From 1fdb871a19ee15a8fa4cad0696801f2b2b2fab3d Mon Sep 17 00:00:00 2001 From: Gene Date: Sat, 22 Apr 2023 16:22:31 +0800 Subject: [PATCH] [MNT] Modify LGBMClassifier in JobSelectorReuser --- learnware/learnware/reuse.py | 40 ++++++++++++++++++++---------------- 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/learnware/learnware/reuse.py b/learnware/learnware/reuse.py index 0e64fdf..925a49e 100644 --- a/learnware/learnware/reuse.py +++ b/learnware/learnware/reuse.py @@ -224,15 +224,17 @@ class JobSelectorReuser(BaseReuser): for lr in learning_rate: for md in max_depth: - model = LGBMClassifier( - max_depth=md, - learning_rate=lr, - n_estimators=2000, - # objective="multiclass", - # num_class=num_class, - boosting_type="gbdt", - seed=0, - ) + lgb_params = { + "boosting_type": "gbdt", + "objective": "binary", + "metric": "binary_logloss", + "learning_rate": lr, + "max_depth": md, + "n_estimators": 2000, + "boost_from_average": False, + "silent": False, + } + model = LGBMClassifier(**lgb_params) train_y = train_y.astype(int) model.fit(train_x, train_y, eval_set=[(val_x, val_y)], early_stopping_rounds=300) pred_y = model.predict(org_train_x) @@ -242,15 +244,17 @@ class JobSelectorReuser(BaseReuser): score_best = score params = (lr, md) - model = LGBMClassifier( - max_depth=params[1], - learning_rate=params[0], - n_estimators=2000, - # objective="multiclass", - # num_class=num_class, - boosting_type="gbdt", - seed=0, - ) + lgb_params = { + "boosting_type": "gbdt", + "objective": "binary", + "metric": "binary_logloss", + "learning_rate": params[0], + "max_depth": params[1], + "n_estimators": 2000, + "boost_from_average": False, + "silent": False, + } + model = LGBMClassifier(**lgb_params) model.fit(org_train_x, org_train_y, eval_set=[(org_train_x, org_train_y)], early_stopping_rounds=300) return model