Traceback (most recent call last):


 "D:\Projects\tactory\autogluon\tabular\trainer\abstract_trainer.py", line 1075, in _train_single_full

hpo_models, hpo_model_performances, hpo_results = model.hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=hyperparameter_tune_kwargs, **model_fit_kwargs)


 "D:\Projects\tactory\autogluon\core\models\abstract\abstract_model.py", line 683, in hyperparameter_tune

return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)


 "D:\Projects\tactory\autogluon\core\models\ensemble\stacker_ensemble_model.py", line 166, in _hyperparameter_tune

return super()._hyperparameter_tune(X=X, y=y, k_fold=k_fold, scheduler_options=scheduler_options, preprocess_kwargs=preprocess_kwargs, **kwargs)


 "D:\Projects\tactory\autogluon\core\models\ensemble\bagged_ensemble_model.py", line 640, in _hyperparameter_tune

hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs)


 "D:\Projects\tactory\autogluon\core\models\abstract\abstract_model.py", line 683, in hyperparameter_tune

return self._hyperparameter_tune(scheduler_options=scheduler_options, **kwargs)


 "D:\Projects\tactory\autogluon\tabular\models\rf\rf_model.py", line 166, in _hyperparameter_tune

return skip_hpo(self, **kwargs)


 "D:\Projects\tactory\autogluon\core\models\abstract\model_trial.py", line 90, in skip_hpo

fit_and_save_model(model=model, params=dict(), fit_args=fit_model_args, predict_proba_args=predict_proba_args, y_val=y_val, time_start=time.time(), time_limit=time_limit)


 "D:\Projects\tactory\autogluon\core\models\abstract\model_trial.py", line 73, in fit_and_save_model

model.fit(**fit_args, time_limit=time_left, reporter=reporter)


 "D:\Projects\tactory\autogluon\core\models\abstract\abstract_model.py", line 405, in fit

self._fit(**kwargs)


 "D:\Projects\tactory\autogluon\tabular\models\rf\rf_model.py", line 117, in _fit

self.model = self.model.fit(X, y, sample_weight=sample_weight)


 "C:\Users\john\AppData\Roaming\Python\Python37\site-packages\sklearn\ensemble\_forest.py", line 392, in fit

for i, t in enumerate(trees))


 "D:\Anaconda3\envs\tactory\lib\site-packages\joblib\parallel.py", line 1054, in __call__

self.retrieve()


 "D:\Anaconda3\envs\tactory\lib\site-packages\joblib\parallel.py", line 933, in retrieve

self._output.extend(job.get(timeout=self.timeout))


 "D:\Anaconda3\envs\tactory\lib\multiprocessing\pool.py", line 657, in get

raise self._value


 "D:\Anaconda3\envs\tactory\lib\multiprocessing\pool.py", line 121, in worker

result = (True, func(*args, **kwds))


 "D:\Anaconda3\envs\tactory\lib\site-packages\joblib\_parallel_backends.py", line 595, in __call__

return self.func(*args, **kwargs)


 "D:\Anaconda3\envs\tactory\lib\site-packages\joblib\parallel.py", line 263, in __call__

for func, args, kwargs in self.items]


 "D:\Anaconda3\envs\tactory\lib\site-packages\joblib\parallel.py", line 263, in <listcomp>

for func, args, kwargs in self.items]


 "C:\Users\john\AppData\Roaming\Python\Python37\site-packages\sklearn\ensemble\_forest.py", line 168, in _parallel_build_trees

tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)


 "C:\Users\john\AppData\Roaming\Python\Python37\site-packages\sklearn\tree\_classes.py", line 894, in fit

X_idx_sorted=X_idx_sorted)


 "C:\Users\john\AppData\Roaming\Python\Python37\site-packages\sklearn\tree\_classes.py", line 276, in fit

if max_depth <= 0:
TypeError: '<=' not supported between instances of 'Int' and 'int'

Traceback (most recent call last):


 "/root/synapse/env/bin/synapse_port_db", line 1056, in <module>

sys.stderr.write(end_error)
TypeError: write() argument must be str, not FirstError
