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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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import multiprocessing |
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from functools import partial |
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from .base_positioner import BasePositioner |
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from .sub_packages import MetaLearn |
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from .util import initialize_search, finish_search_, sort_for_best |
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class BaseOptimizer: |
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def __init__(self, _config_, _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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metric: string, optional (default: "accuracy") |
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The metric the model is evaluated by. |
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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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cv: int, optional (default: 3) |
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The number of folds for the cross validation. |
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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._config_ = _config_ |
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self._arg_ = _arg_ |
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if self._config_.warnings: |
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print("Warning: Since v0.5.0, this API is no longer supported.") |
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print("Please use the new hyperactive API in the following link:") |
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print("https://github.com/SimonBlanke/Hyperactive#how-to-use-hyperactive") |
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if self._config_.meta_learn: |
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self._meta_ = MetaLearn(self._config_.search_config) |
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self.search_config = self._config_.search_config |
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self.n_iter = self._config_.n_iter |
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if self._config_.get_search_path: |
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self.pos_list = [] |
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self.score_list = [] |
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def _hill_climb_iteration(self, _cand_, _p_, X, y): |
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_p_.pos_new = _p_.move_climb(_cand_, _p_.pos_current) |
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_p_.score_new = _cand_.eval_pos(_p_.pos_new, X, y) |
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if _p_.score_new > _cand_.score_best: |
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_cand_, _p_ = self._update_pos(_cand_, _p_) |
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return _cand_, _p_ |
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def _init_base_positioner(self, _cand_, positioner=None, pos_para={}): |
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if positioner: |
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_p_ = positioner(**pos_para) |
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else: |
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_p_ = BasePositioner(**pos_para) |
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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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return _cand_, _p_ |
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def search(self, nth_process, X, y): |
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self._config_, _cand_ = initialize_search(self._config_, nth_process, X, y) |
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_p_ = self._init_opt_positioner(_cand_, X, y) |
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for i in range(self._config_.n_iter): |
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_cand_ = self._iterate(i, _cand_, _p_, X, y) |
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self._config_.update_p_bar(1) |
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if self._config_.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_ = finish_search_(self._config_, _cand_, X, y) |
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return _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._config_.n_jobs) |
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search = partial(self.search, X=X, y=y) |
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_cand_list = pool.map(search, self._config_._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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if self._config_.meta_learn: |
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self._meta_.collect(X, y, _cand_list=[_cand_]) |
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self.model_best = _cand_.model |
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self.score_best = _cand_.score_best |
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start_point = _cand_._get_warm_start() |
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if self._config_.verbosity: |
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# print("\n", self._config_.metric, self.score_best) |
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print("start_point =", start_point) |
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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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start_point_list = [] |
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score_best_list = [] |
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model_best_list = [] |
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for _cand_ in _cand_list: |
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model_best = _cand_.model |
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score_best = _cand_.score_best |
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start_point = _cand_._get_warm_start() |
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start_point_list.append(start_point) |
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score_best_list.append(score_best) |
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model_best_list.append(model_best) |
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start_point_sorted, score_best_sorted = sort_for_best( |
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start_point_list, score_best_list |
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) |
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model_best_sorted, score_best_sorted = sort_for_best( |
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model_best_list, score_best_list |
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) |
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if self._config_.verbosity: |
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print("\nList of start points (best first):") |
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for start_point, score_best in zip(start_point_sorted, score_best_sorted): |
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# print("\n", self._config_.metric, score_best) |
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print("start_point =", start_point) |
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self.score_best = score_best_sorted[0] |
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self.model_best = model_best_sorted[0] |
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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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X, y = self._config_._check_data(X, y) |
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# if self._config_.model_type == "keras": |
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# self._config_.n_jobs = 1 |
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if self._config_.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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