test.py 文件源码

def tune_xgb_cv(params_untuned,scoring='roc_auc', n_jobs=1, cv=5):
    # global  dtrain_whole
    global  num_boost_round
    global  params_sklearn
    # global x
    # global y
    for param_untuned in params_untuned:
        print '==========  ', param_untuned, '  =============='
        print_params(params_sklearn)
        estimator = xgb.XGBClassifier(**params_sklearn)
        grid_search = GridSearchCV(estimator, param_grid=param_untuned, scoring=scoring, n_jobs=n_jobs, cv=cv, verbose=10)
        grid_search.fit(x, y)
        df0 = pd.DataFrame(grid_search.cv_results_)
        df = pd.DataFrame(grid_search.cv_results_)[['params', 'mean_train_score', 'mean_test_score']]
        # print df0
        print df
        print 'the best_params : ', grid_search.best_params_
        print 'the best_score  : ', grid_search.best_score_
        # print grid_search.cv_results_
        for k,v in grid_search.best_params_.items():
            params_sklearn[k] = v