ndcg_at或者ndcg_eval_at或者eval_at:一个整数列表,指定了NDCG评估点的位置。默认为1、2、3、4、5。 参数影响与调参建议 以下为总结的核心参数对模型的影响,及与之对应的调参建议。 (1) 对树生长控制 num_leaves:叶节点的数目。它是控制树模型复杂度的主要参数。 如果是level-wise,则该参数为,其中depth为树...
如果为True,则在训练时就输出度量结果。 ndcg\_at或者ndcg\_eval\_at或者eval\_at:一个整数列表,指定了NDCG评估点的位置。默认为1、2、3、4、5。 2.2 参数影响与调参建议 以下为总结的核心参数对模型的影响,及与之对应的调参建议。 (1) 对树生长控制 num\_leaves:叶节点的数目。它是控制树模型复杂度的主...
eval_at=[3], early_stopping_rounds=10) # 5) Predictions test_pred = gbm.predict(X_test) X_test["predicted_ranking"] = test_pred X_test.sort_values("predicted_ranking", ascending=False) And here is the bug: C:\Users\USER\pythonProject\venv\Scripts\python.exe "C:/Users/USER/python...
classLGBMRegressorFoundat:lightgbm.sklearn classLGBMRegressor(LGBMModel,_LGBMRegressorBase): """LightGBM regressor.""" deffit(self,X,y, sample_weight=None,init_score=None, eval_set=None,eval_names=None,eval_sample_weight=None, eval_init_score=None,eval_metric=None,early_stopping_rounds=None, ...
(X, X_test, y, params, num_classes=2, folds=None, model_type='lgb', eval_metric='logloss', columns=None, plot_feature_importance=False, model=None, verbose=10000, early_stopping_rounds=200, splits=None, n_folds=3): """ 分类模型函数 返回字典,包括: oof predictions, test predictions...
'ndcg_eval_at': [1, 3, 5, 10], 'sparse_threshold': 1.0, 'device': 'gpu', 'gpu_platform_id': 1, 'gpu_device_id': 0 } t0 = time.time() gbm = lgb.train(params, train_set=dtrain, num_boost_round=10, valid_sets=None, valid_names=None, ...
= LightGBMRanker( labelCol=label_col, featuresCol=features_col, groupCol=query_col, predictionCol="preds", leafPredictionCol="leafPreds", featuresShapCol="importances", repartitionByGroupingColumn=True, numLeaves=32, numIterations=200, evalAt=[1,3,5], metric="ndcg", dataTransferMode="bulk"...
编写测试脚本 import lightgbm as lgb import time params = {'max_bin': 63, 'num_leaves': 255, 'learning_rate': 0.1, 'tree_learner': 'serial', 'task': 'train', 'is_training_metric': 'false', 'min_data_in_leaf': 1, 'min_sum_hessian_in_leaf': 100, 'ndcg_eval_at': [1,3,...
cat > lightgbm_gpu.conf <<EOF max_bin = 63 num_leaves = 255 num_iterations = 50 learning_rate = 0.1 tree_learner = serial task = train is_training_metric = false min_data_in_leaf = 1 min_sum_hessian_in_leaf = 100 ndcg_eval_at = 1,3,5,10 sparse_threshold = 1.0 device = gp...
(trn_x, trn_y, eval_set=(val_x, val_y), cat_features=[], use_best_model=True, verbose=500) val_pred = model.predict(val_x) test_pred = model.predict(test_x) train[valid_index] = val_pred test = test_pred / kf.n_splits cv_scores.append(roc_auc_score(val_y, val_pred)...