tools.py 文件源码

def tune_classifier(estimator,params,X_train,Y_train,scoring='roc_auc',n_jobs=3,cv=5):
    results = []
    for k,values in params.items():
        params_single = dict(k=values)
        print '==========  ',params_single,'  =============='
        grid_search = GridSearchCV(estimator,param_grid=params_single,scoring=scoring,n_jobs=n_jobs,cv=cv,verbose=5)
        grid_search.fit(X_train,Y_train)
        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_
        results.append(grid_search.best_params_)
    return results