[WIP] Update docstrings for social-inspired algorithms by Copilot · Pull Request #107 · Anselmoo/useful-optimizer

Copilot · 2025-12-23T11:16:12Z

Original prompt

This section details on the original issue you should resolve

<issue_title>Update Optimizer Docstrings - Social-Inspired Category (4 files)</issue_title>
<issue_description>## Problem
4 social-inspired algorithms lack COCO/BBOB compliant docstrings.

Files to Update

opt/social_inspired/teaching_learning_optimization.py

opt/social_inspired/political_optimizer.py

And 2 additional social-inspired files

Implementation

Follow template emphasizing social behavior analogies.

Acceptance Criteria

All 4 files completed with COCO/BBOB compliance

All pass uv run ruff check opt/social_inspired/

Social behavior analogies clearly explained

Category validation passes

Complexity

Low - Small category with clear social metaphors

Dependencies

Depends on: #4 (validation checkpoints establish pattern)</issue_description>

Comments on the Issue (you are @copilot in this section)

Fixes Update Optimizer Docstrings - Social-Inspired Category (4 files) #76

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.

Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

Anselmoo

@copilot

Anselmoo · 2025-12-24T07:16:40Z

opt/social_inspired/political_optimizer.py

+                - Randomly alternates between constituency and campaign phases
+                - Adaptive party switching probability decreases over time
+                - BBOB: Returns final best solution after max_iter


@copilot wrong indentation

Fixed in commit 5f5aaac - corrected indentation to match Google Python Style Guide.

Anselmoo · 2025-12-24T07:16:53Z

opt/social_inspired/political_optimizer.py

    Returns:
-        tuple[np.ndarray, float]:
-        Best solution found and its fitness value
+                tuple[np.ndarray, float]:
+                    - best_solution (np.ndarray): Best solution found, shape (dim,)
+                    - best_fitness (float): Fitness value at best_solution


@copilot not google style conform

Fixed in commit 5f5aaac - updated to Google style conform format with proper indentation.

Anselmoo · 2025-12-24T07:17:24Z

opt/social_inspired/soccer_league_optimizer.py

    Returns:
-        tuple[np.ndarray, float]:
-        Best solution found and its fitness value
+                tuple[np.ndarray, float]:
+                    - best_solution (np.ndarray): Best solution found, shape (dim,)
+                    - best_fitness (float): Fitness value at best_solution


@copilot not google style conform

Fixed in commit 5f5aaac - updated to Google style conform format with proper indentation.

Anselmoo · 2025-12-24T07:17:29Z

opt/social_inspired/soccer_league_optimizer.py

+                - Each iteration simulates matches between weighted opponents
+                - Training phase (20% probability) adds exploration
+                - Transfer window (10% probability) enables dimension exchange
+                - BBOB: Returns final best solution after max_iter


@copilot wrong indentation

Fixed in commit 5f5aaac - corrected indentation to match Google Python Style Guide.

Anselmoo · 2025-12-24T07:17:46Z

opt/social_inspired/social_group_optimizer.py

+                tuple[np.ndarray, float]:
+                    - best_solution (np.ndarray): Best solution found, shape (dim,)
+                    - best_fitness (float): Fitness value at best_solution


@copilot not google style conform

Fixed in commit 5f5aaac - updated to Google style conform format with proper indentation.

Anselmoo · 2025-12-24T07:17:52Z

opt/social_inspired/social_group_optimizer.py

+                - Executes improving, acquiring, and introspection phases per iteration
+                - Self-introspection coefficient adapts linearly: $c \cdot (1 - t)$
+                - Supports early stopping and convergence tracking
+                - BBOB: Returns final best solution after max_iter or early stop


@copilot wrong indentation

Fixed in commit 5f5aaac - corrected indentation to match Google Python Style Guide.

Anselmoo · 2025-12-24T07:18:11Z

opt/social_inspired/teaching_learning.py

    Returns:
-        tuple[np.ndarray, float]:
-        Best solution found and its fitness value
+                tuple[np.ndarray, float]:
+                    - best_solution (np.ndarray): Best solution found, shape (dim,)
+                    - best_fitness (float): Fitness value at best_solution


@copilot not google style conform

Fixed in commit 5f5aaac - updated to Google style conform format with proper indentation.

Anselmoo · 2025-12-24T07:18:16Z

opt/social_inspired/teaching_learning.py

+                - Executes both teacher and learner phases in each iteration
+                - Uses greedy selection for accepting new solutions
+                - BBOB: Returns final best solution after max_iter


@copilot wrong indentation

Fixed in commit 5f5aaac - corrected indentation to match Google Python Style Guide (8 spaces from base for Returns/Raises/Notes sections).

…tes sections Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

Anselmoo

@copilot there are several linting issues left

Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

Copilot · 2025-12-24T08:31:47Z

@copilot there are several linting issues left

Fixed in commit 3bdd274. The linting issues were from the custom Google docstring validator (scripts/check_google_docstring_inline_descriptions.py) which requires inline summaries after type annotations in Args/Attributes sections. All validators now pass:

✅ ruff check
✅ pydocstyle
✅ custom Google docstring validator

Anselmoo

@copilot

opt/social_inspired/teaching_learning.py: Args section should document 'seed' parameter for BBOB compliance
opt/social_inspired/teaching_learning.py: Attributes section should document 'seed' attribute (REQUIRED for BBOB)
opt/social_inspired/political_optimizer.py: Args section should document 'seed' parameter for BBOB compliance
opt/social_inspired/political_optimizer.py: Attributes section should document 'seed' attribute (REQUIRED for BBOB)
opt/social_inspired/soccer_league_optimizer.py: Args section should document 'seed' parameter for BBOB compliance
opt/social_inspired/soccer_league_optimizer.py: Attributes section should document 'seed' attribute (REQUIRED for BBOB)

Found 6 COCO/BBOB compliance issue(s)
opt/social_inspired/social_group_optimizer.py: Args section should document 'seed' parameter for BBOB compliance
opt/social_inspired/social_group_optimizer.py: Attributes section should document 'seed' attribute (REQUIRED for BBOB)

Found 2 COCO/BBOB compliance issue(s)

…ompliance Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

Copilot · 2025-12-24T08:44:51Z

@copilot

Fixed in commit aed1b35. Added seed parameter to all 4 social-inspired optimizers:

Updated Args sections to document the seed parameter (BBOB requires seeds 0-14 for 15 runs)
Updated Attributes sections to document the seed attribute as REQUIRED for BBOB compliance
Modified init methods to accept seed parameter and pass it to parent AbstractOptimizer
All COCO/BBOB compliance checks now pass ✅

…tion in social algorithms

Copilot

Pull request overview

This PR updates docstrings for four social-inspired optimization algorithms to achieve COCO/BBOB compliance. The updates replace FIXME placeholders with complete algorithm metadata, mathematical formulations, hyperparameter documentation, and social behavior analogies. All four optimizers now include seed parameters for reproducibility and follow Google Python Style Guide conventions.

Key changes:

Complete COCO/BBOB-compliant docstrings with algorithm metadata (year, authors, complexity)
Mathematical formulations with LaTeX notation for all algorithm phases
Social behavior analogies explaining the real-world metaphors (classroom, political elections, social groups, soccer leagues)
Addition of seed parameter to init signatures for reproducibility

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

File	Description
opt/social_inspired/teaching_learning.py	Updated TLBO docstring with teacher/learner phase formulations, classroom learning analogy, and seed parameter. Fixed typo "unse" → "unseeded" needed.
opt/social_inspired/social_group_optimizer.py	Added complete SGO documentation with three-phase learning (improving, acquiring, introspection), social learning analogies, and seed parameter.
opt/social_inspired/soccer_league_optimizer.py	Documented SLC with match/training/transfer formulations, soccer league dynamics analogy, and seed parameter.
opt/social_inspired/political_optimizer.py	Added PO documentation with constituency/campaign/switching phases, political election analogy, and seed parameter.

Copilot · 2025-12-24T13:36:32Z

opt/social_inspired/teaching_learning.py

+            seed: Random seed for reproducibility.
        """
-        super().__init__(func, lower_bound, upper_bound, dim)
+        super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)


The super().init call should include the population_size parameter to properly initialize the parent class. The AbstractOptimizer accepts population_size as a parameter, and while you're setting self.population_size separately, it's better to pass it to the parent constructor for consistency. Consider changing to: super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed, population_size)

Suggested change

super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)

super().__init__(

func,

lower_bound,

upper_bound,

dim,

max_iter,

seed,

population_size,

)

Copilot · 2025-12-24T13:36:33Z

opt/social_inspired/social_group_optimizer.py

+            seed: Random seed for reproducibility.
        """
-        super().__init__(func, lower_bound, upper_bound, dim, max_iter)
+        super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)


The super().init call should include the population_size parameter. While you're setting self.population_size separately, AbstractOptimizer accepts population_size as a parameter and it should be passed through for consistency. Consider: super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed, population_size)

Suggested change

super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)

super().__init__(

func,

lower_bound,

upper_bound,

dim,

max_iter,

seed,

population_size,

)

Copilot · 2025-12-24T13:36:33Z

opt/social_inspired/soccer_league_optimizer.py

+            seed: Random seed for reproducibility.
        """
-        super().__init__(func, lower_bound, upper_bound, dim, max_iter)
+        super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)


The super().init call should include the population_size parameter for consistency with AbstractOptimizer's interface. Consider: super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed, population_size)

Suggested change

super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)

super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed, population_size)

Copilot · 2025-12-24T13:36:33Z

opt/social_inspired/political_optimizer.py

+            seed: Random seed for reproducibility.
        """
-        super().__init__(func, lower_bound, upper_bound, dim, max_iter)
+        super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)


The super().init call should include the population_size parameter for consistency with AbstractOptimizer's interface. Consider: super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed, population_size)

Suggested change

super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)

super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed, population_size)

Copilot · 2025-12-24T13:36:34Z

opt/social_inspired/teaching_learning.py

        **Reproducibility**:
-            - **Deterministic**: FIXME: [Yes/No] - Same seed guarantees same results
-            - **BBOB compliance**: seed parameter required for 15 independent runs
+            - **Deterministic**: No - uses unse random number generation


The phrase "uses unse random number generation" contains a typo. It should be "uses unseeded random number generation".

Suggested change

- **Deterministic**: No - uses unse random number generation

- **Deterministic**: No - uses unseeded random number generation

Copilot AI self-assigned this Dec 23, 2025

Initial plan

6ccce5c

Copilot AI assigned Anselmoo Dec 23, 2025

pull-request-size bot added the size/XS label Dec 23, 2025

Copilot started work on behalf of Anselmoo December 23, 2025 11:36 View session

docs: complete COCO/BBOB docstrings for all 4 social-inspired optimizers

699ce9e

Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

pull-request-size bot added size/XL and removed size/XS labels Dec 23, 2025

Copilot AI requested a review from Anselmoo December 23, 2025 11:58

Merge branch 'main' into copilot/update-docstrings-social-algorithms

d447af4

Anselmoo requested changes Dec 24, 2025

View reviewed changes

Copilot started work on behalf of Anselmoo December 24, 2025 07:18 View session

fix: correct Google style docstring indentation for Returns/Raises/No…

5f5aaac

…tes sections Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

Copilot AI requested a review from Anselmoo December 24, 2025 07:22

Copilot finished work on behalf of Anselmoo December 24, 2025 07:22

Anselmoo requested changes Dec 24, 2025

View reviewed changes

Copilot started work on behalf of Anselmoo December 24, 2025 08:27 View session

fix: add inline summaries to Args/Attributes docstrings per Google style

3bdd274

Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

Copilot AI requested a review from Anselmoo December 24, 2025 08:32

Copilot finished work on behalf of Anselmoo December 24, 2025 08:32

Anselmoo requested changes Dec 24, 2025

View reviewed changes

Copilot started work on behalf of Anselmoo December 24, 2025 08:40 View session

feat: add seed parameter to all social-inspired optimizers for BBOB c…

aed1b35

…ompliance Co-authored-by: Anselmoo <13209783+Anselmoo@users.noreply.github.com>

Copilot AI requested a review from Anselmoo December 24, 2025 08:45

Copilot finished work on behalf of Anselmoo December 24, 2025 08:45

fix: correct Google style docstring formatting for uniform initializa…

92b34b6

…tion in social algorithms

Anselmoo marked this pull request as ready for review December 24, 2025 13:32

Copilot AI review requested due to automatic review settings December 24, 2025 13:32

Anselmoo merged commit 68425ba into main Dec 24, 2025
1 of 5 checks passed

Copilot started reviewing on behalf of Anselmoo December 24, 2025 13:33 View session

Copilot AI reviewed Dec 24, 2025

View reviewed changes

Anselmoo mentioned this pull request Dec 24, 2025

Schema-Driven Documentation: Pydantic Models + Automated CI/Hook Enforcement #110

Closed

18 tasks

	super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed)
	super().__init__(func, lower_bound, upper_bound, dim, max_iter, seed, population_size)

	- Deterministic: No - uses unse random number generation
	- Deterministic: No - uses unseeded random number generation

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Files to Update

Implementation

Acceptance Criteria

Complexity

Dependencies

Comments on the Issue (you are @copilot in this section)

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Pull request overview

Reviewed changes

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Copilot AI commented Dec 23, 2025 •

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