init_params = { "boosting_type": "gbdt", "objective": "regression", "metric": "mae", "n_estimators": 500, "learning_rate": 0.003, "num_leaves": 128, "feature_fraction": 0.4, # "bagging_fraction": 1, # "bagging_freq": 0, "min_data_in_leaf": 500, "min_sum_hessian_in_leaf": 1, # "lambda_l1": 0, # "lambda_l2": 0, "importance_type": "gain", "n_jobs": 4, "random_state": 456, } fit_params = {"callbacks": [counter()]} model_run(lgb.LGBMRegressor, 2017, init_params, fit_params=fit_params, pca=True, save_model=True, out="results/lgbm_3") print("Done")
"learning_rate": 0.002, "num_leaves": 128, "feature_fraction": 0.32, "bagging_fraction": 0.5, "bagging_freq": 1, "min_data_in_leaf": 500, "min_sum_hessian_in_leaf": 1, # "lambda_l1": 0, # "lambda_l2": 0, "importance_type": "gain", "n_jobs": -1, "random_state": 456, } fit_params = {"callbacks": [counter()]} model_run(lgb.LGBMRegressor, 2016, init_params, fit_params=fit_params, save_model=True, out="results/lrf_2") model_run(lgb.LGBMRegressor, 2017, init_params, fit_params=fit_params, save_model=True, out="results/lrf_2") print("Done")