predict_2017_07_06_3.py 文件源码

def xgb2(train2, y, test2, v, z):
    cname = sys._getframe().f_code.co_name
    N_splits = 9
    N_seeds = 4
    from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, space_eval
    dtrain = xgb.DMatrix(train2, y)
    def step_xgb(params):
        cv = xgb.cv(params=params,
                    dtrain=dtrain,
                    num_boost_round=10000,
                    early_stopping_rounds=100,
                    nfold=10,
                    seed=params['seed'])
        score = cv.ix[len(cv)-1, 0]
        print(cname, score, len(cv), params)
        return dict(loss=score, status=STATUS_OK)
    space_xgb = dict(
            max_depth = hp.choice('max_depth', range(2, 8)),
            subsample = hp.quniform('subsample', 0.6, 1, 0.05),
            colsample_bytree = hp.quniform('colsample_bytree', 0.6, 1, 0.05),
            learning_rate = hp.quniform('learning_rate', 0.005, 0.03, 0.005),
            min_child_weight = hp.quniform('min_child_weight', 1, 6, 1),
            gamma = hp.quniform('gamma', 0.5, 10, 0.05),

            objective = 'binary:logistic',
            eval_metric = 'logloss',
            seed = 1,
            silent = 1
        )
    trs = load_state(cname + '_trials')
    if trs == None:
        tr = Trials()
    else:
        tr, _ = trs
    if len(tr.trials) > 0: print('reusing %d trials, best was:'%(len(tr.trials)), space_eval(space_xgb, tr.argmin))
    for n in range(5):
        best = fmin(step_xgb, space_xgb, algo=tpe.suggest, max_evals=len(tr.trials) + 1, trials = tr)
        save_state(cname + '_trials', (tr, space_xgb))
    xgb_params = space_eval(space_xgb, best)
    print(xgb_params)
    xgb_common(train2, y, test2, v, z, N_seeds, N_splits, cname, xgb_params)