Changed back to progressbar2 for better handling of logger.
Coloring still needs some work (bug + what colors to use)
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Fredrik81
parent
5737139979
commit
d5609d4997
@ -22,7 +22,7 @@ from colorama import init as colorama_init
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from joblib import (Parallel, cpu_count, delayed, dump, load,
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wrap_non_picklable_objects)
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from pandas import DataFrame, json_normalize, isna
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from tqdm import tqdm
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import progressbar
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import tabulate
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from os import path
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import io
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@ -44,7 +44,8 @@ with warnings.catch_warnings():
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from skopt import Optimizer
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from skopt.space import Dimension
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progressbar.streams.wrap_stderr()
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progressbar.streams.wrap_stdout()
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logger = logging.getLogger(__name__)
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@ -682,55 +683,67 @@ class Hyperopt:
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logger.info(f'Effective number of parallel workers used: {jobs}')
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# Define progressbar
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self.progress_bar = tqdm(
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total=self.total_epochs, ncols=108, unit=' Epoch',
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bar_format='Epoch {n_fmt}/{total_fmt} ({percentage:3.0f}%)|{bar}|'
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' [{elapsed}<{remaining} {rate_fmt}{postfix}]'
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)
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if self.print_colorized:
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widgets = [
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' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
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' (', progressbar.Percentage(), ')] ',
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progressbar.Bar(marker=progressbar.AnimatedMarker(
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fill='█',
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fill_wrap='\x1b[32m{}\x1b[39m',
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marker_wrap='\x1b[31m{}\x1b[39m',
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)),
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' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
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]
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else:
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widgets = [
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' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs), '] ',
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progressbar.Bar(marker='█'),
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' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
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]
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with progressbar.ProgressBar(
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maxval=self.total_epochs, redirect_stdout=True, redirect_stderr=True,
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widgets=widgets
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) as pbar:
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EVALS = ceil(self.total_epochs / jobs)
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for i in range(EVALS):
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# Correct the number of epochs to be processed for the last
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# iteration (should not exceed self.total_epochs in total)
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n_rest = (i + 1) * jobs - self.total_epochs
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current_jobs = jobs - n_rest if n_rest > 0 else jobs
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EVALS = ceil(self.total_epochs / jobs)
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for i in range(EVALS):
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# Correct the number of epochs to be processed for the last
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# iteration (should not exceed self.total_epochs in total)
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n_rest = (i + 1) * jobs - self.total_epochs
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current_jobs = jobs - n_rest if n_rest > 0 else jobs
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asked = self.opt.ask(n_points=current_jobs)
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f_val = self.run_optimizer_parallel(parallel, asked, i)
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self.opt.tell(asked, [v['loss'] for v in f_val])
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self.fix_optimizer_models_list()
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asked = self.opt.ask(n_points=current_jobs)
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f_val = self.run_optimizer_parallel(parallel, asked, i)
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self.opt.tell(asked, [v['loss'] for v in f_val])
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self.fix_optimizer_models_list()
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# Calculate progressbar outputs
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for j, val in enumerate(f_val):
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# Use human-friendly indexes here (starting from 1)
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current = i * jobs + j + 1
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val['current_epoch'] = current
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val['is_initial_point'] = current <= INITIAL_POINTS
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# Calculate progressbar outputs
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for j, val in enumerate(f_val):
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# Use human-friendly indexes here (starting from 1)
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current = i * jobs + j + 1
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val['current_epoch'] = current
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val['is_initial_point'] = current <= INITIAL_POINTS
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logger.debug(f"Optimizer epoch evaluated: {val}")
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logger.debug(f"Optimizer epoch evaluated: {val}")
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is_best = self.is_best_loss(val, self.current_best_loss)
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# This value is assigned here and not in the optimization method
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# to keep proper order in the list of results. That's because
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# evaluations can take different time. Here they are aligned in the
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# order they will be shown to the user.
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val['is_best'] = is_best
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output = self.get_results(val)
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if output:
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self.progress_bar.write(output)
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self.progress_bar.ncols = 108
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self.progress_bar.update(1)
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is_best = self.is_best_loss(val, self.current_best_loss)
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# This value is assigned here and not in the optimization method
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# to keep proper order in the list of results. That's because
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# evaluations can take different time. Here they are aligned in the
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# order they will be shown to the user.
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val['is_best'] = is_best
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self.print_results(val)
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if is_best:
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self.current_best_loss = val['loss']
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self.trials.append(val)
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# Save results after each best epoch and every 100 epochs
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if is_best or current % 100 == 0:
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self.save_trials()
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self.progress_bar.ncols = 108
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self.progress_bar.close()
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if is_best:
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self.current_best_loss = val['loss']
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self.trials.append(val)
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# Save results after each best epoch and every 100 epochs
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if is_best or current % 100 == 0:
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self.save_trials()
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pbar.update(current)
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except KeyboardInterrupt:
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self.progress_bar.close()
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print('User interrupted..')
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self.save_trials(final=True)
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@ -743,9 +756,3 @@ class Hyperopt:
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# This is printed when Ctrl+C is pressed quickly, before first epochs have
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# a chance to be evaluated.
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print("No epochs evaluated yet, no best result.")
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def __getstate__(self):
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state = self.__dict__.copy()
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del state['trials']
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del state['progress_bar']
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return state
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@ -7,4 +7,4 @@ scikit-learn==0.22.2.post1
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scikit-optimize==0.7.4
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filelock==3.0.12
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joblib==0.14.1
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tqdm==4.43.0
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progressbar2==3.50.1
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