optunaで最適なパラメータを見つけたい

optuna を使って、最適なパラメータを見つけたいです。

python
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

(976, 315) (976,) (482, 315) (482,)

データは上の様になっています。
optunaを利用するにあたって、以下のメソッドを組みました。

python
def objective(trial):

    params = {
        'alphas': trial.suggest_int('alphas', 1, 100)
    }
    model = make_pipeline(RobustScaler(), RidgeCV(cv = kfolds, **params))
    model.fit(X_train, y_train)
    pre = model.predict(X_test)
    acc = metrics.accuracy_score(pre, y_test)
    score = 1 - acc
    return score

objectiveメソッドを使って、

python
study = optuna.create_study(sampler = optuna.samplers.RandomSampler(seed = 0))
study.optimize(objective, n_trials = 100)
print(study.best_params)

を実行したところ、

text
 Trial 0 failed because of the following error: TypeError('len() of unsized object')
Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/optuna/study/_optimize.py", line 213, in _run_trial
    value_or_values = func(trial)
  File "/tmp/ipykernel_57/646466574.py", line 7, in objective
    model.fit(X_train, y_train)
  File "/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py", line 335, in fit
    self._final_estimator.fit(Xt, y, **fit_params_last_step)
  File "/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py", line 1617, in fit
    parameters, cv=cv, scoring=self.scoring)
  File "/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py", line 72, in inner_f
    return f(**kwargs)
  File "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py", line 1184, in __init__
    _check_param_grid(param_grid)
  File "/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_search.py", line 395, in _check_param_grid
    if len(v) == 0:
TypeError: len() of unsized object

のエラーが出てしまいます。

違うデータセットなのですが、以下のコードだと動きます。

python
def objective(trial):
    params = {
        'kernel': 'rbf',
        'C': trial.suggest_loguniform('C', 1e-10, 1e10),
        'gamma': trial.suggest_loguniform('gamma', 1e-10, 3.0)
    }
    model_ = svm.SVC(**params)
    model_.fit(train_X, train_Y)
    pre = model_.predict(test_X)
    acc = metrics.accuracy_score(pre, test_Y)
    score = 1 - acc
    return score

study = optuna.create_study(sampler = optuna.samplers.RandomSampler(seed = 0))
study.optimize(objective, n_trials = 100)
print(study.best_params)