ValueError: Unknown label type　原因がわからないです

解決したいこと

なぜvalue errorが出たのかがわかりません。解決法を知りたいです。
自動車の走行距離予測をしたのですが、モデル作成して学習する段階でエラーが発生しました。
グーグルで検索はしてみたものの同様のエラーを引き起こしている方が見受けられず、解決方法がよくわからなかったため質問しました。

発生している問題・エラー

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-48-9e7374166aef> in <module>
      1 study = optuna.create_study(sampler=optuna.samplers.RandomSampler(seed=0))
----> 2 study.optimize(objective, n_trials=20)
      3 study.best_params

/opt/conda/lib/python3.7/site-packages/optuna/study.py in optimize(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)
    337             if n_jobs == 1:
    338                 self._optimize_sequential(
--> 339                     func, n_trials, timeout, catch, callbacks, gc_after_trial, None
    340                 )
    341             else:

/opt/conda/lib/python3.7/site-packages/optuna/study.py in _optimize_sequential(self, func, n_trials, timeout, catch, callbacks, gc_after_trial, time_start)
    680                     break
    681 
--> 682             self._run_trial_and_callbacks(func, catch, callbacks, gc_after_trial)
    683 
    684             self._progress_bar.update((datetime.datetime.now() - time_start).total_seconds())

/opt/conda/lib/python3.7/site-packages/optuna/study.py in _run_trial_and_callbacks(self, func, catch, callbacks, gc_after_trial)
    711         # type: (...) -> None
    712 
--> 713         trial = self._run_trial(func, catch, gc_after_trial)
    714         if callbacks is not None:
    715             frozen_trial = copy.deepcopy(self._storage.get_trial(trial._trial_id))

/opt/conda/lib/python3.7/site-packages/optuna/study.py in _run_trial(self, func, catch, gc_after_trial)
    732 
    733         try:
--> 734             result = func(trial)
    735         except exceptions.TrialPruned as e:
    736             message = "Setting status of trial#{} as {}. {}".format(

<ipython-input-47-cd1aca4a1726> in objective(trial)
     48                            num_iteration=model.best_iteration) 
     49 
---> 50     score = log_loss(val_y,y_pred)
     51     return score

/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

/opt/conda/lib/python3.7/site-packages/sklearn/metrics/_classification.py in log_loss(y_true, y_pred, eps, normalize, sample_weight, labels)
   2192         lb.fit(labels)
   2193     else:
-> 2194         lb.fit(y_true)
   2195 
   2196     if len(lb.classes_) == 1:

/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/_label.py in fit(self, y)
    435 
    436         self.sparse_input_ = sp.issparse(y)
--> 437         self.classes_ = unique_labels(y)
    438         return self
    439 

/opt/conda/lib/python3.7/site-packages/sklearn/utils/multiclass.py in unique_labels(*ys)
     95     _unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
     96     if not _unique_labels:
---> 97         raise ValueError("Unknown label type: %s" % repr(ys))
     98 
     99     ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys))

ValueError: Unknown label type: (array([22.59451179, 28.90502359, 35.83456984, 35.56561512, 36.3013514 ,
       35.60750768, 35.86269225, 28.87491295, 35.61716994, 35.20647437,
       16.82089019, 26.11941417, 15.78761298, 36.01213921, 17.76725375,
       23.40500653, 22.86447728, 26.71916695, 28.45776812, 28.36142444,
       22.36496533, 27.68217789, 22.76016449, 36.05522341, 17.36098976,
       35.48743429, 28.35037671, 23.01227349, 22.68577205, 44.7184269 ,
       17.89870707, 34.44463986, 23.08124429, 35.89353561, 35.54715086,
       26.64345843, 23.20533902, 28.07958004, 33.67726238, 17.37683043,
       22.75968551, 22.55056677, 36.2740012 , 36.34073259, 22.05089714,
       17.37782491, 34.66072386, 35.49297302, 17.71885245, 34.66772443,
       36.40349381, 35.63239535, 23.37250712, 17.67452124, 17.34427513,
       17.25831382, 17.41499331, 23.0056828 , 22.82702594, 17.8254479 ,
       28.14694394, 26.74093766, 28.31097497, 23.2943741 , 17.30800176,
       22.71509557, 16.86599171, 17.69463678, 17.90628397, 17.87201811,
       28.20726497, 22.8104364 , 36.07334203, 36.01187998, 23.21599232,
       22.73956128, 22.78242384, 22.81369193, 35.64000983, 44.68008394,
       17.72888241, 35.67429971, 33.10170317, 23.00288713, 22.56051811,
       17.44334087, 28.24218354, 21.93320039, 25.70872862, 17.97721088,
       27.93024788, 17.93163347, 23.00870174, 35.76318905, 23.48625637,
       17.4500792 , 23.46950582, 28.60920431, 17.90062131, 22.80655247]),)

該当するソースコード

python
1
2# trainの目的変数と説明変数の値を取得
3target = train['mpg'].values
4features = train.drop(['id','mpg'],axis=1).values
5
6# testデータ
7test_X = test.drop(['id'],axis=1).values
8
9# trainを学習データと検証データに分割
10(features , val_X , target , val_y) = train_test_split(features, target , test_size = 0.2)
11
12    
13
14def objective(trial):
15    lgb_params = {
16        'task' : 'train',
17        'boosting_type' : 'gbdt',
18        'objective' : 'regression',
19        'metric' : {'l2'},
20        'num_leaves' : 31,
21        'learning_rate' : 0.1,
22        'feature_fraction' : 0.9,
23        'bagging_fraction' : 0.8,
24        'bagging_freq': 5,
25        'verbose' : 0 
26    }
27    lgb_train = lgb.Dataset(features, target) #学習用
28    
29    lgb_eval = lgb.Dataset(val_X, val_y,reference=lgb_train) #Boosting用
30    
31     #学習
32    model = lgb.train(lgb_params, lgb_train,
33                      valid_sets=[lgb_train,lgb_eval],
34                      num_boost_round=1000,
35                      early_stopping_rounds=10,
36                      verbose_eval=10)
37    
38    y_pred = model.predict(val_X,
39                           num_iteration=model.best_iteration) 
40    
41    score = log_loss(val_y,y_pred)
42    return score