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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 time |
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import numpy as np |
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import multiprocessing |
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from functools import partial |
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from .base_positioner import BasePositioner |
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from .verb import VerbosityLVL0, VerbosityLVL1, VerbosityLVL2 |
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from .util import init_candidate, init_eval |
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from .candidate import Candidate |
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from meta_learn import HyperactiveWrapper |
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class BaseOptimizer: |
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def __init__(self, _core_, _arg_): |
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""" |
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Parameters |
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---------- |
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search_config: dict |
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A dictionary providing the model and hyperparameter search space for the |
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optimization process. |
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n_iter: int |
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The number of iterations the optimizer performs. |
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n_jobs: int, optional (default: 1) |
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The number of searches to run in parallel. |
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verbosity: int, optional (default: 1) |
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Verbosity level. 1 prints out warm_start points and their scores. |
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random_state: int, optional (default: None) |
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Sets the random seed. |
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warm_start: dict, optional (default: False) |
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Dictionary that definies a start point for the optimizer. |
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memory: bool, optional (default: True) |
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A memory, that saves the evaluation during the optimization to save time when |
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optimizer returns to position. |
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scatter_init: int, optional (default: False) |
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Defines the number n of random positions that should be evaluated with 1/n the |
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training data, to find a better initial position. |
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Returns |
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------- |
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None |
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""" |
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self._core_ = _core_ |
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self._arg_ = _arg_ |
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self._meta_ = None |
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self.search_config = self._core_.search_config |
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self.n_iter = self._core_.n_iter |
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if self._core_.meta_learn: |
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self._meta_ = HyperactiveWrapper(self._core_.search_config) |
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verbs = [VerbosityLVL0, VerbosityLVL1, VerbosityLVL2] |
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self._verb_ = verbs[_core_.verbosity]() |
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self.pos_list = [] |
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self.score_list = [] |
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def _init_base_positioner(self, _cand_, positioner=None): |
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if positioner: |
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_p_ = positioner(**self._arg_.kwargs_opt) |
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else: |
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_p_ = BasePositioner(**self._arg_.kwargs_opt) |
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_p_.pos_current = _cand_.pos_best |
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_p_.score_current = _cand_.score_best |
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return _p_ |
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def _update_pos(self, _cand_, _p_): |
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_cand_.pos_best = _p_.pos_new |
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_cand_.score_best = _p_.score_new |
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_p_.pos_current = _p_.pos_new |
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_p_.score_current = _p_.score_new |
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self._verb_.best_since_iter = _cand_.iter |
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return _cand_, _p_ |
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def _initialize_search(self, _core_, nth_process, X, y): |
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_cand_ = init_candidate(_core_, nth_process, Candidate) |
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_cand_ = init_eval(_cand_, nth_process, X, y) |
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_p_ = self._init_opt_positioner(_cand_, X, y) |
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self._verb_.init_p_bar(_cand_, self._core_) |
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if self._meta_: |
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meta_data = self._meta_.get_func_metadata(_cand_) |
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self._meta_.retrain(_cand_) |
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para, score = self._meta_.search(X, y, _cand_) |
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_cand_._space_.load_memory(*meta_data) |
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return _core_, _cand_, _p_ |
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def _finish_search(self, _core_, _cand_, X, y): |
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_cand_.eval_pos(_cand_.pos_best, X, y, force_eval=True) |
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self.eval_time = _cand_.eval_time_sum |
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self._verb_.close_p_bar() |
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return _cand_ |
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def search(self, nth_process, X, y): |
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self._core_, _cand_, _p_ = self._initialize_search( |
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self._core_, nth_process, X, y |
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) |
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for iter in range(self._core_.n_iter): |
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_cand_.iter = iter |
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_cand_ = self._iterate(iter, _cand_, _p_, X, y) |
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self._verb_.update_p_bar(1, _cand_) |
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run_time = time.time() - self.start_time |
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if self._core_.max_time and run_time > self._core_.max_time: |
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break |
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# get_search_path |
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if self._core_.get_search_path: |
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pos_list = [] |
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score_list = [] |
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if isinstance(_p_, list): |
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for p in _p_: |
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pos_list.append(p.pos_new) |
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score_list.append(p.score_new) |
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pos_list_ = np.array(pos_list) |
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score_list_ = np.array(score_list) |
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self.pos_list.append(pos_list_) |
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self.score_list.append(score_list_) |
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else: |
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pos_list.append(_p_.pos_new) |
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score_list.append(_p_.score_new) |
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pos_list_ = np.array(pos_list) |
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score_list_ = np.array(score_list) |
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self.pos_list.append(pos_list_) |
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self.score_list.append(score_list_) |
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_cand_ = self._finish_search(self._core_, _cand_, X, y) |
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return _cand_ |
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def _process_results(self, X, y, _cand_): |
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start_point = self._verb_.print_start_point(_cand_) |
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self.results_params[_cand_.func_] = start_point |
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self.results_models[_cand_.func_] = _cand_.model_best |
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if self._core_.meta_learn: |
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self._meta_.collect(X, y, _cand_) |
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def _search_multiprocessing(self, X, y): |
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"""Wrapper for the parallel search. Passes integer that corresponds to process number""" |
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pool = multiprocessing.Pool(self._core_.n_jobs) |
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search = partial(self.search, X=X, y=y) |
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_cand_list = pool.map(search, self._core_._n_process_range) |
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return _cand_list |
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def _run_one_job(self, X, y): |
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_cand_ = self.search(0, X, y) |
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self._process_results(X, y, _cand_) |
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def _run_multiple_jobs(self, X, y): |
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_cand_list = self._search_multiprocessing(X, y) |
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for i in range(int(self._core_.n_jobs / 2)): |
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print("\n") |
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for _cand_ in _cand_list: |
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self._process_results(X, y, _cand_) |
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def _fit(self, X, y): |
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"""Public method for starting the search with the training data (X, y) |
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Parameters |
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---------- |
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X : array-like or sparse matrix of shape = [n_samples, n_features] |
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y : array-like, shape = [n_samples] or [n_samples, n_outputs] |
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Returns |
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------- |
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None |
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""" |
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self.start_time = time.time() |
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self.results_params = {} |
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self.results_models = {} |
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if self._core_.n_jobs == 1: |
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self._run_one_job(X, y) |
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else: |
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self._run_multiple_jobs(X, y) |
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SimonBlanke /
Hyperactive
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