Investigating alternative approaches for idealization - #1
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We will see the immensely enhancement analysis of the making
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Pull request overview
This PR introduces a small Python MVP package (“enhanced_idealization”) for generating and ranking “idealized” candidate strategies based on weighted criteria, constraints, scenarios, and optional user profiles, along with a CLI, example input, and initial unit tests.
Changes:
- Adds the
enhanced_idealizationpackage with request/result models, recommendation engine, and text reporting. - Adds a CLI entry point (
python -m enhanced_idealization …) plus an example JSON request. - Adds a unittest suite for core engine behaviors and updates repository metadata/config (README, pyproject, gitignore).
Reviewed changes
Copilot reviewed 9 out of 10 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
enhanced_idealization/engine.py |
Implements candidate generation, scoring, scenario simulation, confidence, explanations, and history comparison. |
enhanced_idealization/models.py |
Adds dataclass models for requests, candidates, constraints, scenarios, and results. |
enhanced_idealization/reporting.py |
Adds plain-text report rendering for CLI output. |
enhanced_idealization/__main__.py |
Adds argparse-based CLI to load JSON and print the text report. |
enhanced_idealization/__init__.py |
Exposes key public package symbols. |
tests/test_engine.py |
Adds unit tests for ranking, personalization, scenarios, confidence, and tradeoffs behavior. |
examples/product_strategy.json |
Provides a sample request payload for running the CLI. |
README.md |
Documents purpose, layout, how to run the sample, and how to run tests. |
pyproject.toml |
Adds packaging metadata (setuptools build, project info). |
.gitignore |
Adds Python bytecode/cache ignores. |
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| if profile_name is None: | ||
| return request.profiles[0] if request.profiles else None | ||
| for profile in request.profiles: | ||
| if profile.name == profile_name: | ||
| return profile | ||
| raise ValueError(f"Unknown profile: {profile_name}") |
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| deduplicated: dict[str, Candidate] = {} | ||
| for candidate in candidates: | ||
| deduplicated[candidate.name] = candidate | ||
| return list(deduplicated.values()) |
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| def test_tradeoff_builder_handles_sparse_metrics(self) -> None: | ||
| self.assertEqual( | ||
| self.engine._build_tradeoffs({}), | ||
| [ | ||
| "Strength data is not available yet.", | ||
| "Tradeoff data is not available yet.", | ||
| ], | ||
| ) | ||
| self.assertEqual( | ||
| self.engine._build_tradeoffs({"feasibility": 0.8}), | ||
| [ | ||
| "Strength currently centers on feasibility.", | ||
| "Tradeoff analysis needs additional metrics beyond feasibility.", | ||
| ], | ||
| ) |
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| def render_text_report(result: IdealizationResult) -> str: | ||
| lines = [ | ||
| f"Enhanced Idealization Report: {result.request_title}", | ||
| f"Applied profile: {result.applied_profile or 'none'}", | ||
| "Adaptive criteria weights:", |
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