Switch to TQDM
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Fredrik81
parent
3a8b68c0fd
commit
81cbb92556
@ -52,8 +52,8 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
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if not export_csv:
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try:
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Hyperopt.print_result_table(config, trials, total_epochs,
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not filteroptions['only_best'], print_colorized, 0)
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print(Hyperopt.get_result_table(config, trials, total_epochs,
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not filteroptions['only_best'], print_colorized, 0))
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except KeyboardInterrupt:
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print('User interrupted..')
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@ -21,7 +21,7 @@ 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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import progressbar
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from tqdm import tqdm
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import tabulate
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from os import path, popen
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import io
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@ -275,11 +275,37 @@ class Hyperopt:
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if not self.print_all:
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# Separate the results explanation string from dots
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print("\n")
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self.print_result_table(self.config, results, self.total_epochs,
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print(self.get_result_table(
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self.config, results, self.total_epochs,
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self.print_all, self.print_colorized,
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self.hyperopt_table_header)
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self.hyperopt_table_header
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)
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)
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self.hyperopt_table_header = 2
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def get_results(self, results) -> str:
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"""
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Log results if it is better than any previous evaluation
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"""
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output = ''
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is_best = results['is_best']
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# if not self.print_all:
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# Print '\n' after each 100th epoch to separate dots from the log messages.
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# Otherwise output is messy on a terminal.
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# return '.', end='' if results['current_epoch'] % 100 != 0 else None # type: ignore
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if self.print_all or is_best:
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# if not self.print_all:
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# Separate the results explanation string from dots
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# print("\n")
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output = self.get_result_table(
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self.config, results, self.total_epochs,
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self.print_all, self.print_colorized,
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self.hyperopt_table_header
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)
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self.hyperopt_table_header = 2
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return output
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@staticmethod
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def print_results_explanation(results, total_epochs, highlight_best: bool,
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print_colorized: bool) -> None:
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@ -303,13 +329,13 @@ class Hyperopt:
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f"Objective: {results['loss']:.5f}")
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@staticmethod
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def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
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print_colorized: bool, remove_header: int) -> None:
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def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
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print_colorized: bool, remove_header: int) -> str:
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"""
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Log result table
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"""
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if not results:
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return
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return ''
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tabulate.PRESERVE_WHITESPACE = True
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@ -380,7 +406,7 @@ class Hyperopt:
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trials.to_dict(orient='list'), tablefmt='psql',
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headers='keys', stralign="right"
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)
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print(table)
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return table
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@staticmethod
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def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
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@ -661,6 +687,7 @@ class Hyperopt:
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try:
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with Parallel(n_jobs=config_jobs) as parallel:
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"""
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self.progress_bar = progressbar.ProgressBar(
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min_value=0,
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max_value=self.total_epochs,
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@ -668,9 +695,17 @@ class Hyperopt:
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line_breaks=True,
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enable_colors=self.print_colorized
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)
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self.progress_bar.start()
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"""
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jobs = parallel._effective_n_jobs()
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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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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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@ -684,8 +719,7 @@ class Hyperopt:
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self.fix_optimizer_models_list()
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# Calculate progressbar outputs
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pbar_line = ceil(self._get_height() / 2)
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# pbar_line = ceil(self._get_height() / 2)
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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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@ -699,20 +733,25 @@ class Hyperopt:
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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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# print(current)
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output = self.get_results(val)
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self.progress_bar.write(output)
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# self.progress_bar.write(str(len(output.split('\n')[0])))
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self.progress_bar.ncols = len(output.split('\n')[0])
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self.progress_bar.update(1)
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"""
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if pbar_line <= current:
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self.progress_bar.update(current)
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pbar_line = current + ceil(self._get_height() / 2)
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"""
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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.finish()
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self.progress_bar.ncols = 108
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self.progress_bar.close()
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# self.progress_bar.update(current)
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except KeyboardInterrupt:
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