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[Breaking] Require format to be specified in input URI. (#9077)

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Previously, we use `libsvm` as default when format is not specified. However, the dmlc
data parser is not particularly robust against errors, and the most common type of error
is undefined format.

Along with which, we will recommend users to use other data loader instead. We will
continue the maintenance of the parsers as it's currently used for many internal tests
including federated learning.
This commit is contained in:
Jiaming Yuan
2023-04-28 19:45:15 +08:00
committed by GitHub
parent e922004329
commit 1f9a57d17b
58 changed files with 327 additions and 268 deletions
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24
tests/python/test_basic_models.py
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@@ -42,8 +42,7 @@ class TestModels:
param = {'verbosity': 0, 'objective': 'binary:logistic',
'booster': 'gblinear', 'alpha': 0.0001, 'lambda': 1,
'nthread': 1}
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtest = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.test"))
dtrain, dtest = tm.load_agaricus(__file__)
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 4
bst = xgb.train(param, dtrain, num_round, watchlist)
@@ -55,8 +54,7 @@ class TestModels:
assert err < 0.2
def test_dart(self):
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtest = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.test"))
dtrain, dtest = tm.load_agaricus(__file__)
param = {'max_depth': 5, 'objective': 'binary:logistic',
'eval_metric': 'logloss', 'booster': 'dart', 'verbosity': 1}
# specify validations set to watch performance
@@ -122,7 +120,7 @@ class TestModels:
def test_boost_from_prediction(self):
# Re-construct dtrain here to avoid modification
margined = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
margined, _ = tm.load_agaricus(__file__)
bst = xgb.train({'tree_method': 'hist'}, margined, 1)
predt_0 = bst.predict(margined, output_margin=True)
margined.set_base_margin(predt_0)
@@ -130,13 +128,13 @@ class TestModels:
predt_1 = bst.predict(margined)
assert np.any(np.abs(predt_1 - predt_0) > 1e-6)
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtrain, _ = tm.load_agaricus(__file__)
bst = xgb.train({'tree_method': 'hist'}, dtrain, 2)
predt_2 = bst.predict(dtrain)
assert np.all(np.abs(predt_2 - predt_1) < 1e-6)
def test_boost_from_existing_model(self):
X = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
X, _ = tm.load_agaricus(__file__)
booster = xgb.train({'tree_method': 'hist'}, X, num_boost_round=4)
assert booster.num_boosted_rounds() == 4
booster = xgb.train({'tree_method': 'hist'}, X, num_boost_round=4,
@@ -156,8 +154,7 @@ class TestModels:
'objective': 'reg:logistic',
"tree_method": tree_method
}
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtest = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.test"))
dtrain, dtest = tm.load_agaricus(__file__)
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 10
@@ -203,8 +200,7 @@ class TestModels:
self.run_custom_objective()
def test_multi_eval_metric(self):
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtest = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.test"))
dtrain, dtest = tm.load_agaricus(__file__)
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
param = {'max_depth': 2, 'eta': 0.2, 'verbosity': 1,
'objective': 'binary:logistic'}
@@ -226,7 +222,7 @@ class TestModels:
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtrain, _ = tm.load_agaricus(__file__)
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'auc'}, seed=0, fpreproc=fpreproc)
@@ -234,7 +230,7 @@ class TestModels:
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
num_round = 2
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtrain, _ = tm.load_agaricus(__file__)
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'error'}, seed=0, show_stdv=False)
@@ -392,7 +388,7 @@ class TestModels:
os.remove(model_path)
try:
dtrain = xgb.DMatrix(os.path.join(dpath, "agaricus.txt.train"))
dtrain, _ = tm.load_agaricus(__file__)
xgb.train({'objective': 'foo'}, dtrain, num_boost_round=1)
except ValueError as e:
e_str = str(e)