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[Breaking] Remove learning_rates in Python. (#5155)

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* Remove `learning_rates`.

It's been deprecated since we have callback.

* Set `before_iteration` of `reset_learning_rate` to False to preserve 
  the initial learning rate, and comply to the term "reset".

Closes #4709.

* Tests for various `tree_method`.
This commit is contained in:
Jiaming Yuan
2019-12-24 14:25:48 +08:00
committed by GitHub
parent 73b1bd2789
commit 298ebe68ac
5 changed files with 75 additions and 34 deletions
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60
tests/python/test_basic_models.py
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@@ -88,50 +88,82 @@ class TestModels(unittest.TestCase):
assert np.sum(np.abs(preds_list[ii] - preds_list[jj])) > 0
os.remove(model_path)
def test_eta_decay(self):
def run_eta_decay(self, tree_method):
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 4
# learning_rates as a list
# init eta with 0 to check whether learning_rates work
param = {'max_depth': 2, 'eta': 0, 'verbosity': 0,
'objective': 'binary:logistic'}
'objective': 'binary:logistic', 'tree_method': tree_method}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[xgb.callback.reset_learning_rate([
0.8, 0.7, 0.6, 0.5
])],
evals_result=evals_result)
eval_errors = list(map(float, evals_result['eval']['error']))
eval_errors_0 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
# validation error should decrease, if eta > 0
assert eval_errors[0] > eval_errors[-1]
assert eval_errors_0[0] > eval_errors_0[-1]
# init learning_rate with 0 to check whether learning_rates work
param = {'max_depth': 2, 'learning_rate': 0, 'verbosity': 0,
'objective': 'binary:logistic'}
'objective': 'binary:logistic', 'tree_method': tree_method}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0.8, 0.7, 0.6, 0.5],
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[xgb.callback.reset_learning_rate(
[0.8, 0.7, 0.6, 0.5])],
evals_result=evals_result)
eval_errors = list(map(float, evals_result['eval']['error']))
eval_errors_1 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
# validation error should decrease, if learning_rate > 0
assert eval_errors[0] > eval_errors[-1]
assert eval_errors_1[0] > eval_errors_1[-1]
# check if learning_rates override default value of eta/learning_rate
param = {'max_depth': 2, 'verbosity': 0, 'objective': 'binary:logistic'}
param = {
'max_depth': 2, 'verbosity': 0, 'objective': 'binary:logistic',
'tree_method': tree_method
}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=[0, 0, 0, 0],
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[xgb.callback.reset_learning_rate(
[0, 0, 0, 0]
)],
evals_result=evals_result)
eval_errors = list(map(float, evals_result['eval']['error']))
eval_errors_2 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
# validation error should not decrease, if eta/learning_rate = 0
assert eval_errors[0] == eval_errors[-1]
assert eval_errors_2[0] == eval_errors_2[-1]
# learning_rates as a customized decay function
def eta_decay(ithround, num_boost_round):
return num_boost_round / (ithround + 1)
bst = xgb.train(param, dtrain, num_round, watchlist, learning_rates=eta_decay)
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[
xgb.callback.reset_learning_rate(eta_decay)
],
evals_result=evals_result)
eval_errors_3 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
assert eval_errors_3[0] == eval_errors_2[0]
for i in range(1, len(eval_errors_0)):
assert eval_errors_3[i] != eval_errors_2[i]
def test_eta_decay_hist(self):
self.run_eta_decay('hist')
def test_eta_decay_approx(self):
self.run_eta_decay('approx')
def test_eta_decay_exact(self):
self.run_eta_decay('exact')
def test_boost_from_prediction(self):
# Re-construct dtrain here to avoid modification
margined = xgb.DMatrix(dpath + 'agaricus.txt.train')