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Replace all uses of deprecated function sklearn.datasets.load_boston (#7373)

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* Replace all uses of deprecated function sklearn.datasets.load_boston

* More renaming

* Fix bad name

* Update assertion

* Fix n boosted rounds.

* Avoid over regularization.

* Rebase.

* Avoid over regularization.

* Whac-a-mole

Co-authored-by: fis <jm.yuan@outlook.com>
This commit is contained in:
Philip Hyunsu Cho
2022-01-30 04:27:57 -08:00
committed by GitHub
parent b4340abf56
commit c621775f34
13 changed files with 56 additions and 66 deletions
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2
tests/python/test_callback.py
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@@ -384,7 +384,7 @@ class TestCallbacks:
os.path.join(tmpdir, 'model_' + str(i) + '.pkl'))
def test_callback_list(self):
X, y = tm.get_boston()
X, y = tm.get_california_housing()
m = xgb.DMatrix(X, y)
callbacks = [xgb.callback.EarlyStopping(rounds=10)]
for i in range(4):

4
tests/python/test_demos.py
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@@ -45,8 +45,8 @@ def test_sklearn_demo():
script = os.path.join(PYTHON_DEMO_DIR, 'sklearn_examples.py')
cmd = ['python', script]
subprocess.check_call(cmd)
assert os.path.exists('best_boston.pkl')
os.remove('best_boston.pkl')
assert os.path.exists('best_calif.pkl')
os.remove('best_calif.pkl')
@pytest.mark.skipif(**tm.no_sklearn())

8
tests/python/test_linear.py
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@@ -39,8 +39,8 @@ class TestLinear:
# We test a weaker condition that the loss has not increased between the first and last
# iteration
@given(parameter_strategy, strategies.integers(10, 50),
tm.dataset_strategy, coord_strategy, strategies.floats(1e-5, 2.0),
strategies.floats(1e-5, 2.0))
tm.dataset_strategy, coord_strategy, strategies.floats(1e-5, 1.0),
strategies.floats(1e-5, 1.0))
@settings(deadline=None)
def test_coordinate_regularised(self, param, num_rounds, dataset, coord_param, alpha, lambd):
param['updater'] = 'coord_descent'
@@ -69,8 +69,8 @@ class TestLinear:
assert tm.non_increasing(sampled_result)
@given(parameter_strategy, strategies.integers(10, 50),
tm.dataset_strategy, strategies.floats(1e-5, 2.0),
strategies.floats(1e-5, 2.0))
tm.dataset_strategy, strategies.floats(1e-5, 1.0),
strategies.floats(1e-5, 1.0))
@settings(deadline=None)
def test_shotgun_regularised(self, param, num_rounds, dataset, alpha, lambd):
param['updater'] = 'shotgun'

4
tests/python/test_predict.py
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@@ -88,8 +88,8 @@ def test_predict_leaf():
def test_predict_shape():
from sklearn.datasets import load_boston
X, y = load_boston(return_X_y=True)
from sklearn.datasets import fetch_california_housing
X, y = fetch_california_housing(return_X_y=True)
reg = xgb.XGBRegressor(n_estimators=1)
reg.fit(X, y)
predt = reg.get_booster().predict(xgb.DMatrix(X), strict_shape=True)

16
tests/python/test_with_dask.py
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@@ -425,8 +425,8 @@ def test_boost_from_prediction(tree_method: str, client: "Client") -> None:
def test_inplace_predict(client: "Client") -> None:
from sklearn.datasets import load_boston
X_, y_ = load_boston(return_X_y=True)
from sklearn.datasets import fetch_california_housing
X_, y_ = fetch_california_housing(return_X_y=True)
X, y = dd.from_array(X_, chunksize=32), dd.from_array(y_, chunksize=32)
reg = xgb.dask.DaskXGBRegressor(n_estimators=4).fit(X, y)
booster = reg.get_booster()
@@ -1405,8 +1405,8 @@ class TestWithDask:
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_sklearn())
def test_custom_objective(self, client: "Client") -> None:
from sklearn.datasets import load_boston
X, y = load_boston(return_X_y=True)
from sklearn.datasets import fetch_california_housing
X, y = fetch_california_housing(return_X_y=True)
X, y = da.from_array(X), da.from_array(y)
rounds = 20
@@ -1552,8 +1552,8 @@ class TestWithDask:
assert np.allclose(np.sum(shap, axis=len(shap.shape) - 1), margin, 1e-5, 1e-5)
def test_shap(self, client: "Client") -> None:
from sklearn.datasets import load_boston, load_digits
X, y = load_boston(return_X_y=True)
from sklearn.datasets import fetch_california_housing, load_digits
X, y = fetch_california_housing(return_X_y=True)
params: Dict[str, Any] = {'objective': 'reg:squarederror'}
self.run_shap(X, y, params, client)
@@ -1597,8 +1597,8 @@ class TestWithDask:
1e-5, 1e-5)
def test_shap_interactions(self, client: "Client") -> None:
from sklearn.datasets import load_boston
X, y = load_boston(return_X_y=True)
from sklearn.datasets import fetch_california_housing
X, y = fetch_california_housing(return_X_y=True)
params = {'objective': 'reg:squarederror'}
self.run_shap_interactions(X, y, params, client)

3
tests/python/test_with_shap.py
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@@ -14,7 +14,8 @@ pytestmark = pytest.mark.skipif(shap is None, reason="Requires shap package")
# Check integration is not broken from xgboost side
# Changes in binary format may cause problems
def test_with_shap():
X, y = shap.datasets.boston()
from sklearn.datasets import fetch_california_housing
X, y = fetch_california_housing(return_X_y=True)
dtrain = xgb.DMatrix(X, label=y)
model = xgb.train({"learning_rate": 0.01}, dtrain, 10)
explainer = shap.TreeExplainer(model)

44
tests/python/test_with_sklearn.py
View File

@@ -328,16 +328,16 @@ def test_select_feature():
def test_num_parallel_tree():
from sklearn.datasets import load_boston
from sklearn.datasets import fetch_california_housing
reg = xgb.XGBRegressor(n_estimators=4, num_parallel_tree=4,
tree_method='hist')
boston = load_boston()
bst = reg.fit(X=boston['data'], y=boston['target'])
X, y = fetch_california_housing(return_X_y=True)
bst = reg.fit(X=X, y=y)
dump = bst.get_booster().get_dump(dump_format='json')
assert len(dump) == 16
reg = xgb.XGBRFRegressor(n_estimators=4)
bst = reg.fit(X=boston['data'], y=boston['target'])
bst = reg.fit(X=X, y=y)
dump = bst.get_booster().get_dump(dump_format='json')
assert len(dump) == 4
@@ -346,14 +346,12 @@ def test_num_parallel_tree():
'num_parallel_tree']) == 4
def test_boston_housing_regression():
def test_calif_housing_regression():
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import KFold
boston = load_boston()
y = boston['target']
X = boston['data']
X, y = fetch_california_housing(return_X_y=True)
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X, y):
xgb_model = xgb.XGBRegressor().fit(X[train_index], y[train_index])
@@ -377,12 +375,12 @@ def test_boston_housing_regression():
xgb_model.feature_names_in_
def run_boston_housing_rf_regression(tree_method):
def run_calif_housing_rf_regression(tree_method):
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import KFold
X, y = load_boston(return_X_y=True)
X, y = fetch_california_housing(return_X_y=True)
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X, y):
xgb_model = xgb.XGBRFRegressor(random_state=42, tree_method=tree_method).fit(
@@ -397,29 +395,27 @@ def run_boston_housing_rf_regression(tree_method):
rfreg.fit(X, y, early_stopping_rounds=10)
def test_boston_housing_rf_regression():
run_boston_housing_rf_regression("hist")
def test_calif_housing_rf_regression():
run_calif_housing_rf_regression("hist")
def test_parameter_tuning():
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
from sklearn.datasets import fetch_california_housing
boston = load_boston()
y = boston['target']
X = boston['data']
X, y = fetch_california_housing(return_X_y=True)
xgb_model = xgb.XGBRegressor(learning_rate=0.1)
clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6],
'n_estimators': [50, 100, 200]},
cv=3, verbose=1)
clf.fit(X, y)
assert clf.best_score_ < 0.7
assert clf.best_params_ == {'n_estimators': 100, 'max_depth': 4}
assert clf.best_params_ == {'n_estimators': 200, 'max_depth': 4}
def test_regression_with_custom_objective():
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import KFold
def objective_ls(y_true, y_pred):
@@ -427,9 +423,7 @@ def test_regression_with_custom_objective():
hess = np.ones(len(y_true))
return grad, hess
boston = load_boston()
y = boston['target']
X = boston['data']
X, y = fetch_california_housing(return_X_y=True)
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X, y):
xgb_model = xgb.XGBRegressor(objective=objective_ls).fit(
@@ -841,13 +835,13 @@ def test_save_load_model():
def test_RFECV():
from sklearn.datasets import load_boston
from sklearn.datasets import fetch_california_housing
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_iris
from sklearn.feature_selection import RFECV
# Regression
X, y = load_boston(return_X_y=True)
X, y = fetch_california_housing(return_X_y=True)
bst = xgb.XGBRegressor(booster='gblinear', learning_rate=0.1,
n_estimators=10,
objective='reg:squarederror',

8
tests/python/testing.py
View File

@@ -229,8 +229,8 @@ class TestDataset:
@memory.cache
def get_boston():
data = datasets.load_boston()
def get_california_housing():
data = datasets.fetch_california_housing()
return data.data, data.target
@@ -315,7 +315,9 @@ def make_categorical(
_unweighted_datasets_strategy = strategies.sampled_from(
[
TestDataset("boston", get_boston, "reg:squarederror", "rmse"),
TestDataset(
"calif_housing", get_california_housing, "reg:squarederror", "rmse"
),
TestDataset("digits", get_digits, "multi:softmax", "mlogloss"),
TestDataset("cancer", get_cancer, "binary:logistic", "logloss"),
TestDataset(