OSError : [Errno 22] Invalid argument

[hanzigs]

Hi, Can I have a help here please I am using this NatureInspiredSearchCV as

    grid = NatureInspiredSearchCV(model,
                                  cv=3,
                                  param_grid=model_parameters_space,
                                  verbose=0,
                                  algorithm='hba',
                                  population_size=50,
                                  max_n_gen=100,
                                  max_stagnating_gen=20,
                                  runs=5,
                                  scoring='accuracy',
                                #   n_jobs=-1,
                                  random_state=42)

If I comment n_jobs, it is working fine If I use n_jobs, I am getting below error, It looks like n_jobs is not working, not sure,

"""Exception occured: OSError : [Errno 22] Invalid argument (  File "C:\Python\Lib\site-packages\joblib\externals\loky\backend\resource_tracker.py", line 209, in _send
    nbytes = os.write(self._fd, msg)
  File "C:\Python\Lib\site-packages\joblib\externals\loky\backend\resource_tracker.py", line 182, in _check_alive
    self._send('PROBE', '', '')
  File "C:\Python\Lib\site-packages\joblib\externals\loky\backend\resource_tracker.py", line 102, in ensure_running
    if self._check_alive():
  File "C:\Python\Lib\site-packages\joblib\externals\loky\backend\spawn.py", line 86, in get_preparation_data
    _resource_tracker.ensure_running()
  File "C:\Python\Lib\site-packages\joblib\externals\loky\backend\popen_loky_win32.py", line 54, in __init__
    prep_data = spawn.get_preparation_data(
  File "C:\Python\Lib\site-packages\joblib\externals\loky\backend\process.py", line 39, in _Popen
    return Popen(process_obj)
  File "C:\Python\Lib\multiprocessing\process.py", line 121, in start
    self._popen = self._Popen(self)
  File "C:\Python\Lib\site-packages\joblib\externals\loky\process_executor.py", line 1087, in _adjust_process_count
    p.start()
  File "C:\Python\Lib\site-packages\joblib\externals\loky\process_executor.py", line 1096, in _ensure_executor_running
    self._adjust_process_count()
  File "C:\Python\Lib\site-packages\joblib\externals\loky\process_executor.py", line 1122, in submit
    self._ensure_executor_running()
  File "C:\Python\Lib\site-packages\joblib\externals\loky\reusable_executor.py", line 177, in submit
    return super(_ReusablePoolExecutor, self).submit(
  File "C:\Python\Lib\site-packages\joblib\_parallel_backends.py", line 531, in apply_async
    future = self._workers.submit(SafeFunction(func))
  File "C:\Python\Lib\site-packages\joblib\parallel.py", line 777, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Python\Lib\site-packages\joblib\parallel.py", line 859, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Python\Lib\site-packages\joblib\parallel.py", line 1041, in __call__
    if self.dispatch_one_batch(iterator):
  File "C:\Python\Lib\site-packages\sklearn\model_selection\_search.py", line 795, in evaluate_candidates
    out = parallel(delayed(_fit_and_score)(clone(base_estimator),
  File "C:\Python\Lib\site-packages\sklearn_nature_inspired_algorithms\model_selection\_parameter_search.py", line 38, in _evaluate
    cv_results = self.evaluate_candidates([params])
  File "C:\Python\Lib\site-packages\niapy\problems\problem.py", line 57, in evaluate
    return self._evaluate(x)
  File "C:\Python\Lib\site-packages\niapy\task.py", line 144, in eval
    x_f = self.problem.evaluate(x) * self.optimization_type.value
  File "C:\Python\Lib\site-packages\sklearn_nature_inspired_algorithms\model_selection\_stagnation_stopping_task.py", line 40, in eval
    x_f = super().eval(A)
  File "C:\Python\Lib\site-packages\numpy\lib\shape_base.py", line 379, in apply_along_axis
    res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
  File "<__array_function__ internals>", line 5, in apply_along_axis
  File "C:\Python\Lib\site-packages\niapy\algorithms\algorithm.py", line 38, in default_numpy_init
    fpop = np.apply_along_axis(task.eval, 1, pop)
  File "C:\Python\Lib\site-packages\niapy\algorithms\algorithm.py", line 258, in init_population
    pop, fpop = self.initialization_function(task=task, population_size=self.population_size, rng=self.rng,
  File "C:\Python\Lib\site-packages\niapy\algorithms\basic\ba.py", line 135, in init_population
    population, fitness, d = super().init_population(task)
  File "C:\Python\Lib\site-packages\niapy\algorithms\algorithm.py", line 308, in iteration_generator
    pop, fpop, params = self.init_population(task)
  File "C:\Python\Lib\site-packages\niapy\algorithms\algorithm.py", line 333, in run_task
    xb, fxb = next(algo)
  File "C:\Python\Lib\site-packages\niapy\algorithms\algorithm.py", line 353, in run
    r = self.run_task(task)
  File "C:\Python\Lib\site-packages\niapy\algorithms\algorithm.py", line 357, in run
    raise e
  File "C:\Python\Lib\site-packages\sklearn_nature_inspired_algorithms\model_selection\nature_inspired_search_cv.py", line 43, in _run_search
    self.__algorithm.run(task=task)
  File "C:\Python\Lib\site-packages\sklearn\model_selection\_search.py", line 841, in fit
    self._run_search(evaluate_candidates)
  File "C:\Python\Lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "C:\src\scripts\AutoMLUtils.py", line 775, in feHPTuning
    lgbmgrid_result = lgbmgrid.fit(X_train,
  File "C:\src\scripts\AutoMLUtils.py", line 865, in feHyperParameterSelection
    febest_hyperparameters = feHPTuning(X_train,
  File "C:\src\scripts\AutoMLUtils.py", line 1050, in featureEnggData
    FE_HParams = feHyperParameterSelection(X_train_stomek,
  File "C:\src\scripts\AutoMLTrainer.py", line 537, in train
    to_drop, FE_HParams, balancer_algo, balancer = featureEnggData(cleaned_df,
===
   at Python.Runtime.PyObject.Invoke(PyTuple args, PyDict kw)
   at Python.Runtime.PyObject.InvokeMethod(String name, PyTuple args, PyDict kw)
   at Python.Runtime.PyObject.TryInvokeMember(InvokeMemberBinder binder, Object[] args, Object& result)
   at CallSite.Target(Closure , CallSite , Object , String , Object )
   at System.Dynamic.UpdateDelegates.UpdateAndExecute3[T0,T1,T2,TRet](CallSite site, T0 arg0, T1 arg1, T2 arg2)
   at Intuition.AutoML.Trainer.Run(Guid tenantId, TrainingRunParameter parameter) in C:\src\src\Intuition.AutoML\Implementation\Trainer.cs:line 109)