'n_estimators': 20000, 'max_depth': 5, 'max_delta_step': 5, 'colsample_bylevel': 0.9, 'colsample_bytree': 0.95, 'subsample': 0.8, 'gamma': 1.5, 'max_leaves': 10, 'min_child_weight': 50, 'reg_alpha': 0.6, # L1 regularization 'reg_lambda': 50, # L2 regularization 'seed': 42 } model = ModelLoader(xgb.XGBClassifier, model_params, **xgboost_params) fit_params = {'early_stopping_rounds': 2500, 'verbose': 1000} predict_params = {} results = model.run(data_loader, roc_auc_score, fit_params, predict_params, verbose=True) if args.save: current_file_path = os.path.abspath(__file__) # to save this .py file model.save(data_loader, results, current_file_path, args.preds, args.models) ## << Create and train model
"verbosity": 0, "seed": 42 } def fit(self, train, cv): x_tr, y_tr = train x_cv, y_cv = cv trn_data = lgb.Dataset(x_tr, label=y_tr) val_data = lgb.Dataset(x_cv, label=y_cv) evals_result = {} self.model = lgb.train(self.lgb_params, trn_data, 100000, valid_sets=[trn_data, val_data], early_stopping_rounds=3000, verbose_eval=1000, evals_result=evals_result) def predict(self, test): return self.model.predict(test) model = ModelLoader(LightGbmTrainer, model_params) results = model.run(data_loader, roc_auc_score, {}, {}, verbose=True) if args.save: current_file_path = os.path.abspath(__file__) # to save this .py file model.save(data_loader, results, current_file_path, args.preds, args.models) ## << Create and train model