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Deprecate reg:linear' in favor of reg:squarederror'. (#4267)

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* Deprecate `reg:linear' in favor of `reg:squarederror'.
* Replace the use of `reg:linear'.
* Replace the use of `silent`.
This commit is contained in:
Jiaming Yuan
2019-03-17 17:55:04 +08:00
committed by GitHub
parent cf8d5b9b76
commit 29a1356669
34 changed files with 210 additions and 193 deletions
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23
tests/python/test_basic_models.py
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@@ -11,7 +11,7 @@ rng = np.random.RandomState(1994)
class TestModels(unittest.TestCase):
def test_glm(self):
param = {'silent': 1, 'objective': 'binary:logistic',
param = {'verbosity': 0, 'objective': 'binary:logistic',
'booster': 'gblinear', 'alpha': 0.0001, 'lambda': 1, 'nthread': 1}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 4
@@ -26,7 +26,7 @@ class TestModels(unittest.TestCase):
def test_dart(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'max_depth': 5, 'objective': 'binary:logistic', 'booster': 'dart', 'silent': False}
param = {'max_depth': 5, 'objective': 'binary:logistic', 'booster': 'dart', 'verbosity': 1}
# specify validations set to watch performance
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2
@@ -51,7 +51,7 @@ class TestModels(unittest.TestCase):
# check whether sample_type and normalize_type work
num_round = 50
param['silent'] = True
param['verbosity'] = 0
param['learning_rate'] = 0.1
param['rate_drop'] = 0.1
preds_list = []
@@ -74,7 +74,8 @@ class TestModels(unittest.TestCase):
# learning_rates as a list
# init eta with 0 to check whether learning_rates work
param = {'max_depth': 2, 'eta': 0, 'silent': 1, 'objective': 'binary:logistic'}
param = {'max_depth': 2, 'eta': 0, 'verbosity': 0,
'objective': 'binary:logistic'}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
evals_result=evals_result)
@@ -84,7 +85,8 @@ class TestModels(unittest.TestCase):
assert eval_errors[0] > eval_errors[-1]
# init learning_rate with 0 to check whether learning_rates work
param = {'max_depth': 2, 'learning_rate': 0, 'silent': 1, 'objective': 'binary:logistic'}
param = {'max_depth': 2, 'learning_rate': 0, 'verbosity': 0,
'objective': 'binary:logistic'}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
evals_result=evals_result)
@@ -94,7 +96,7 @@ class TestModels(unittest.TestCase):
assert eval_errors[0] > eval_errors[-1]
# check if learning_rates override default value of eta/learning_rate
param = {'max_depth': 2, 'silent': 1, 'objective': 'binary:logistic'}
param = {'max_depth': 2, 'verbosity': 0, 'objective': 'binary:logistic'}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0, 0, 0, 0],
evals_result=evals_result)
@@ -111,7 +113,7 @@ class TestModels(unittest.TestCase):
assert isinstance(bst, xgb.core.Booster)
def test_custom_objective(self):
param = {'max_depth': 2, 'eta': 1, 'silent': 1}
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 2
@@ -152,7 +154,8 @@ class TestModels(unittest.TestCase):
def test_multi_eval_metric(self):
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
param = {'max_depth': 2, 'eta': 0.2, 'silent': 1, 'objective': 'binary:logistic'}
param = {'max_depth': 2, 'eta': 0.2, 'verbosity': 0,
'objective': 'binary:logistic'}
param['eval_metric'] = ["auc", "logloss", 'error']
evals_result = {}
bst = xgb.train(param, dtrain, 4, watchlist, evals_result=evals_result)
@@ -161,7 +164,7 @@ class TestModels(unittest.TestCase):
assert set(evals_result['eval'].keys()) == {'auc', 'error', 'logloss'}
def test_fpreproc(self):
param = {'max_depth': 2, 'eta': 1, 'silent': 1,
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
num_round = 2
@@ -175,7 +178,7 @@ class TestModels(unittest.TestCase):
metrics={'auc'}, seed=0, fpreproc=fpreproc)
def test_show_stdv(self):
param = {'max_depth': 2, 'eta': 1, 'silent': 1,
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
num_round = 2
xgb.cv(param, dtrain, num_round, nfold=5,