Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Save model in ubj as the default. (#9947)

Browse Source
This commit is contained in:
Jiaming Yuan
2024-01-05 17:53:36 +08:00
committed by GitHub
parent c03a4d5088
commit 38dd91f491
23 changed files with 598 additions and 550 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
tests/python/test_callback.py
View File

@@ -278,14 +278,18 @@ class TestCallbacks:
dtrain, dtest = tm.load_agaricus(__file__)
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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', 'eval_metric': 'error',
'tree_method': tree_method}
param = {
"max_depth": 2,
"eta": 0,
"objective": "binary:logistic",
"eval_metric": "error",
"tree_method": tree_method,
}
evals_result = {}
bst = xgb.train(
param,
@@ -295,15 +299,19 @@ class TestCallbacks:
callbacks=[scheduler([0.8, 0.7, 0.6, 0.5])],
evals_result=evals_result,
)
eval_errors_0 = 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[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', 'eval_metric': 'error',
'tree_method': tree_method}
param = {
"max_depth": 2,
"learning_rate": 0,
"objective": "binary:logistic",
"eval_metric": "error",
"tree_method": tree_method,
}
evals_result = {}
bst = xgb.train(
@@ -314,15 +322,17 @@ class TestCallbacks:
callbacks=[scheduler([0.8, 0.7, 0.6, 0.5])],
evals_result=evals_result,
)
eval_errors_1 = 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_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',
'eval_metric': 'error', 'tree_method': tree_method
"max_depth": 2,
"objective": "binary:logistic",
"eval_metric": "error",
"tree_method": tree_method,
}
evals_result = {}
bst = xgb.train(