A simple function which takes model and parameter space as input and to tune the hyperparameters of a model using Grid Search or Random Search method selected

hyperparameter_tuning_gridsearchCV_randomsearchCV.py

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, StratifiedKFold
from sklearn.metrics         import accuracy_score

# configure the cross-validation procedure
kf = StratifiedKFold(n_splits = 10 , shuffle = True , random_state = 42)

def tune_hyperparameter(search_method, estimator, search_space):
    
    # enumerate splits
    outer_results = list()
    for train_ix, test_ix in kf.split(X,y):
            
        # split data
        X_train, X_test = X[train_ix, :], X[test_ix, :]
        y_train, y_test = y[train_ix], y[test_ix]

        # configure the cross-validation procedure
        cv_inner = KFold(n_splits=5, shuffle=True, random_state=1)

        if search_method == "grid":
            clf = GridSearchCV(
                estimator=estimator, 
                param_grid=search_space, 
                scoring='accuracy',
                n_jobs=-1, 
                cv=cv_inner, 
                verbose=0,
                refit=True
            )
        elif search_method == "random":           
            clf = RandomizedSearchCV(
                estimator=estimator,
                param_distributions=search_space,
                n_iter=10,
                n_jobs=-1,
                cv=cv_inner,
                verbose=0,
                random_state=1,
                refit=True
            )
            
        # execute grid search
        result = clf.fit(X_train, y_train)

        # get the best performing model fit on the whole training set
        best_model = result.best_estimator_

        # evaluate model on the hold out dataset
        yhat = best_model.predict(X_test)

        # evaluate the model
        acc = accuracy_score(y_test, yhat)

        # store the result
        outer_results.append(acc)

        # report progress
        print('acc=%.3f, est=%.3f, cfg=%s' % (acc, result.best_score_, result.best_params_))
        
    # summarize the estimated performance of the model
    print('Accuracy: %.3f (%.3f)' % (np.mean(outer_results), np.std(outer_results)))

    print("Best",search_method,"Model : ", best_model)
    print("-"*50, '\n\n')
    return best_model