None Type error on optunity.maximize function

[hsuecu]

this is my function wich need to be maximized `

def performance_lr(x_train, y_train, x_test, y_test, penalty=None, tol=None, C=None, intercept_scaling=None, solver=None):

    def mapper(f, breakpoint=[], cat=[]):
        return cat[bisect(breakpoint, f)]

    penalty=mapper(penalty, breakpoint=[0.25, 0.5, 0.75],
                   cat=['none', 'l1', 'l2', 'elasticnet'])

    solver=mapper(solver, breakpoint=[0.2, 0.4, 0.6, 0.8], cat=['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'])
    print(f'penalty:{penalty}, solver:{solver}')
    
    model = LogisticRegression(penalty=penalty, tol=tol, C=C,
                               intercept_scaling=intercept_scaling,
                               solver=solver, n_jobs=-1, random_state=42)
    
    scores = np.mean(cross_val_score(model, X, y, cv=3, n_jobs=-1,
                                    scoring='accuracy'))

`

but there seems to an error to come which is `

TypeError Traceback (most recent call last) in ----> 1 optimal_confg, info, _ = optunity.maximize(performance_lr, 2 solver_name='particle swarm', 3 num_evals=50, 4 **search)

/usr/local/lib/python3.8/dist-packages/optunity/api.py in maximize(f, num_evals, solver_name, pmap, **kwargs) 178 suggestion = suggest_solver(num_evals, solver_name, **kwargs) 179 solver = make_solver(**suggestion) --> 180 solution, details = optimize(solver, f, maximize=True, max_evals=num_evals, 181 pmap=pmap) 182 return solution, details, suggestion

/usr/local/lib/python3.8/dist-packages/optunity/api.py in optimize(solver, func, maximize, max_evals, pmap, decoder) 243 time = timeit.default_timer() 244 try: --> 245 solution, report = solver.optimize(f, maximize, pmap=pmap) 246 except fun.MaximumEvaluationsException: 247 # early stopping because maximum number of evaluations is reached

/usr/local/lib/python3.8/dist-packages/optunity/solvers/ParticleSwarm.py in optimize(self, f, maximize, pmap) 269 for g in range(self.num_generations): 270 fitnesses = pmap(evaluate, list(map(self.particle2dict, pop))) --> 271 for part, fitness in zip(pop, fitnesses): 272 part.fitness = fit * util.score(fitness) 273 if not part.best or part.best_fitness < part.fitness:

/usr/local/lib/python3.8/dist-packages/optunity/solvers/ParticleSwarm.py in evaluate(d) 257 @functools.wraps(f) 258 def evaluate(d): --> 259 return f(**d) 260 261 if maximize:

/usr/local/lib/python3.8/dist-packages/optunity/functions.py in wrapped_f(*args, **kwargs) 299 value = wrapped_f.call_log.get(*args, **kwargs) 300 if value is None: --> 301 value = f(*args, **kwargs) 302 wrapped_f.call_log.insert(value, *args, **kwargs) 303 return value

/usr/local/lib/python3.8/dist-packages/optunity/functions.py in wrapped_f(*args, **kwargs) 354 else: 355 wrapped_f.num_evals += 1 --> 356 return f(*args, **kwargs) 357 wrapped_f.num_evals = 0 358 return wrapped_f

/usr/local/lib/python3.8/dist-packages/optunity/constraints.py in wrapped_f(*args, **kwargs) 149 def wrapped_f(*args, **kwargs): 150 try: --> 151 return f(*args, **kwargs) 152 except ConstraintViolation: 153 return default

/usr/local/lib/python3.8/dist-packages/optunity/constraints.py in wrapped_f(*args, **kwargs) 127 if violations: 128 raise ConstraintViolation(violations, *args, **kwargs) --> 129 return f(*args, **kwargs) 130 wrapped_f.constraints = constraints 131 return wrapped_f

/usr/local/lib/python3.8/dist-packages/optunity/constraints.py in func(*args, **kwargs) 264 @functions.wraps(f) 265 def func(*args, **kwargs): --> 266 return f(*args, **kwargs) 267 return func 268

/usr/local/lib/python3.8/dist-packages/optunity/cross_validation.py in call(self, *args, **kwargs) 402 kwargs['y_test'] = select(self.y, rows_test) 403 scores.append(self.f(**kwargs)) --> 404 return self.reduce(scores) 405 406 def getattr(self, name):

/usr/local/lib/python3.8/dist-packages/optunity/cross_validation.py in mean(x) 235 236 def mean(x): --> 237 return float(sum(x)) / len(x) 238 239 def mean_and_list(x):

TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

`