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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 random |
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import numpy as np |
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import multiprocessing |
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class MainArgs: |
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def __init__(self, X, y, verbosity, random_state, memory): |
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self.X = X |
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self.y = y |
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self.verbosity = verbosity |
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self.random_state = random_state |
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self.memory = memory |
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self.get_search_path = False |
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if verbosity > 9: |
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self.get_search_path = True |
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if self.verbosity > 2: |
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self.verbosity = 2 |
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self.opt_para = dict() |
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def search_args( |
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self, |
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search_config, |
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max_time, |
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n_iter, |
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optimizer, |
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n_jobs, |
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warm_start, |
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scatter_init, |
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): |
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self.search_config = search_config |
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self.max_time = max_time |
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self.n_iter = n_iter |
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self.optimizer = optimizer |
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self.n_jobs = n_jobs |
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self.warm_start = warm_start |
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self.scatter_init = scatter_init |
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self.model_list = list(self.search_config.keys()) |
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self.n_models = len(self.model_list) |
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if self.max_time: |
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self.max_time = self.max_time * 3600 |
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self.set_n_jobs() |
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self._n_process_range = range(0, int(self.n_jobs)) |
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if isinstance(optimizer, dict): |
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self.optimizer = list(optimizer.keys())[0] |
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self.opt_para = optimizer[self.optimizer] |
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def _set_random_seed(self, thread=0): |
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"""Sets the random seed separately for each thread (to avoid getting the same results in each thread)""" |
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if self.random_state: |
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rand = int(self.random_state) |
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else: |
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rand = 0 |
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random.seed(rand + thread) |
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np.random.seed(rand + thread) |
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def set_n_jobs(self): |
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"""Sets the number of jobs to run in parallel""" |
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num_cores = multiprocessing.cpu_count() |
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if self.n_jobs == -1 or self.n_jobs > num_cores: |
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self.n_jobs = num_cores |
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SimonBlanke /
Hyperactive
