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"""optimization_strategy module for Hyperactive optimization.""" |
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# Email: [email protected] |
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# License: MIT License |
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from .optimizer_attributes import OptimizerAttributes |
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class BaseOptimizationStrategy(OptimizerAttributes): |
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"""BaseOptimizationStrategy class.""" |
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def __init__(self): |
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super().__init__() |
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View Code Duplication |
def setup_search( |
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self, |
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objective_function, |
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s_space, |
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n_iter, |
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initialize, |
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constraints, |
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pass_through, |
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callbacks, |
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catch, |
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max_score, |
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early_stopping, |
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random_state, |
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memory, |
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memory_warm_start, |
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verbosity, |
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): |
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"""Set up search parameters.""" |
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self.objective_function = objective_function |
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self.s_space = s_space |
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self.n_iter = n_iter |
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self.initialize = initialize |
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self.constraints = constraints |
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self.pass_through = pass_through |
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self.callbacks = callbacks |
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self.catch = catch |
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self.max_score = max_score |
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self.early_stopping = early_stopping |
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self.random_state = random_state |
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self.memory = memory |
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self.memory_warm_start = memory_warm_start |
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self.verbosity = verbosity |
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self._max_time = None |
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if "progress_bar" in self.verbosity: |
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self.verbosity = [] |
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else: |
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self.verbosity = [] |
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@property |
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def max_time(self): |
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"""Max Time function.""" |
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return self._max_time |
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@max_time.setter |
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def max_time(self, value): |
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"""Max Time function.""" |
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self._max_time = value |
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for optimizer_setup in self.optimizer_setup_l: |
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optimizer_setup["optimizer"].max_time = value |
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def search(self, nth_process, p_bar): |
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"""Search function.""" |
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for optimizer_setup in self.optimizer_setup_l: |
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hyper_opt = optimizer_setup["optimizer"] |
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duration = optimizer_setup["duration"] |
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opt_strat_early_stopping = optimizer_setup["early_stopping"] |
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if opt_strat_early_stopping: |
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early_stopping = opt_strat_early_stopping |
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else: |
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early_stopping = self.early_stopping |
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n_iter = round(self.n_iter * duration / self.duration_sum) |
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# initialize |
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if self.best_para is not None: |
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initialize = {} |
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if "warm_start" in initialize: |
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initialize["warm_start"].append(self.best_para) |
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else: |
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initialize["warm_start"] = [self.best_para] |
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else: |
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initialize = dict(self.initialize) |
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# memory_warm_start |
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if self.search_data is not None: |
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memory_warm_start = self.search_data |
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else: |
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memory_warm_start = self.memory_warm_start |
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# warm_start_smbo |
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if ( |
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hyper_opt.optimizer_class.optimizer_type == "sequential" |
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and self.search_data is not None |
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): |
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hyper_opt.opt_params["warm_start_smbo"] = self.search_data |
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hyper_opt.setup_search( |
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objective_function=self.objective_function, |
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s_space=self.s_space, |
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n_iter=n_iter, |
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initialize=initialize, |
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constraints=self.constraints, |
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pass_through=self.pass_through, |
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callbacks=self.callbacks, |
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catch=self.catch, |
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max_score=self.max_score, |
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early_stopping=early_stopping, |
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random_state=self.random_state, |
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memory=self.memory, |
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memory_warm_start=memory_warm_start, |
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verbosity=self.verbosity, |
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) |
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hyper_opt.search(nth_process, p_bar) |
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self._add_result_attributes( |
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hyper_opt.best_para, |
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hyper_opt.best_score, |
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hyper_opt.best_since_iter, |
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hyper_opt.eval_times, |
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hyper_opt.iter_times, |
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hyper_opt.search_data, |
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hyper_opt.gfo_optimizer.random_seed, |
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) |
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SimonBlanke /
Hyperactive
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