feat: add greedy lookahead algorithm with rolling horizon for hyperpixel building#9
feat: add greedy lookahead algorithm with rolling horizon for hyperpixel building#9dhalmazna wants to merge 6 commits intofeat/algorithm-utilsfrom
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request establishes a foundational greedy lookahead algorithm for hyperpixel construction, employing a rolling horizon approach. This new algorithm provides a crucial baseline for evaluating more complex Monte Carlo search methods that will be integrated in the future, ensuring a structured progression in the development of hyperpixel building strategies. Highlights
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Code Review
This pull request introduces a greedy lookahead algorithm, which is a solid addition as a baseline for hyperpixel construction. The implementation is well-structured, with a clear separation between the main building loop and the candidate generation logic. The use of a rolling horizon strategy is correctly implemented. My main suggestion is to use TypedDict for the return type to improve type safety and code clarity, which is a lightweight improvement over the current dict[str, object] while you plan for a future refactor to a dataclass.
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Pull request overview
This PR adds a greedy lookahead algorithm for building hyperpixels using a rolling horizon strategy (BFS lookahead + commit-one-step), along with a HyperpixelResult TypedDict to formalize the return contract of hyperpixel builders.
Changes:
- New
ciao/algorithm/lookahead.pyimplementing BFS-based greedy lookahead with rolling horizon for hyperpixel construction. - New
HyperpixelResultTypedDict inciao/scoring/hyperpixel.pyand migration ofselect_top_hyperpixelsto use it (including renaming the sort key fromhyperpixel_scoretoscore). - README and
__init__.pyupdates to reflect the new module.
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
ciao/algorithm/lookahead.py |
New greedy lookahead builder using BFS candidate generation and rolling horizon commit strategy |
ciao/scoring/hyperpixel.py |
Added HyperpixelResult TypedDict; updated select_top_hyperpixels to use it |
ciao/algorithm/__init__.py |
Re-exports the new build_hyperpixel_greedy_lookahead function |
README.md |
Updated algorithm description and file tree to reflect the renamed module |
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| def _generate_lookahead_candidates( | ||
| current_mask: int, | ||
| adj_masks: tuple[int, ...], | ||
| used_mask: int, | ||
| lookahead_distance: int, | ||
| desired_length: int, | ||
| ) -> dict[int, int]: | ||
| """Generate all connected supersets up to lookahead_distance steps via BFS. | ||
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| Returns: | ||
| Dict mapping candidate_mask -> first_step_segment_id | ||
| """ | ||
| candidates: dict[int, int] = {} # mask -> first_step | ||
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| # Queue stores tuples of: (current_mask, first_step_that_led_here, current_depth) | ||
| queue: deque[tuple[int, int | None, int]] = deque([(current_mask, None, 0)]) | ||
| visited = {current_mask} | ||
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| while queue: | ||
| mask, first_step, depth = queue.popleft() | ||
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| # Store valid candidates (depth > 0) | ||
| if depth > 0 and first_step is not None and mask not in candidates: | ||
| # Only add if not already seen (shortest path wins) | ||
| candidates[mask] = first_step | ||
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| # Stop expanding if we reached the lookahead limit or maximum size | ||
| if depth >= lookahead_distance or mask.bit_count() >= desired_length: | ||
| continue | ||
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| frontier = get_frontier(mask, adj_masks, used_mask) | ||
| for seg_id in iter_bits(frontier): | ||
| new_mask = add_node(mask, seg_id) | ||
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| if new_mask not in visited: | ||
| visited.add(new_mask) | ||
| # If at the first layer (depth 0), this seg_id is our first_step. | ||
| # Otherwise, pass along the first_step inherited from the parent. | ||
| next_first_step = seg_id if depth == 0 else first_step | ||
| queue.append((new_mask, next_first_step, depth + 1)) | ||
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| return candidates |
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That's the user's problem if he sets the lookahead_distance too large :D
Context:
This PR introduces the
lookahead.pymodule, which implements a greedy search algorithm with a rolling horizon strategy for constructing hyperpixels. This serves as a baseline algorithm before we introduce full Monte Carlo search methods.What's Changed / Added:
ciao/algorithm/lookahead.py: Added the implementation of the greedy lookahead builder. It uses bitwise operations and BFS to explore paths.ciao/scoring/hyperpixel.py: Introduced theHyperpixelResultusingTypedDictto establish a clear, strict contract for what a built hyperpixel returns.How it works:
lookahead_distancesteps away.