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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import random |
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import numpy as np |
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import multiprocessing |
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from .checks import check_hyperactive_para, check_search_para |
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def stop_warnings(): |
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# because sklearn warnings are annoying when they appear 100 times |
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def warn(*args, **kwargs): |
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pass |
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import warnings |
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warnings.warn = warn |
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class ProcessArguments: |
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def __init__(self, args, kwargs, random_state): |
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self.kwargs = kwargs |
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self._set_default() |
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self._add_args2kwargs(args) |
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self.function_parameter = self.kwargs["function_parameter"] |
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self.search_space = self.kwargs["search_space"] |
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self.optimizer = self.kwargs["optimizer"] |
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self.random_state = random_state |
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self.n_jobs = self.kwargs["n_jobs"] |
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self.init_para = self.kwargs["init_para"] |
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self.set_n_jobs() |
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if isinstance(self.optimizer, dict): |
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optimizer = list(self.optimizer.keys())[0] |
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self.opt_para = self.optimizer[optimizer] |
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self.optimizer = optimizer |
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self.n_positions = self._get_n_positions() |
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print("n_positions", self.n_positions) |
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else: |
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self.opt_para = {} |
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self.n_positions = self._get_n_positions() |
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def _get_n_positions(self): |
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n_positions_strings = [ |
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"n_positions", |
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"system_temperatures", |
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"n_particles", |
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"individuals", |
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] |
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n_positions = 1 |
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for n_pos_name in n_positions_strings: |
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if n_pos_name in list(self.opt_para.keys()): |
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n_positions = self.opt_para[n_pos_name] |
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if n_positions == "system_temperatures": |
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n_positions = len(n_positions) |
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return n_positions |
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def set_n_jobs(self): |
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"""Sets the number of jobs to run in parallel""" |
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num_cores = multiprocessing.cpu_count() |
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if self.n_jobs == -1 or self.n_jobs > num_cores: |
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self.n_jobs = num_cores |
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def get_process_para(self): |
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pass |
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def _check_parameter(kwargs): |
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pass |
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def _add_args2kwargs(self, args): |
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for arg in args: |
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if callable(arg): |
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self.kwargs["objective_function"] = arg |
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elif isinstance(arg, dict): |
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self.kwargs["search_space"] = arg |
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def set_random_seed(self, thread): |
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"""Sets the random seed separately for each thread (to avoid getting the same results in each thread)""" |
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if self.random_state is None: |
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self.random_state = np.random.randint(0, high=2 ** 32 - 2) |
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random.seed(self.random_state + thread) |
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np.random.seed(self.random_state + thread) |
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def _set_default(self): |
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self.kwargs.setdefault("function_parameter", None) |
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self.kwargs.setdefault("memory", None) |
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self.kwargs.setdefault("optimizer", "RandomSearch") |
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self.kwargs.setdefault("n_iter", 10) |
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self.kwargs.setdefault("n_jobs", 1) |
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self.kwargs.setdefault("init_para", []) |
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self.kwargs.setdefault("distribution", None) |
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SimonBlanke /
Hyperactive
