import os from util import ensemble_util model = ensemble_util.XGBoostModel( model_path=os.path.abspath(__file__), corr_threshold=0.9, search=20, top_n=3, eval_func=ensemble_util.xgb_sparse_greedy_f2_metric, xgb_param={ 'eta': [0.1], 'silent': True, # option for logging 'objective': 'binary:logistic', # error evaluation for multiclass tasks 'max_depth': range(2, 11), # depth of the trees in the boosting process 'min_child_weight': [1, 2, 3, 4, 5] }, number_round=1000, ) model.train_all_label() # model.build_and_predict_test()
import os import sys sys.path.append(os.path.abspath("../")) sys.path.append(os.path.abspath("../../")) from util import ensemble_util model = ensemble_util.XGBoostModel( model_path=os.path.abspath(__file__), corr_threshold=0.9, search=10, top_n=1, eval_func=ensemble_util.xgb_sparse_greedy_f2_metric, # meta_model_dir="E:\\backup\\jdfc", meta_model_dir="D:\\github\\JDC\\competition", xgb_param={ 'eta': [0.05], 'silent': True, # option for logging 'objective': 'binary:logistic', # error evaluation for multiclass tasks 'max_depth': range(2, 11), # depth of the trees in the boosting process 'min_child_weight': [1, 2, 3, 4, 5], 'nthread': 6 }, number_round=1000, ) # model.train_all_label() model.build_cnn_ensemble()