GridSearchCVで自作関数を使用したい

Python初心者なのですが、解析プログラムを作成しています。
DNNモデル構造をGridsearchで最適化しようと、自作関数を定義したところでエラーが出ました。
よろしければお知恵をお貸しください。どうぞよろしくお願い致します。

'float' object is not callable


def build_model(activation , optimizer):

    input_layer=Input(shape=(4,),dtype='float')
    model1=Dense(8)(input_layer)
    model1=Activation('relu')(model1)
    model1=Dropout(0.25)(model1)
    model2=Dense(4)(model1)
    model2=Activation(activation)(model2)
    model2=Dropout(0.25)(model2)
    model2=Dense(1)(model2)
    model3=Dense(4)(model1)
    model3=Activation(activation)(model3)
    model3=Dropout(0.25)(model3)
    model3=Dense(1)(model3)
    output_layer=Concatenate()([model2,model3])
    defmodel=Model(input=input_layer,outputs=output_layer)
    defmodel.summary()
    defmodel.compile(optimizer=optimizer,loss='mean_squared_logarithmic_error')
    return defmodel

activation = ["softmax"]
optimizer = ["adam"]
nb_epoch = [200]
batch_size = [50]

param_grid = dict(activation=activation,
                  optimizer=optimizer,
                  nb_epoch=nb_epoch,
                  batch_size=batch_size)
defmodel = KerasRegressor(build_fn=build_model, verbose=0)

def my_scorer(a, b):
    c=float(1.0)
    scoring_value = dll(a, b, float(c))　#dllは外部ライブラリで、三つの引数を受け取り、一つの値を返します。
    return scoring_value

from sklearn.metrics import make_scorer
my_func = make_scorer(my_scorer(a, b), greater_is_better=False)

grid = GridSearchCV(estimator=defmodel, param_grid=param_grid, scoring=my_func)

grid_result = grid.fit(info_headless, coeff)

試したこと

引数a,bはndarray型です。
自作関数を使わない状態では問題ありませんでした。
my_value=my_scorer(a, b)などとしても問題なく動作しますが、上記のコードだと最後の1行でエラーを出すようです。

補足情報（FW/ツールのバージョンなど）

ここにより詳細な情報を記載してください。

meg_

2020/08/07 12:08

エラーメッセージは全文掲載ください。

TR123

2020/08/07 12:17

失礼いたしました。合っているかわかりませんが、こちらでよろしいでしょうか。 Traceback (most recent call last): File "<input>", line 1, in <module> File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\utils\validation.py", line 73, in inner_f return f(**kwargs) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_search.py", line 736, in fit self._run_search(evaluate_candidates) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_search.py", line 1188, in _run_search evaluate_candidates(ParameterGrid(self.param_grid)) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_search.py", line 715, in evaluate_candidates cv.split(X, y, groups))) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 1029, in __call__ if self.dispatch_one_batch(iterator): File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 847, in dispatch_one_batch self._dispatch(tasks) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 765, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async result = ImmediateResult(func) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__ self.results = batch() File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 253, in __call__ for func, args, kwargs in self.items] File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 253, in <listcomp> for func, args, kwargs in self.items] File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_validation.py", line 560, in _fit_and_score test_scores = _score(estimator, X_test, y_test, scorer) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_validation.py", line 607, in _score scores = scorer(estimator, X_test, y_test) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\metrics\_scorer.py", line 88, in __call__ *args, **kwargs) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\metrics\_scorer.py", line 213, in _score **self._kwargs) TypeError: 'float' object is not callable

meg_

2020/08/07 14:28

私も詳しくはないのですが、「my_func = make_scorer(my_scorer(a, b), greater_is_better=False)」は「my_func = make_scorer(my_scorer, greater_is_better=False)」ではどうでしょうか？

TR123

2020/08/07 15:32

ご回答ありがとうございます。 index 16 is out of bounds for axis 0 with size 16 という別のエラーが出てしまうようです。 aとbが思うように渡せなくなるのかと思います。。。また思いつくことがあればぜひご教示くだされば幸いです。ありがとうございます。 Traceback (most recent call last): File "<input>", line 1, in <module> File "C:\Users\TR\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\201.6668.115\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "C:\Users\TR\AppData\Local\JetBrains\Toolbox\apps\PyCharm-P\ch-0\201.6668.115\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:/Users/TR/Documents/PyCharm/Project200802/toda200729_gridsearch_2.py", line 163, in <module> grid_result = grid.fit(info_headless, coeff) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\utils\validation.py", line 73, in inner_f return f(**kwargs) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_search.py", line 736, in fit self._run_search(evaluate_candidates) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_search.py", line 1188, in _run_search evaluate_candidates(ParameterGrid(self.param_grid)) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_search.py", line 715, in evaluate_candidates cv.split(X, y, groups))) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 1029, in __call__ if self.dispatch_one_batch(iterator): File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 847, in dispatch_one_batch self._dispatch(tasks) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 765, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\_parallel_backends.py", line 208, in apply_async result = ImmediateResult(func) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\_parallel_backends.py", line 572, in __init__ self.results = batch() File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 253, in __call__ for func, args, kwargs in self.items] File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\joblib\parallel.py", line 253, in <listcomp> for func, args, kwargs in self.items] File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_validation.py", line 560, in _fit_and_score test_scores = _score(estimator, X_test, y_test, scorer) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\model_selection\_validation.py", line 607, in _score scores = scorer(estimator, X_test, y_test) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\metrics\_scorer.py", line 88, in __call__ *args, **kwargs) File "C:\Users\TR\anaconda3\envs\Project200803\lib\site-packages\sklearn\metrics\_scorer.py", line 213, in _score **self._kwargs) File "C:/Users/TR/Documents/PyCharm/Project200802/toda200729_gridsearch_2.py", line 153, in my_scorer scoring_obj_value = pynlme.compute(parameters, coeff, adjustment) IndexError: index 16 is out of bounds for axis 0 with size 16