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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 warnings |
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from .main_args import MainArgs |
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from .opt_args import Arguments |
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from . import ( |
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HillClimbingOptimizer, |
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StochasticHillClimbingOptimizer, |
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TabuOptimizer, |
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RandomSearchOptimizer, |
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RandomRestartHillClimbingOptimizer, |
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RandomAnnealingOptimizer, |
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SimulatedAnnealingOptimizer, |
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StochasticTunnelingOptimizer, |
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ParallelTemperingOptimizer, |
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ParticleSwarmOptimizer, |
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EvolutionStrategyOptimizer, |
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BayesianOptimizer, |
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) |
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def stop_warnings(): |
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# because sklearn warnings are annoying when they appear 100 times |
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def warn(*args, **kwargs): |
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pass |
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import warnings |
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warnings.warn = warn |
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class Hyperactive: |
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def __init__(self, X, y, memory=True, random_state=1, verbosity=3, warnings=False): |
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self.X = X |
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self._main_args_ = MainArgs(X, y, memory, random_state, verbosity) |
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if not warnings: |
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stop_warnings() |
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self.optimizer_dict = { |
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"HillClimbing": HillClimbingOptimizer, |
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"StochasticHillClimbing": StochasticHillClimbingOptimizer, |
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"TabuSearch": TabuOptimizer, |
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"RandomSearch": RandomSearchOptimizer, |
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"RandomRestartHillClimbing": RandomRestartHillClimbingOptimizer, |
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"RandomAnnealing": RandomAnnealingOptimizer, |
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"SimulatedAnnealing": SimulatedAnnealingOptimizer, |
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"StochasticTunneling": StochasticTunnelingOptimizer, |
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"ParallelTempering": ParallelTemperingOptimizer, |
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"ParticleSwarm": ParticleSwarmOptimizer, |
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"EvolutionStrategy": EvolutionStrategyOptimizer, |
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"Bayesian": BayesianOptimizer, |
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} |
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def search( |
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self, |
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search_config, |
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n_iter=10, |
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max_time=None, |
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optimizer="RandomSearch", |
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n_jobs=1, |
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init_config=None, |
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): |
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start_time = time.time() |
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self._main_args_.search_args( |
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search_config, max_time, n_iter, optimizer, n_jobs, init_config |
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) |
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self._opt_args_ = Arguments(self._main_args_.opt_para) |
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optimizer_class = self.optimizer_dict[self._main_args_.optimizer] |
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try: |
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import ray |
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if ray.is_initialized(): |
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ray_ = True |
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else: |
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ray_ = False |
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except ImportError: |
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warnings.warn("failed to import ray", ImportWarning) |
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ray_ = False |
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if ray_: |
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optimizer_class = ray.remote(optimizer_class) |
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opts = [ |
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optimizer_class.remote(self._main_args_, self._opt_args_) |
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for job in range(self._main_args_.n_jobs) |
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] |
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searches = [ |
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opt.search.remote(job, ray_=ray_) for job, opt in enumerate(opts) |
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] |
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ray.get(searches) |
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else: |
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self._optimizer_ = optimizer_class(self._main_args_, self._opt_args_) |
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self._optimizer_.search() |
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self.results_params = self._optimizer_.results_params |
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self.results_models = self._optimizer_.results_models |
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self.pos_list = self._optimizer_.pos_list |
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self.score_list = self._optimizer_.score_list |
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self.total_time = time.time() - start_time |
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def get_total_time(self): |
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return self.total_time |
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def get_eval_time(self): |
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return self._optimizer_.eval_time |
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def save_report(self): |
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pass |
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SimonBlanke /
Hyperactive
