# Refactor a Legacy scikit-learn Module with Atlas in 2026

> Atlas enables scikit-learn developers to safely refactor legacy modules by mapping public surfaces, pinning behavior with `pytest`, and applying changes with verified patches.

To restructure an old scikit-learn module without altering its behavior or breaking existing callers, Atlas leverages its `lsp` tool to map the public API, `bash` to run `pytest` for behavior pinning, and `apply_patch` for controlled code modifications. This workflow ensures that even in 2026, complex refactoring tasks in scikit-learn are safe and verifiable, integrating directly with your `uv` and `ruff format` toolchain.

## Key takeaways

- Atlas uses `lsp` to map all scikit-learn module callers, preventing silent breakage.
- Behavior is pinned by running `pytest` via `bash` before any scikit-learn code changes.
- Structural changes are applied safely with `apply_patch`, which verifies file context.
- Atlas tracks remaining scikit-learn callsites with `todowrite` for clear progress.
- Every Atlas edit to scikit-learn code is presented as a unified diff for explicit approval.
- Atlas integrates with `ruff format` and `uv` for consistent scikit-learn code quality.

## How Atlas Maps a scikit-learn Module's Public API

Atlas begins a scikit-learn module refactor by comprehensively mapping its public surface, ensuring no callsite is overlooked. This critical first step, performed in 2026, uses the `lsp` tool's `documentSymbol` and `findReferences` operations to enumerate every exported symbol and its 100% of its callers.

Before any code changes, Atlas uses its `lsp` tool to interact with the Language Server Protocol, specifically invoking the `documentSymbol` operation on the target scikit-learn module. This identifies all public functions, classes, and variables. For each identified symbol, Atlas then executes `findReferences` to locate every single callsite across the entire codebase. This exhaustive enumeration is crucial for understanding the module's dependencies and ensuring that the refactor does not introduce silent breakage. The results are often tracked in a `todowrite` list, allowing developers to systematically address each callsite as the refactoring progresses, preventing a partially migrated module from being mistaken for a finished one. This process is permission-gated, requiring explicit approval before Atlas queries the LSP server.

## Pinning scikit-learn Module Behavior with pytest

Pinning the existing behavior of a scikit-learn module is the second critical step, ensuring that any refactoring does not introduce regressions. Atlas achieves this by running the module's `pytest` suite via the `bash` tool, establishing a green baseline of 0 failures before any structural changes are made.

Once the public surface and its callers are mapped, Atlas uses the `bash` tool to execute the project's `pytest` suite. For a scikit-learn project, this typically involves running `pytest path/to/your/module_tests.py` or simply `pytest` from the project root, assuming a `pyproject.toml` is configured. The goal is to record a 'green baseline' where all existing tests pass. This baseline serves as the immutable contract for the module's behavior. Atlas will re-run these tests after each significant change, ensuring that the refactoring maintains the original functionality. This step is permission-gated, requiring user approval before Atlas executes any shell commands, providing a crucial safety net for your development environment.

## Applying Structural Changes to scikit-learn Code Safely

Atlas applies structural changes to scikit-learn modules using its `apply_patch` tool, which anchors on context lines and refuses to apply against a drifted file. This ensures that each modification, whether moving a `Pipeline` component or refactoring an estimator's `fit` method, is precise and safe, with 100% context verification.

Refactoring a scikit-learn module often involves moving code, renaming files, or restructuring classes like `ColumnTransformer` definitions. Atlas performs these structural changes using its `apply_patch` tool. This tool is designed for safety: it seeks each hunk's context and old_lines. If the file has drifted since the patch was generated, `apply_patch` will fail with a 'Failed to find context' error, preventing unintended modifications to an outdated codebase. After each successful hunk lands, Atlas immediately re-runs the `pytest` suite using `bash` to verify that the module's behavior remains unchanged. This iterative approach, rather than a single test run at the end, drastically reduces the risk of introducing subtle bugs in complex scikit-learn estimators or utility functions.

## Tracking Refactoring Progress in scikit-learn

Tracking the progress of a scikit-learn module refactor is essential to prevent partially migrated code from being deployed. Atlas uses its `todowrite` tool to manage remaining callsites and integrates with `git` to snapshot changes, ensuring a clear path to completion in 2026.

As Atlas refactors a scikit-learn module, it maintains a `todowrite` list of remaining callsites that still need migration. This list, initially populated by the `lsp` tool's `findReferences` output, ensures that no caller is forgotten. Atlas also reads `git` branches, status, and diffs, and can stage and create commits on your behalf. Every file edit is snapshotted as `git` patches, allowing edits to be diffed, reviewed, and rolled back if necessary. This robust tracking mechanism ensures that a partially refactored scikit-learn module, perhaps one where a `predict` method has been moved but not all its callers updated, cannot be mistaken for a finished one, providing clear visibility into the project's status.

## Ensuring scikit-learn Code Quality and Review

Atlas integrates direct with scikit-learn's standard code quality tools, such as `ruff format`, to ensure refactored code adheres to project standards. Every proposed change is presented as a unified diff for approval, offering 100% transparency and control over the final output.

After Atlas applies structural changes and verifies behavior, it can apply code formatting using `ruff format` via the `bash` tool. This ensures that the refactored scikit-learn code adheres to the project's style guidelines, just as if a human developer had made the changes. Before any file is written to disk, Atlas computes a unified diff for every proposed edit and surfaces it for explicit approval. This allows developers to review the exact changes, including those from `ruff format`, before they are committed. All Atlas tool calls, including `bash` commands for `pytest` or `ruff format`, are permission-gated against allow, ask, and deny rules, providing granular control and a secure workflow for maintaining high-quality scikit-learn code.

## Steps

1. Run Atlas in your scikit-learn project with a `pyproject.toml` configured for `uv` and `pytest`.
2. Ask Atlas to map the public surface of your legacy scikit-learn module using the `lsp` tool's `documentSymbol` operation.
3. Instruct Atlas to find all references to each public symbol with `lsp findReferences` and track them in a `todowrite` list.
4. Use Atlas's `bash` tool to run `pytest` for the module, recording a green baseline of passing tests.
5. Guide Atlas to restructure the module using `apply_patch`, for example, moving a `ColumnTransformer` definition.
6. After each `apply_patch` hunk, have Atlas re-run `pytest` via `bash` to verify behavior is unchanged.
7. Once refactoring is complete, ask Atlas to apply `ruff format` to the modified files using `bash`.
8. Review the unified diffs presented by Atlas and approve the changes before they are written and committed.

## FAQ

### How does Atlas ensure a scikit-learn refactor doesn't break existing code?

Atlas ensures a scikit-learn refactor doesn't break existing code by first mapping all public API calls with `lsp findReferences`. It then pins the module's behavior by running `pytest` via `bash` to establish a green baseline. Changes are applied incrementally with `apply_patch`, and `pytest` is re-run after each modification to verify behavior.

### Can Atlas refactor scikit-learn Pipelines or ColumnTransformers?

Yes, Atlas can refactor scikit-learn `Pipelines` and `ColumnTransformers`. It can move components within a `Pipeline`, restructure `ColumnTransformer` definitions, or even move a scaler that was fit on the full dataset inside a `Pipeline` to fix classic leakage bugs, all while verifying behavior with `pytest`.

### What scikit-learn specific tools does Atlas integrate with?

Atlas integrates directly with scikit-learn's standard toolchain. It uses `pytest` for testing, `uv` for package management, and `ruff format` for code formatting. Atlas executes these tools via its `bash` capability, always behind a permission prompt for safety.

### How does Atlas handle code formatting during a scikit-learn refactor?

Atlas handles code formatting by integrating with `ruff format`. After applying structural changes and verifying behavior, Atlas can execute `ruff format` via its `bash` tool. The resulting formatted changes are then presented as part of the unified diff for your review and approval before being written to disk.

### Is it safe to let Atlas modify my scikit-learn codebase?

Yes, it is safe. Atlas operates with multiple layers of safety. Every tool call is permission-gated, requiring your explicit approval. It drafts a plan in a read-only agent first, and every file edit generates a unified diff for your approval before writing. Atlas also snapshots changes as `git` patches for easy rollback.

### How does Atlas help with custom scikit-learn estimators?

Atlas can help custom scikit-learn estimators pass `check_estimator` by guiding the implementation of `get_params` and `set_params` correctly. It can read your estimator definitions and suggest or apply the necessary changes to adhere to the estimator API contract, verifying with `pytest`.

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