Switch to TQDM

freqtrade/commands/hyperopt_commands.py

@@ -52,8 +52,8 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
 
     if not export_csv:
         try:
-            Hyperopt.print_result_table(config, trials, total_epochs,
+            print(Hyperopt.get_result_table(config, trials, total_epochs,
-                                        not filteroptions['only_best'], print_colorized, 0)
+                                            not filteroptions['only_best'], print_colorized, 0))
         except KeyboardInterrupt:
             print('User interrupted..')
 

freqtrade/optimize/hyperopt.py

@@ -21,7 +21,7 @@ from colorama import init as colorama_init
 from joblib import (Parallel, cpu_count, delayed, dump, load,
                     wrap_non_picklable_objects)
 from pandas import DataFrame, json_normalize, isna
-import progressbar
+from tqdm import tqdm
 import tabulate
 from os import path, popen
 import io
@@ -275,11 +275,37 @@ class Hyperopt:
             if not self.print_all:
                 # Separate the results explanation string from dots
                 print("\n")
-            self.print_result_table(self.config, results, self.total_epochs,
+            print(self.get_result_table(
-                                    self.print_all, self.print_colorized,
+                    self.config, results, self.total_epochs,
-                                    self.hyperopt_table_header)
+                    self.print_all, self.print_colorized,
+                    self.hyperopt_table_header
+                )
+            )
             self.hyperopt_table_header = 2
 
+    def get_results(self, results) -> str:
+        """
+        Log results if it is better than any previous evaluation
+        """
+        output = ''
+        is_best = results['is_best']
+        # if not self.print_all:
+        #    Print '\n' after each 100th epoch to separate dots from the log messages.
+        #    Otherwise output is messy on a terminal.
+        #    return '.', end='' if results['current_epoch'] % 100 != 0 else None  # type: ignore
+
+        if self.print_all or is_best:
+            # if not self.print_all:
+            #    Separate the results explanation string from dots
+            #    print("\n")
+            output = self.get_result_table(
+                self.config, results, self.total_epochs,
+                self.print_all, self.print_colorized,
+                self.hyperopt_table_header
+            )
+            self.hyperopt_table_header = 2
+        return output
+
     @staticmethod
     def print_results_explanation(results, total_epochs, highlight_best: bool,
                                   print_colorized: bool) -> None:
@@ -303,13 +329,13 @@ class Hyperopt:
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
+    def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
-                           print_colorized: bool, remove_header: int) -> None:
+                         print_colorized: bool, remove_header: int) -> str:
         """
         Log result table
         """
         if not results:
-            return
+            return ''
 
         tabulate.PRESERVE_WHITESPACE = True
 
@@ -380,7 +406,7 @@ class Hyperopt:
                 trials.to_dict(orient='list'), tablefmt='psql',
                 headers='keys', stralign="right"
             )
-        print(table)
+        return table
 
     @staticmethod
     def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
@@ -661,6 +687,7 @@ class Hyperopt:
 
         try:
             with Parallel(n_jobs=config_jobs) as parallel:
+                """
                 self.progress_bar = progressbar.ProgressBar(
                     min_value=0,
                     max_value=self.total_epochs,
@@ -668,9 +695,17 @@ class Hyperopt:
                     line_breaks=True,
                     enable_colors=self.print_colorized
                 )
-                self.progress_bar.start()
+                """
                 jobs = parallel._effective_n_jobs()
                 logger.info(f'Effective number of parallel workers used: {jobs}')
+
+                # Define progressbar
+                self.progress_bar = tqdm(
+                    total=self.total_epochs, ncols=108, unit=' Epoch',
+                    bar_format='Epoch {n_fmt}/{total_fmt} ({percentage:3.0f}%)|{bar}|'
+                    ' [{elapsed}<{remaining} {rate_fmt}{postfix}]'
+                )
+
                 EVALS = ceil(self.total_epochs / jobs)
                 for i in range(EVALS):
                     # Correct the number of epochs to be processed for the last
@@ -684,8 +719,7 @@ class Hyperopt:
                     self.fix_optimizer_models_list()
 
                     # Calculate progressbar outputs
-                    pbar_line = ceil(self._get_height() / 2)
+                    # pbar_line = ceil(self._get_height() / 2)
-
                     for j, val in enumerate(f_val):
                         # Use human-friendly indexes here (starting from 1)
                         current = i * jobs + j + 1
@@ -699,20 +733,25 @@ class Hyperopt:
                         # evaluations can take different time. Here they are aligned in the
                         # order they will be shown to the user.
                         val['is_best'] = is_best
-
+                        # print(current)
-                        self.print_results(val)
+                        output = self.get_results(val)
-
+                        self.progress_bar.write(output)
+                        # self.progress_bar.write(str(len(output.split('\n')[0])))
+                        self.progress_bar.ncols = len(output.split('\n')[0])
+                        self.progress_bar.update(1)
+                        """
                         if pbar_line <= current:
                             self.progress_bar.update(current)
                             pbar_line = current + ceil(self._get_height() / 2)
-
+                        """
                         if is_best:
                             self.current_best_loss = val['loss']
                         self.trials.append(val)
                         # Save results after each best epoch and every 100 epochs
                         if is_best or current % 100 == 0:
                             self.save_trials()
-                self.progress_bar.finish()
+                self.progress_bar.ncols = 108
+                self.progress_bar.close()
 
                 # self.progress_bar.update(current)
         except KeyboardInterrupt: