_gblinear():
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 = xgb.XGBClassifier(booster='gblinear', n_estimators=100)
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 test_sklearn_api():
run_sklearn_api("gbtree", 0.2, 10)
run_sklearn_api("gblinear", 0.5, 100)
@pytest.mark.skipif(**tm.no_matplotlib())
@@ -721,7 +699,7 @@ def test_sklearn_get_default_params():
assert cls.get_params()['base_score'] is not None
def test_validation_weights_xgbmodel():
def run_validation_weights(model):
from sklearn.datasets import make_hastie_10_2
# prepare training and test data
@@ -733,7 +711,7 @@ def test_validation_weights_xgbmodel():
# instantiate model
param_dist = {'objective': 'binary:logistic', 'n_estimators': 2,
'random_state': 123}
clf = xgb.sklearn.XGBModel(**param_dist)
clf = model(**param_dist)
# train it using instance weights only in the training set
weights_train = np.random.choice([1, 2], len(X_train))
@@ -778,49 +756,9 @@ def test_validation_weights_xgbmodel():
sample_weight_eval_set=[weights_train])
def test_validation_weights_xgbclassifier():
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 = xgb.sklearn.XGBClassifier(**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_validation_weights():
run_validation_weights(xgb.XGBModel)
run_validation_weights(xgb.XGBClassifier)
def save_load_model(model_path):
@@ -862,16 +800,16 @@ def save_load_model(model_path):
def test_save_load_model():
with TemporaryDirectory() as tempdir:
with tempfile.TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, 'digits.model')
save_load_model(model_path)
with TemporaryDirectory() as tempdir:
with tempfile.TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, 'digits.model.json')
save_load_model(model_path)
from sklearn.datasets import load_digits
with TemporaryDirectory() as tempdir:
with tempfile.TemporaryDirectory() as tempdir:
model_path = os.path.join(tempdir, 'digits.model.json')
digits = load_digits(n_class=2)
y = digits['target']
@@ -949,7 +887,7 @@ def test_XGBClassifier_resume():
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import log_loss
with TemporaryDirectory() as tempdir:
with tempfile.TemporaryDirectory() as tempdir:
model1_path = os.path.join(tempdir, 'test_XGBClassifier.model')
model1_booster_path = os.path.join(tempdir, 'test_XGBClassifier.booster')
@@ -1089,7 +1027,7 @@ def test_pandas_input():
def run_feature_weights(X, y, fw, model=xgb.XGBRegressor):
with TemporaryDirectory() as tmpdir:
with tempfile.TemporaryDirectory() as tmpdir:
colsample_bynode = 0.5
reg = model(tree_method='hist', colsample_bynode=colsample_bynode)