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Enable loading model from <1.0.0 trained with objective='binary:logitraw' (#6517)

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* Enable loading model from <1.0.0 trained with objective='binary:logitraw'

* Add binary:logitraw in model compatibility testing suite

* Feedback from @trivialfis: Override ProbToMargin() for LogisticRaw

Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Philip Hyunsu Cho
2020-12-16 16:53:46 -08:00
committed by GitHub
parent bf6cfe3b99
commit ad1a527709
5 changed files with 45 additions and 21 deletions
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32
tests/python/generate_models.py
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@@ -64,22 +64,24 @@ def generate_logistic_model():
y = np.random.randint(0, 2, size=kRows)
assert y.max() == 1 and y.min() == 0
data = xgboost.DMatrix(X, label=y, weight=w)
booster = xgboost.train({'tree_method': 'hist',
'num_parallel_tree': kForests,
'max_depth': kMaxDepth,
'objective': 'binary:logistic'},
num_boost_round=kRounds, dtrain=data)
booster.save_model(booster_bin('logit'))
booster.save_model(booster_json('logit'))
for objective, name in [('binary:logistic', 'logit'), ('binary:logitraw', 'logitraw')]:
data = xgboost.DMatrix(X, label=y, weight=w)
booster = xgboost.train({'tree_method': 'hist',
'num_parallel_tree': kForests,
'max_depth': kMaxDepth,
'objective': objective},
num_boost_round=kRounds, dtrain=data)
booster.save_model(booster_bin(name))
booster.save_model(booster_json(name))
reg = xgboost.XGBClassifier(tree_method='hist',
num_parallel_tree=kForests,
max_depth=kMaxDepth,
n_estimators=kRounds)
reg.fit(X, y, w)
reg.save_model(skl_bin('logit'))
reg.save_model(skl_json('logit'))
reg = xgboost.XGBClassifier(tree_method='hist',
num_parallel_tree=kForests,
max_depth=kMaxDepth,
n_estimators=kRounds,
objective=objective)
reg.fit(X, y, w)
reg.save_model(skl_bin(name))
reg.save_model(skl_json(name))
def generate_classification_model():

11
tests/python/test_model_compatibility.py
View File

@@ -24,6 +24,10 @@ def run_booster_check(booster, name):
config['learner']['learner_model_param']['base_score']) == 0.5
assert config['learner']['learner_train_param'][
'objective'] == 'multi:softmax'
elif name.find('logitraw') != -1:
assert len(booster.get_dump()) == gm.kForests * gm.kRounds
assert config['learner']['learner_model_param']['num_class'] == str(0)
assert config['learner']['learner_train_param']['objective'] == 'binary:logitraw'
elif name.find('logit') != -1:
assert len(booster.get_dump()) == gm.kForests * gm.kRounds
assert config['learner']['learner_model_param']['num_class'] == str(0)
@@ -77,6 +81,13 @@ def run_scikit_model_check(name, path):
assert config['learner']['learner_train_param'][
'objective'] == 'rank:ndcg'
run_model_param_check(config)
elif name.find('logitraw') != -1:
logit = xgboost.XGBClassifier()
logit.load_model(path)
assert (len(logit.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
config = json.loads(logit.get_booster().save_config())
assert config['learner']['learner_train_param']['objective'] == 'binary:logitraw'
elif name.find('logit') != -1:
logit = xgboost.XGBClassifier()
logit.load_model(path)