xg_train_slower.py 文件源码

def tune_xgb_cv(params_untuned,params_sklearn,scoring='roc_auc', n_jobs=4, cv=5,verbose=10):

    for param_untuned in params_untuned:
        print '==========  ', param_untuned, '  =============='
        print_params(params_sklearn)
        estimator = xgb.XGBClassifier(**params_sklearn)
        # if(param_untuned.keys()[0] == 'n_estimators'):
        #     cv = 1
        grid_search = GridSearchCV(estimator, param_grid=param_untuned, scoring=scoring, n_jobs=n_jobs, cv=cv, verbose=verbose)
        grid_search.fit(x, y)
        df = pd.DataFrame(grid_search.cv_results_)[['params', 'mean_train_score', 'mean_test_score']]
        print df
        print 'the best_params : ', grid_search.best_params_
        print 'the best_score  : ', grid_search.best_score_
        for k,v in grid_search.best_params_.items():
            params_sklearn[k] = v
    return estimator,params_sklearn