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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 numpy as np |
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from .objective_function import ObjectiveFunction |
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from .hyper_gradient_conv import HyperGradientConv |
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from .optimizer_attributes import OptimizerAttributes |
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from .constraint import Constraint |
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class HyperOptimizer(OptimizerAttributes): |
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def __init__(self, **opt_params): |
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super().__init__() |
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self.opt_params = opt_params |
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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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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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if "progress_bar" in self.verbosity: |
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self.verbosity = ["progress_bar"] |
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else: |
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self.verbosity = [] |
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def convert_results2hyper(self): |
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self.eval_times = sum(self.gfo_optimizer.eval_times) |
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self.iter_times = sum(self.gfo_optimizer.iter_times) |
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if self.gfo_optimizer.best_para is not None: |
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value = self.hg_conv.para2value(self.gfo_optimizer.best_para) |
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position = self.hg_conv.position2value(value) |
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best_para = self.hg_conv.value2para(position) |
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self.best_para = best_para |
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else: |
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self.best_para = None |
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self.best_score = self.gfo_optimizer.best_score |
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self.positions = self.gfo_optimizer.search_data |
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self.search_data = self.hg_conv.positions2results(self.positions) |
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results_dd = self.gfo_optimizer.search_data.drop_duplicates( |
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subset=self.s_space.dim_keys, keep="first" |
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) |
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self.memory_values_df = results_dd[ |
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self.s_space.dim_keys + ["score"] |
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].reset_index(drop=True) |
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def _setup_process(self, nth_process): |
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self.nth_process = nth_process |
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self.hg_conv = HyperGradientConv(self.s_space) |
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initialize = self.hg_conv.conv_initialize(self.initialize) |
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search_space_positions = self.s_space.positions |
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# conv warm start for smbo from values into positions |
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if "warm_start_smbo" in self.opt_params: |
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self.opt_params["warm_start_smbo"] = ( |
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self.hg_conv.conv_memory_warm_start( |
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self.opt_params["warm_start_smbo"] |
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) |
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) |
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gfo_constraints = [ |
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Constraint(constraint, self.s_space) |
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for constraint in self.constraints |
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] |
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self.gfo_optimizer = self.optimizer_class( |
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search_space=search_space_positions, |
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initialize=initialize, |
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constraints=gfo_constraints, |
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random_state=self.random_state, |
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nth_process=nth_process, |
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**self.opt_params, |
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) |
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self.conv = self.gfo_optimizer.conv |
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def search(self, nth_process, p_bar): |
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self._setup_process(nth_process) |
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gfo_wrapper_model = ObjectiveFunction( |
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objective_function=self.objective_function, |
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optimizer=self.gfo_optimizer, |
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callbacks=self.callbacks, |
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catch=self.catch, |
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nth_process=self.nth_process, |
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) |
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gfo_wrapper_model.pass_through = self.pass_through |
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memory_warm_start = self.hg_conv.conv_memory_warm_start( |
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self.memory_warm_start |
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) |
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gfo_objective_function = gfo_wrapper_model(self.s_space()) |
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self.gfo_optimizer.init_search( |
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gfo_objective_function, |
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self.n_iter, |
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self.max_time, |
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self.max_score, |
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self.early_stopping, |
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self.memory, |
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memory_warm_start, |
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False, |
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) |
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for nth_iter in range(self.n_iter): |
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if p_bar: |
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p_bar.set_description( |
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"[" |
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+ str(nth_process) |
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+ "] " |
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+ str(self.objective_function.__name__) |
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+ " (" |
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+ self.optimizer_class.name |
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+ ")", |
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) |
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self.gfo_optimizer.search_step(nth_iter) |
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if self.gfo_optimizer.stop.check(): |
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break |
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if p_bar: |
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p_bar.set_postfix( |
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best_score=str(gfo_wrapper_model.optimizer.score_best), |
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best_pos=str(gfo_wrapper_model.optimizer.pos_best), |
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best_iter=str( |
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gfo_wrapper_model.optimizer.p_bar._best_since_iter |
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), |
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) |
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p_bar.update(1) |
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p_bar.refresh() |
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self.gfo_optimizer.finish_search() |
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self.convert_results2hyper() |
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self._add_result_attributes( |
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self.best_para, |
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self.best_score, |
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self.gfo_optimizer.p_bar._best_since_iter, |
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self.eval_times, |
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self.iter_times, |
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self.search_data, |
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self.gfo_optimizer.random_seed, |
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) |
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SimonBlanke /
Hyperactive
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