Source code for PARyOpt.evaluators.async_parse_result_local

"""
---
    Copyright (c) 2018 Baskar Ganapathysubramanian, Balaji Sesha Sarath Pokuri
    
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"""
ParseResultEvaluator
An evaluator that only parses files periodically for function evaluations and
does not actually execute a script
"""
import tempfile
from datetime import datetime, timedelta
import os

import numpy as np
from typing import Union, Callable

from PARyOpt.evaluators.async import AsyncFunctionEvaluator, ValueNotReady, EvaluationFailed, EvaluateAgain


[docs]class AsyncLocalParseResultEvaluator(AsyncFunctionEvaluator): """ Fills files in a set of folders and periodically checks if the external evaluator has finished \ evaluation. Supports asynchronous evaluations. Only on a local machine. :var folder_num: folder into which the location values are written into :var job_generator: callable that sets up the run directory for a given x (by e.g. writing config files). \ It will be passed two arguments: the job directory and the point to evaluate at (x). :var jobs_dir: base directory where the jobs are written into :var parse_result: function to parse result from directory , if not specified, it will search in \ jobs_dir/folder_<folder_num>/if_parse.txt and y.txt :var total_folders: total number of folders to write into :var wait_time: min time to wait before parsing the results folder """ def __init__(self, parse_result: Callable[[str, np.array], float]= None, job_generator: Callable[[str, np.array], None]= None, jobs_dir: str= os.path.join(os.getcwd(), 'opt_jobs'), wait_time: float= timedelta(minutes=1), total_folders: int=16, max_pending: int= 0, required_fraction: float=1.0): super().__init__(required_fraction=required_fraction, max_pending=max_pending) self.job_generator = job_generator self.parse_result = parse_result self.jobs_dir = jobs_dir self.wait_time = wait_time self.folder_num = 0 self.total_folders = total_folders
[docs] def start(self, x: np.array) -> (str, datetime): """ function to start a cost function evaluation (write to file in this case) given the location \ of evaluation :param x: location of evaluation :return: folder name in which it was submitted """ if not os.path.exists(self.jobs_dir): os.mkdir(self.jobs_dir) folder_name = tempfile.mkdtemp(prefix='folder_', dir=self.jobs_dir) if self.job_generator is Callable: self.job_generator(folder_name, x) else: # default method to fill folder with simulation related files with open(folder_name + '/x.txt', 'a') as f: f.write('{}\n'.format(x)) self.folder_num += 1 # cycle the folder numbers so that the folders are reused self.folder_num %= self.total_folders
return folder_name, datetime.now()
[docs] def check_for_result(self, x: np.array, data: (str, datetime)) -> Union[ValueNotReady, EvaluationFailed, EvaluateAgain, float]: """ Function to check if a location has been evaluated or not (ValueNotReady). If it completes, categorize the \ result as one of a float , EvaluationFailed or EvaluateAgain :param x: location to evaluate :param data: directory :return: Union[ValueNotReady, EvaluationFailed, EvaluateAgain, float] """ submit_folder = data[0] submit_time = data[1] if datetime.now() - submit_time < self.wait_time: return ValueNotReady() if self.parse_result is Callable: return self.parse_result(x, data) else: # default parsing method if_parse = "False" y_val = None # check if the folder has to be parsed for cost function value with open(submit_folder + '/if_parse.txt', 'r') as f_parse: if_parse = f_parse.readline().strip() if if_parse == "False": # cost function evaluation not yet done return ValueNotReady() else: # cost function evaluation is done, parse y.txt with open(submit_folder + '/y.txt', 'r') as f: y_vals = f.readlines() # read all the lines in y.txt and # get the latest function value, which corresponds to the latest x.txt y_val = float(y_vals[-1]) if abs(y_val) > 1e5: # evaluation has failed return EvaluationFailed('parsed value is very large') else:
return float(y_val)