hyperopt Remove unnecessary arguments
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@ -290,7 +290,7 @@ class Hyperopt:
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# noinspection PyProtectedMember
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# noinspection PyProtectedMember
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attr.value = params_dict[attr_name]
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attr.value = params_dict[attr_name]
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def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict[str, Any]:
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def generate_optimizer(self, raw_params: List[Any]) -> Dict[str, Any]:
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"""
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"""
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Used Optimize function.
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Used Optimize function.
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Called once per epoch to optimize whatever is configured.
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Called once per epoch to optimize whatever is configured.
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@ -411,10 +411,10 @@ class Hyperopt:
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)
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)
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def run_optimizer_parallel(
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def run_optimizer_parallel(
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self, parallel: Parallel, asked: List[List], i: int) -> List[Dict[str, Any]]:
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self, parallel: Parallel, asked: List[List]) -> List[Dict[str, Any]]:
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""" Start optimizer in a parallel way """
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""" Start optimizer in a parallel way """
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return parallel(delayed(
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return parallel(delayed(
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wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
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wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
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def _set_random_state(self, random_state: Optional[int]) -> int:
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def _set_random_state(self, random_state: Optional[int]) -> int:
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return random_state or random.randint(1, 2**16 - 1)
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return random_state or random.randint(1, 2**16 - 1)
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@ -588,7 +588,7 @@ class Hyperopt:
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current_jobs = jobs - n_rest if n_rest > 0 else jobs
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current_jobs = jobs - n_rest if n_rest > 0 else jobs
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asked, is_random = self.get_asked_points(n_points=current_jobs)
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asked, is_random = self.get_asked_points(n_points=current_jobs)
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f_val = self.run_optimizer_parallel(parallel, asked, i)
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f_val = self.run_optimizer_parallel(parallel, asked)
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self.opt.tell(asked, [v['loss'] for v in f_val])
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self.opt.tell(asked, [v['loss'] for v in f_val])
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# Calculate progressbar outputs
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# Calculate progressbar outputs
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