Scikit learn custom scoring function - Specificity - scikit-learn

Question

I'm trying to do a random grid search on randomforestclassifier.
# Instantiate a RandomForestClassifier
RFC = RandomForestClassifier()
# Instantiate the RandomizedSearchCV object: RFC
rand_search3 = RandomizedSearchCV(RFC, param_grid, n_iter=10, cv=5,n_jobs=-1, verbose=1, scoring = "f1_macro")
# Fit it to the data
rand_search3.fit(X_train_transformed,y_train)
I'm trying to get the best model by assessing specificity.
Went through the documentation for custom scoring. Also looked at lots of posts that are related. I have came up with 2 ways for the specificity.
1 :
from sklearn.metrics import make_scorer
def my_custom_func(y_true, y_pred):
cm = confusion_matrix(y_true, y_pred)
return cm[1][1] / (cm[1][0] + cm[1][1])
Specificity_score = make_scorer(my_custom_func, greater_is_better=True)
2:
from sklearn.metrics import recall_score
from sklearn.metrics import make_scorer
specificity = make_scorer(recall_score, pos_label=0, greater_is_better=True)
specificity
When I try using the custom function for the scoring,
rand_search3 = RandomizedSearchCV(RFC, param_grid, n_iter=10, cv=5,n_jobs=-1, verbose=1, scoring = Specificity_score)
rand_search3 = RandomizedSearchCV(RFC, param_grid, n_iter=10, cv=5,n_jobs=-1, verbose=1, scoring = specificity)
both failed with the same error message.
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_10548/1204393696.py in <module>
20
21 # Fit it to the data
---> 22 rand_search3.fit(X_train_transformed,y_train)
23
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
61 #wraps(f)
62 def inner_f(*args, **kwargs):
---> 63 extra_args = len(args) - len(all_args)
64 if extra_args <= 0:
65 return f(*args, **kwargs)
~\anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
839 delayed(_fit_and_score)(
840 clone(base_estimator),
--> 841 X,
842 y,
843 train=train,
~\anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
1631 Mean cross-validated score of the best_estimator.
1632
-> 1633 For multi-metric evaluation, this is not available if ``refit`` is
1634 ``False``. See ``refit`` parameter for more information.
1635
~\anaconda3\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params, cv, more_results)
825 def evaluate_candidates(candidate_params, cv=None, more_results=None):
826 cv = cv or cv_orig
--> 827 candidate_params = list(candidate_params)
828 n_candidates = len(candidate_params)
829
~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _insert_error_scores(results, error_score)
293
294 results = _aggregate_score_dicts(results)
--> 295
296 ret = {}
297 ret["fit_time"] = results["fit_time"]
KeyError: 'fit_failed'
Any solutions?