skip progressbar if needed

[ziofil]

User description

Context:
when launching jobs on the command line, using settings.PROGRESSBAR = False is not enough because it's just invisible, but some output lines are still skipped.

Description of the Change:
Entirely skips creating the ProgressBar object

Benefits:
Slightly faster optimizations with no progress bar, cleaner console output

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PR Type

enhancement, bug_fix

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Description

• Added a condition to skip the creation of the ProgressBar object if settings.PROGRESSBAR is set to False, improving console output and optimization speed.
• Refactored the optimization process by introducing a new _optimization_loop method that handles the optimization steps with an optional progress bar.
• Updated imports to include settings for accessing the PROGRESSBAR setting.

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Changes walkthrough 📝

Relevant files

Enhancement

optimizer.py Conditional Progress Bar Creation in Optimizer                      mrmustard/training/optimizer.py Added conditional logic to skip progress bar creation if settings.PROGRESSBAR is False. Introduced _optimization_loop method to handle optimization with optional progress bar. Updated imports to include settings. Improved console output and optimization speed by avoiding unnecessary progress bar creation. +45/-22

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PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🏅 Score: 92

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Code Duplication The new _optimization_loop method contains code that is very similar to the original implementation. Consider further refactoring to reduce duplication. Error Handling The new implementation doesn't include explicit error handling for the optimization loop. Consider adding try-except blocks to handle potential exceptions.

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PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Score
Enhancement	Improve code structure and resource management by using a context manager consistently Consider using a context manager for the progress bar to ensure proper resource management, even when settings.PROGRESSBAR is False. This can be achieved by creating a dummy context manager when the progress bar is not needed. mrmustard/training/optimizer.py [101-106] -bar = ProgressBar(max_steps) -if settings.PROGRESSBAR: - with bar: - self._optimization_loop(cost_fn, trainable_params, max_steps, callbacks, bar) -else: - self._optimization_loop(cost_fn, trainable_params, max_steps, callbacks) +from contextlib import nullcontext +bar = ProgressBar(max_steps) if settings.PROGRESSBAR else None +with bar or nullcontext(): + self._optimization_loop(cost_fn, trainable_params, max_steps, callbacks, bar) + Apply this suggestion Suggestion importance[1-10]: 8 Why: This suggestion enhances resource management by using a consistent context manager pattern, even when the progress bar is not needed. It improves code readability and ensures that resources are properly managed, which is a significant improvement.	8
	Simplify method signature and improve code readability by using a default argument Consider using a default argument for the progress_bar parameter in the _optimization_loop method to simplify the method call and improve code readability. mrmustard/training/optimizer.py [108-110] def _optimization_loop( - self, cost_fn, trainable_params, max_steps, callbacks, progress_bar=None + self, cost_fn, trainable_params, max_steps, callbacks, progress_bar=ProgressBar(0) ): Apply this suggestion Suggestion importance[1-10]: 4 Why: While using a default argument can simplify the method call, setting a default ProgressBar with 0 steps may not be meaningful and could lead to confusion. The suggestion offers a minor improvement in readability but lacks practical utility.	4
Best practice	Reduce code duplication and improve maintainability by using partial function application Consider using the functools.partial function to create a modified version of _optimization_loop with preset arguments, reducing code duplication in the _minimize method. mrmustard/training/optimizer.py [102-106] +from functools import partial + +optimization_func = partial(self._optimization_loop, cost_fn, trainable_params, max_steps, callbacks) if settings.PROGRESSBAR: with bar: - self._optimization_loop(cost_fn, trainable_params, max_steps, callbacks, bar) + optimization_func(bar) else: - self._optimization_loop(cost_fn, trainable_params, max_steps, callbacks) + optimization_func() Apply this suggestion Suggestion importance[1-10]: 7 Why: Using functools.partial reduces code duplication and enhances maintainability by centralizing the function call setup. This is a good practice that simplifies the code structure and reduces potential errors.	7
Maintainability	Improve code organization by encapsulating related functionality within a single method Consider moving the cost_fn_modified flag and related logic into the _optimization_loop method to improve encapsulation and reduce the complexity of the _minimize method. mrmustard/training/optimizer.py [98-102] def _minimize(self, cost_fn, by_optimizing, max_steps, callbacks): # finding out which parameters are trainable from the ops trainable_params = self._get_trainable_params(by_optimizing) - cost_fn_modified = False - orig_cost_fn = cost_fn + self._optimization_loop(cost_fn, trainable_params, max_steps, callbacks) Apply this suggestion Suggestion importance[1-10]: 6 Why: Moving the cost_fn_modified logic into _optimization_loop improves encapsulation and reduces complexity in _minimize. This enhances code organization and maintainability, though it requires careful consideration of method responsibilities.	6

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Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 89.77%. Comparing base (3a1d8bb) to head (b1e7e95).

Additional details and impacted files

@@           Coverage Diff            @@
##           develop     #516   +/-   ##
========================================
  Coverage    89.77%   89.77%           
========================================
  Files           92       92           
  Lines         6072     6072           
========================================
  Hits          5451     5451           
  Misses         621      621           

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[apchytr]

Thanks Filippo!