hyperopt调参实例——XGBoost

def xgb_train(dtrain, dtest, param, offline=True, verbose=True, num_boost_round=1000):
    if verbose:
        if offline:
            watchlist = [(dtrain, 'train'), (dtest, 'test')]
        else:
            watchlist = [(dtrain, 'train')]
        model = xgb.train(param, dtrain, num_boost_round=num_boost_round, evals=watchlist)
        feature_score = model.get_fscore()
        feature_score = sorted(feature_score.items(), key=lambda x: x[1], reverse=True)
        fs = []
        for key, value in feature_score:
            fs.append("{0},{1}\n".format(key, value))
        if offline:
            feature_score_file = './feature_score/offline_feature_score' + '.csv'
        else:
            feature_score_file = './feature_score/online_feature_score' + '.csv'
        f = open(feature_score_file, 'w')
        f.writelines("feature,score\n")
        f.writelines(fs)
        f.close()
    else:
        model = xgb.train(param, dtrain, num_boost_round=num_boost_round)
    return model

def xgb_predict(model, dtest):
    print 'model_best_ntree_limit : {0}\n'.format(model.best_ntree_limit)
    pred_y = model.predict(dtest, ntree_limit=model.best_ntree_limit)
    return pred_y

def tune_xgb(dtrain, dtest):
    tune_reuslt_file = "./log/tune_" + model_name + ".csv"
    f_w = open(tune_reuslt_file, 'w')
    def objective(args):
        params = {
            'booster': 'gbtree',
            'objective': 'binary:logistic',
            'eval_metric': 'auc',
            'scale_pos_weight': weight,
            # 'lambda': 1000,
            'nthread': n_jobs,
            'eta': args['learning_rate'],
            # 'gamma': args['gamma'],
            'colsample_bytree': args['colsample_bytree'],
            'max_depth': args['max_depth'],
            'subsample': args['subsample']
        }

        #if fs verbose = False
        model = xgb_train(dtrain, dtest, params, offline=True, verbose=False, num_boost_round=int(args['n_estimators']))

        #model.save_model('xgb.model')
        model.dump_model('dump_model_txt')

        pred_y = xgb_predict(model, dtest)
        pred_y[pred_y>0.5] = 1
        pred_y[pred_y<=0.5] = 0
        test_y = dtest.get_label()
        F1 = evalF1(test_y, pred_y)

        xgb_log.write(str(args))
        xgb_log.write('\n')
        xgb_log.write(str(F1))
        xgb_log.write('\n')
        return F1*(-1.0)

    # Searching space
    space = {
        'n_estimators': hp.quniform("n_estimators", 100, 200, 20),
        # 'reg_lambda': hp.loguniform("reg_lambda", np.log(1), np.log(1500)),
        # 'gamma': hp.loguniform("gamma", np.log(0.1), np.log(100)),
        'learning_rate': hp.uniform("learning_rate", 0.05, 0.15),
        'max_depth': 8,
        'subsample': hp.uniform("subsample", 0.5, 0.9),
        'colsample_bytree': hp.uniform("colsample_bytree", 0.5, 0.9),
    }
    best_sln = fmin(objective, space, algo=tpe.suggest, max_evals=150)
    #best_sln = fmin(objective, space, algo=hyperopt.anneal.suggest, max_evals=300)
    pickle.dump(best_sln,f_w,True)
    best_F1 = objective(best_sln)
    xgb_log.write(str(best_F1) + '\n')
    f_w.close()

参考文献：
1、https://github.com/hyperopt/hyperopt/tree/master/hyperopt
2、https://github.com/FontTian/hyperopt-doc-zh/tree/master/hyperopt/doc/cn 相关文档的中文翻译
3、http://blog.csdn.net/a819825294/article/details/51775418