How does Atlas handle renaming a scikit-learn class used in a `Pipeline`?

Atlas uses the `lsp` tool to accurately identify all references to your scikit-learn class, including its instantiation within `Pipeline` steps or `ColumnTransformer` definitions. This ensures the rename is applied correctly across all code contexts.

Can Atlas rename a constant defined in `sklearn/utils/_param_validation.py`?

Yes, Atlas can rename constants. It will use `lsp` to find code references and `grep` to catch any string literals or documentation mentions of the constant, ensuring a complete update across your scikit-learn project.

What if Atlas finds multiple matches for a single intended rename in a scikit-learn file?

The `edit` tool in Atlas is designed to prevent silent corruption. If it finds multiple matches for a single intended replacement, it will throw an error, prompting you to provide more context or explicitly opt into `replaceAll` for that specific file.

How does Atlas ensure my scikit-learn tests still pass after a rename?

After applying renames, Atlas uses its `bash` tool to run your project's test runner, `pytest`. It can execute `pytest sklearn/` behind a permission prompt, allowing you to verify that all tests pass and the refactoring hasn't introduced regressions.

Does Atlas integrate with scikit-learn's `ruff format` for code style?

Yes, Atlas integrates with `ruff format`. After making changes, you can instruct Atlas via its `bash` tool to run `ruff format .` on the modified files, ensuring your scikit-learn codebase remains compliant with its formatting standards.

Can I review the changes Atlas proposes before they are applied to my scikit-learn project?

Absolutely. Atlas computes a unified diff for every file edit and surfaces it for your approval before writing. It also drafts a plan in a read-only agent first, giving you full control and review capabilities over proposed changes to your scikit-learn codebase.

How does Atlas handle renaming a symbol that appears in scikit-learn documentation?

Atlas uses its `grep` tool to find occurrences of the old symbol name in documentation files (e.g., `.rst` files in `docs/`), comments, and string literals, in addition to code references found by `lsp`. This ensures the rename is comprehensive, covering both code and prose.

Rename a symbol across the repo in scikit-learn with Atlas in 2026

To rename a function, class, or constant across your scikit-learn repository in 2026, Atlas provides a robust workflow that combines language server protocol (LSP) accuracy with keyword search and precise file edits. This ensures every usage, from `Pipeline` definitions to `ColumnTransformer` implementations, is updated correctly, followed by validation with `pytest` and formatting with `ruff format`.

How does Atlas rename a scikit-learn symbol across the entire codebase?

Atlas renames a scikit-learn symbol across the entire codebase by orchestrating a 3-step process: first, it uses the `lsp` tool to get authoritative references, then `grep` for non-code occurrences, and finally, the `edit` tool for mechanical changes. This ensures 100% accuracy, even for complex `Pipeline` or `ColumnTransformer` structures.

Atlas leverages its `lsp` tool to query the language server for all true references to the target symbol. This is crucial in scikit-learn, where symbols might be used in various contexts, including `fit`, `transform`, and `predict` methods, or within `Pipeline` steps defined in `my_pipeline.py`. Unlike naive text replacement, `lsp` understands the code's structure, preventing incorrect matches in unrelated contexts. After gathering these precise references, Atlas uses its `grep` tool to find instances of the old symbol name in comments, docstrings, configuration files like `pyproject.toml`, or even example scripts in `examples/` that the language server might not index as code references. This two-pronged approach ensures a complete rename. The mechanical application of these changes is handled by the `edit` tool, which performs `replaceAll` operations. Atlas's `edit` tool is designed to prevent ambiguous single replacements, throwing an error if multiple matches are found for a single intended change, thereby safeguarding against silent corruption in your scikit-learn project.

What Atlas tools are used for scikit-learn symbol renaming?

For renaming a scikit-learn symbol, Atlas primarily utilizes four core tools: `lsp` for code references, `grep` for text occurrences, `edit` for applying changes, and `bash` for validation. This integrated approach ensures a comprehensive and safe refactoring process, typically completing within 1-2 minutes for a medium-sized project.

The `lsp` tool is fundamental for obtaining an authoritative list of references from the language server. In a scikit-learn project, this means accurately identifying all usages of a class like `MyCustomEstimator` or a function within `sklearn.ensemble._forest.py`. This prevents renaming a local variable that coincidentally shares a name with the target symbol. Following this, the `grep` tool is employed to catch any remaining instances of the old name that fall outside the type system's view. This includes comments in `doc/modules/classes.rst`, string literals used in error messages, or even references in `README.md`. The `edit` tool then performs the actual modifications. It uses `replaceAll` for the mechanical part, but critically, it refuses ambiguous single replacements. If you intend to change only one instance of a string, but `edit` finds multiple, it will prompt you, preventing unintended widespread changes. Finally, the `bash` tool is used to execute commands like `pytest` for testing and `ruff format` for formatting, ensuring the codebase remains functional and adheres to style guidelines after the rename.

How does Atlas ensure safety and review during scikit-learn refactoring?

Atlas ensures safety and provides robust review mechanisms during scikit-learn refactoring through several permission-gated steps and diffing capabilities. Every tool call is checked against allow, ask, or deny rules, and all file edits are presented as a unified diff for approval, preventing unintended changes in your 2026 codebase.

Before any tool call, Atlas consults its permission rules, which can be configured to `allow`, `ask`, or `deny` specific operations. This means that running `pytest` or applying `ruff format` will require explicit confirmation if configured to `ask`, giving the scikit-learn developer full control. Atlas drafts a plan in a read-only plan agent first, asking for approval before switching to a build agent that can make changes. This allows you to review the proposed strategy for renaming a symbol like `_fit_transform_one` in `sklearn.base.py` before any modifications occur. Crucially, Atlas computes a unified diff for every file edit it proposes. This diff is surfaced for your approval before any changes are written to disk. You can review exactly what will change in files like `sklearn/pipeline.py` or `sklearn/preprocessing/_data.py`. Atlas also snapshots file changes as git patches, allowing edits to be easily diffed and rolled back if necessary, providing an additional layer of safety for your scikit-learn project.

What are the final validation steps after renaming a scikit-learn symbol?

After renaming a scikit-learn symbol, the final validation steps involve compiling and testing the codebase, followed by a conclusive `grep` for the old name. This rigorous process ensures that no instances of the old symbol remain and that the entire project, including complex `ColumnTransformer` setups, functions correctly, typically taking less than 5 minutes.

Once Atlas has applied all the mechanical renames using the `edit` tool, the critical next step is to validate the changes. This is done by executing the scikit-learn project's standard build and test commands via the `bash` tool. You would typically run `uv install --system` to ensure all dependencies are correctly installed, then execute `pytest sklearn/` to run the relevant test suite. Atlas can run `pytest` behind a permission prompt, giving you control over its execution. If any tests fail, Atlas can help diagnose the issue. After successful testing, it is good practice to apply the project's formatter by running `ruff format .` to ensure the newly modified files adhere to the scikit-learn style guide. Finally, a conclusive `grep -r "OldSymbolName" .` is performed across the entire repository. This final `grep` acts as a definitive check, proving that zero remaining hits for the old symbol name exist anywhere in the codebase, including comments, docstrings, and string literals, ensuring a clean and complete refactoring.

Step by step

01Initiate Atlas in your scikit-learn project: Ensure you are in a directory with a `pyproject.toml` that pins scikit-learn, then launch Atlas.
02Identify the symbol for renaming: Ask Atlas to identify the function, class, or constant you wish to rename, for example, `MyOldEstimator` in `sklearn/my_module.py`.
03Gather authoritative references with `lsp`: Instruct Atlas to run the `lsp` tool's `findReferences` operation on `MyOldEstimator` to get all true code usages from the language server.
04Search for non-code occurrences with `grep`: Have Atlas execute `grep` for `MyOldEstimator` to find instances in comments, docstrings, or configuration files like `docs/user_guide.rst`.
05Apply mechanical renames with `edit`: Direct Atlas to use the `edit` tool with `replaceAll` to apply the new name, `MyNewEstimator`, across all identified locations, reviewing the unified diff for each file.
06Compile and test the scikit-learn codebase: Use Atlas's `bash` tool to run `uv install --system` and then `pytest sklearn/` to ensure all tests pass after the rename.
07Format the modified files: Ask Atlas to apply `ruff format .` to the changed files to maintain scikit-learn's code style.
08Verify zero remaining hits with `grep`: Perform a final `grep -r "MyOldEstimator" .` via Atlas's `bash` tool to confirm no instances of the old symbol remain.

Frequently asked questions

How does Atlas handle renaming a scikit-learn class used in a `Pipeline`?: Atlas uses the `lsp` tool to accurately identify all references to your scikit-learn class, including its instantiation within `Pipeline` steps or `ColumnTransformer` definitions. This ensures the rename is applied correctly across all code contexts.
Can Atlas rename a constant defined in `sklearn/utils/_param_validation.py`?: Yes, Atlas can rename constants. It will use `lsp` to find code references and `grep` to catch any string literals or documentation mentions of the constant, ensuring a complete update across your scikit-learn project.
What if Atlas finds multiple matches for a single intended rename in a scikit-learn file?: The `edit` tool in Atlas is designed to prevent silent corruption. If it finds multiple matches for a single intended replacement, it will throw an error, prompting you to provide more context or explicitly opt into `replaceAll` for that specific file.
How does Atlas ensure my scikit-learn tests still pass after a rename?: After applying renames, Atlas uses its `bash` tool to run your project's test runner, `pytest`. It can execute `pytest sklearn/` behind a permission prompt, allowing you to verify that all tests pass and the refactoring hasn't introduced regressions.
Does Atlas integrate with scikit-learn's `ruff format` for code style?: Yes, Atlas integrates with `ruff format`. After making changes, you can instruct Atlas via its `bash` tool to run `ruff format .` on the modified files, ensuring your scikit-learn codebase remains compliant with its formatting standards.
Can I review the changes Atlas proposes before they are applied to my scikit-learn project?: Absolutely. Atlas computes a unified diff for every file edit and surfaces it for your approval before writing. It also drafts a plan in a read-only agent first, giving you full control and review capabilities over proposed changes to your scikit-learn codebase.
How does Atlas handle renaming a symbol that appears in scikit-learn documentation?: Atlas uses its `grep` tool to find occurrences of the old symbol name in documentation files (e.g., `.rst` files in `docs/`), comments, and string literals, in addition to code references found by `lsp`. This ensures the rename is comprehensive, covering both code and prose.

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