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[Breaking] Update sklearn interface. (#4929)

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* Remove nthread, seed, silent. Add tree_method, gpu_id, num_parallel_tree. Fix #4909.
* Check data shape. Fix #4896.
* Check element of eval_set is tuple. Fix #4875
*  Add doc for random_state with hogwild. Fixes #4919
This commit is contained in:
Jiaming Yuan
2019-10-12 02:50:09 -04:00
committed by GitHub
parent c2cce4fac3
commit 4bbf062ed3
5 changed files with 177 additions and 128 deletions
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29
tests/python/test_with_sklearn.py
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@@ -175,6 +175,21 @@ def test_feature_importances_gain():
np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
def test_num_parallel_tree():
from sklearn.datasets import load_boston
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'])
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'])
dump = bst.get_booster().get_dump(dump_format='json')
assert len(dump) == 4
def test_boston_housing_regression():
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_boston
@@ -430,18 +445,18 @@ def test_split_value_histograms():
def test_sklearn_random_state():
clf = xgb.XGBClassifier(random_state=402)
assert clf.get_xgb_params()['seed'] == 402
assert clf.get_xgb_params()['random_state'] == 402
clf = xgb.XGBClassifier(seed=401)
assert clf.get_xgb_params()['seed'] == 401
clf = xgb.XGBClassifier(random_state=401)
assert clf.get_xgb_params()['random_state'] == 401
def test_sklearn_n_jobs():
clf = xgb.XGBClassifier(n_jobs=1)
assert clf.get_xgb_params()['nthread'] == 1
assert clf.get_xgb_params()['n_jobs'] == 1
clf = xgb.XGBClassifier(nthread=2)
assert clf.get_xgb_params()['nthread'] == 2
clf = xgb.XGBClassifier(n_jobs=2)
assert clf.get_xgb_params()['n_jobs'] == 2
def test_kwargs():
@@ -482,7 +497,7 @@ def test_kwargs_error():
def test_sklearn_clone():
from sklearn.base import clone
clf = xgb.XGBClassifier(n_jobs=2, nthread=3)
clf = xgb.XGBClassifier(n_jobs=2)
clf.n_jobs = -1
clone(clf)