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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 copy |
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import inspect |
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import numpy as np |
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import pandas as pd |
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from .objective_function import ObjectiveFunction |
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from .hyper_gradient_trafo import HyperGradientTrafo |
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class TrafoClass: |
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def __init__(self, *args, **kwargs): |
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pass |
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def _convert_args2gfo(self, memory_warm_start): |
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memory_warm_start = self.trafo.trafo_memory_warm_start(memory_warm_start) |
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return memory_warm_start |
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def _positions2results(self, positions): |
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results_dict = {} |
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for para_name in self.conv.para_names: |
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values_list = self.search_space[para_name] |
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pos_ = positions[para_name].values |
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values_ = [values_list[idx] for idx in pos_] |
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results_dict[para_name] = values_ |
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results = pd.DataFrame.from_dict(results_dict) |
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diff_list = np.setdiff1d(positions.columns, results.columns) |
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results[diff_list] = positions[diff_list] |
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return results |
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def _convert_results2hyper(self): |
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self.eval_time = np.array(self._optimizer.eval_times).sum() |
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self.iter_time = np.array(self._optimizer.iter_times).sum() |
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if self._optimizer.best_para is not None: |
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value = self.trafo.para2value(self._optimizer.best_para) |
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position = self.trafo.position2value(value) |
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best_para = self.trafo.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._optimizer.best_score |
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self.positions = self._optimizer.results |
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self.results = self._positions2results(self.positions) |
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results_dd = self._optimizer.results.drop_duplicates( |
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subset=self.trafo.para_names, keep="first" |
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) |
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self.memory_values_df = results_dd[ |
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self.trafo.para_names + ["score"] |
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].reset_index(drop=True) |
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class _BaseOptimizer_(TrafoClass): |
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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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def init(self, search_space, initialize, data_c): |
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self.search_space = search_space |
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self.initialize = initialize |
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self.data_c = data_c |
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self.trafo = HyperGradientTrafo(search_space) |
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initialize = self.trafo.trafo_initialize(initialize) |
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search_space_positions = self.trafo.search_space_positions |
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# trafo 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"] = self.trafo.trafo_memory_warm_start( |
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self.opt_params["warm_start_smbo"] |
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) |
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self._optimizer = self._OptimizerClass( |
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search_space_positions, initialize, **self.opt_params |
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) |
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self.conv = self._optimizer.conv |
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def check_LTM(self, memory): |
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try: |
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memory.study_id |
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memory.model_id |
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except: |
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self.memory = memory |
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else: |
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self.init_ltm(memory) |
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def init_ltm(self, memory): |
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self.ltm = copy.deepcopy(memory) |
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self.ltm.init_study( |
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self.objective_function, self.search_space, self.nth_process |
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) |
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self.memory_warm_start = self.ltm.load() |
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self.memory = True |
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print("\n self.memory_warm_start \n", self.memory_warm_start) |
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def search( |
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self, |
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objective_function, |
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n_iter, |
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max_time=None, |
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max_score=None, |
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memory=True, |
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memory_warm_start=None, |
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verbosity={ |
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"progress_bar": True, |
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"print_results": True, |
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"print_times": True, |
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}, |
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random_state=None, |
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nth_process=None, |
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): |
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self.objective_function = objective_function |
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self.nth_process = nth_process |
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gfo_wrapper_model = ObjectiveFunction( |
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objective_function, self._optimizer, nth_process |
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) |
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# ltm init |
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self.check_LTM(memory) |
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memory_warm_start = self._convert_args2gfo(memory_warm_start) |
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self._optimizer.search( |
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gfo_wrapper_model(self.search_space, self.data_c), |
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n_iter, |
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max_time, |
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max_score, |
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memory, |
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memory_warm_start, |
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verbosity, |
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random_state, |
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nth_process, |
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) |
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self._convert_results2hyper() |
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self.p_bar = self._optimizer.p_bar |
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# ltm save after finish |
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""" |
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if inspect.isclass(type(memory)): |
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self.ltm.save_on_finish(self.results) |
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""" |
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SimonBlanke /
Hyperactive
