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Cleanup Python tests. (#7426)

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Jiaming Yuan 2021-11-14 15:47:05 +08:00 committed by GitHub
parent 38ca96c9fc
commit 8cc75f1576
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2 changed files with 27 additions and 116 deletions
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49
tests/python/test_with_dask.py
View File

@ -137,16 +137,13 @@ def test_from_dask_array() -> None:
prediction = prediction.compute()
booster: xgb.Booster = result['booster']
single_node_predt = booster.predict(
xgb.DMatrix(X.compute())
)
single_node_predt = booster.predict(xgb.DMatrix(X.compute()))
np.testing.assert_allclose(prediction, single_node_predt)
config = json.loads(booster.save_config())
assert int(config['learner']['generic_param']['nthread']) == 5
from_arr = xgb.dask.predict(
client, model=booster, data=X)
from_arr = xgb.dask.predict(client, model=booster, data=X)
assert isinstance(from_arr, da.Array)
assert np.all(single_node_predt == from_arr.compute())
@ -477,23 +474,6 @@ def test_dask_classifier(model: str, client: "Client") -> None:
run_dask_classifier(X, y_bin, w, model, None, client, 2)
@pytest.mark.skipif(**tm.no_sklearn())
def test_sklearn_grid_search(client: "Client") -> None:
from sklearn.model_selection import GridSearchCV
X, y, _ = generate_array()
reg = xgb.dask.DaskXGBRegressor(learning_rate=0.1,
tree_method='hist')
reg.client = client
model = GridSearchCV(reg, {'max_depth': [2, 4],
'n_estimators': [5, 10]},
cv=2, verbose=1)
model.fit(X, y)
# Expect unique results for each parameter value This confirms
# sklearn is able to successfully update the parameter
means = model.cv_results_['mean_test_score']
assert len(means) == len(set(means))
def test_empty_dmatrix_training_continuation(client: "Client") -> None:
kRows, kCols = 1, 97
X = dd.from_array(np.random.randn(kRows, kCols))
@ -714,18 +694,11 @@ def test_auc(client: "Client") -> None:
# No test for Exact, as empty DMatrix handling are mostly for distributed
# environment and Exact doesn't support it.
def test_empty_dmatrix_hist() -> None:
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
def test_empty_dmatrix(tree_method) -> None:
with LocalCluster(n_workers=kWorkers) as cluster:
with Client(cluster) as client:
parameters = {'tree_method': 'hist'}
run_empty_dmatrix_reg(client, parameters)
run_empty_dmatrix_cls(client, parameters)
def test_empty_dmatrix_approx() -> None:
with LocalCluster(n_workers=kWorkers) as cluster:
with Client(cluster) as client:
parameters = {'tree_method': 'approx'}
parameters = {'tree_method': tree_method}
run_empty_dmatrix_reg(client, parameters)
run_empty_dmatrix_cls(client, parameters)
@ -1102,12 +1075,12 @@ class TestWithDask:
os.remove(after_fname)
def run_updater_test(
self,
client: "Client",
params: Dict,
num_rounds: int,
dataset: tm.TestDataset,
tree_method: str
self,
client: "Client",
params: Dict,
num_rounds: int,
dataset: tm.TestDataset,
tree_method: str
) -> None:
params['tree_method'] = tree_method
params = dataset.set_params(params)

94
tests/python/test_with_sklearn.py
View File

@ -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)