Atlas empowers scikit-learn developers in 2026 to move an entire codebase off a deprecated function or module onto its replacement without missing a caller by systematically enumerating all callsites, applying context-anchored patches, and validating changes with `pytest` and `ruff format`.
How to find all deprecated scikit-learn API calls with Atlas?
Atlas efficiently identifies every callsite of a deprecated scikit-learn API using a hybrid approach, combining `lsp`'s precise reference finding with `grep` for dynamic usages, ensuring 100% coverage across your project in 2026.
To begin migrating a deprecated API in your scikit-learn codebase, the first critical step is to enumerate every single callsite. Atlas achieves this comprehensive enumeration by leveraging two powerful tools. First, the `lsp` tool's `findReferences` operation queries the language server to yield a complete set of static callers for the deprecated symbol. This is highly effective for direct function or class calls within your Python files. Second, Atlas cross-checks these findings with `grep` for any dynamic or string-based usages that a language server might miss, such as references in configuration files, docstrings, or dynamically constructed calls. This dual approach ensures that no callsite, whether explicit or implicit, is overlooked in your scikit-learn project, providing a robust foundation for the migration process. Atlas operates within your project's `pyproject.toml` context, understanding your dependencies and code structure.
How Atlas tracks deprecated API migration progress in scikit-learn?
Atlas tracks every deprecated API callsite as a distinct `todowrite` entry, providing clear visibility into partial progress and preventing any silent skips across your scikit-learn project, making it easy to manage 10s or 100s of changes.
Migrating a deprecated API across an entire scikit-learn codebase can involve dozens or even hundreds of changes. Atlas addresses the challenge of managing this scale by creating one `todowrite` entry for each identified callsite. This approach transforms the migration into a series of tracked, manageable tasks. As Atlas processes each callsite, the `todowrite` entry is updated, making partial progress visible and ensuring that no change is silently skipped or forgotten. This granular tracking is crucial for large-scale refactoring efforts, providing a clear audit trail and allowing developers to pause and resume work without losing context. It ensures that the job of moving an entire codebase off a deprecated function or module onto its replacement is completed without missing a single caller.
How Atlas applies deprecated API changes to scikit-learn code?
Atlas migrates each deprecated scikit-learn API callsite using `apply_patch`, a context-anchored tool that precisely seeks the hunk's context and old lines, failing rather than misapplying to a drifted file, ensuring 0 accidental corruptions.
Once all callsites are enumerated and tracked, Atlas proceeds to migrate each one using the `apply_patch` tool. This tool is designed for precision and safety, crucial when modifying core scikit-learn components like Pipelines or custom estimators. `apply_patch` does not guess; it seeks the exact hunk's context and old lines within the target file. If the file has drifted or the expected lines are not found, it throws a 'Failed to find expected lines' error, preventing misapplication and potential code corruption. For example, if you are moving a scaler that was fit on the full dataset inside a Pipeline to prevent data leakage, Atlas will precisely locate the old scaler instantiation and replace it with the new, correctly scoped version within the Pipeline definition. This ensures that complex scikit-learn idioms, such as those found in `ColumnTransformer` or custom `fit`, `transform`, and `predict` methods, are handled with utmost care and accuracy.
How Atlas validates scikit-learn API migrations with `pytest` and `ruff format`?
Atlas ensures the integrity of scikit-learn API migrations by running affected tests with `pytest` after each file modification and applying `ruff format` to maintain code style, guaranteeing 100% functional and stylistic correctness.
Maintaining code quality and correctness during a large-scale migration is paramount. Atlas integrates directly with your scikit-learn project's toolchain to validate changes. After each file is modified by `apply_patch`, Atlas uses the `bash` tool to run the affected tests with `pytest`. For instance, it might execute `atlas bash -c "pytest path/to/modified_module_tests.py"`. Only once these tests pass is the corresponding `todowrite` entry marked as completed. This immediate feedback loop catches regressions early. Furthermore, Atlas applies `ruff format` to the modified diff using a command like `atlas bash -c "ruff format path/to/modified_file.py"` to ensure that all changes adhere to your project's formatting standards. Every Atlas tool call, including these validation steps, is permission-gated against allow, ask, and deny rules, giving you full control over execution.
How Atlas handles review and commits for scikit-learn API changes?
Atlas provides a robust review process for scikit-learn API migrations, computing a unified diff for every file edit and surfacing it for approval before writing, and can stage and create commits on your behalf, streamlining the process by 2026.
Transparency and control are central to Atlas's workflow. For every file edit proposed during a scikit-learn API migration, Atlas computes a unified diff and surfaces it for your approval. This allows you to review the exact changes before they are written to your codebase. Atlas also reads git branches, status, and diffs, and can stage and create commits on your behalf, simplifying the version control aspect of the migration. It snapshots file changes as git patches, so edits can be easily diffed and rolled back if necessary. This comprehensive review mechanism, combined with Atlas's terminal-native TUI rendered with SolidJS through the OpenTUI renderer, provides a clear and interactive experience, ensuring that you maintain full oversight throughout the entire migration process.
Step by step
- 01Enumerate every callsite of the deprecated scikit-learn symbol using `atlas lsp findReferences` and cross-check with `atlas grep` for dynamic or string-based usages.
- 02Create one `atlas todowrite` entry per identified callsite to track partial progress and ensure no scikit-learn API migration is silently skipped.
- 03Migrate each scikit-learn callsite using `atlas apply_patch`, which seeks the hunk's context and old_lines, throwing 'Failed to find expected lines' rather than guessing.
- 04Run the affected scikit-learn tests with `atlas bash -c "pytest path/to/affected_test.py"` after each file modification, and mark the `todowrite` entry completed only once they pass.
- 05Apply `ruff format` to the modified scikit-learn files using `atlas bash -c "ruff format path/to/modified_file.py"` to maintain code style.
- 06Review the unified diffs presented by Atlas and approve the changes before they are written to your scikit-learn project.
- 07Finish by confirming zero remaining hits for the deprecated scikit-learn symbol with `atlas grep "deprecated_symbol"`, then delete the old implementation.
- 08Allow Atlas to stage and create a commit for the completed scikit-learn API migration.
Frequently asked questions
- How does Atlas ensure it finds *all* deprecated scikit-learn API calls?
- Atlas combines `lsp findReferences` for static symbol usage with `grep` for dynamic or string-based usages, ensuring a complete enumeration of every deprecated scikit-learn API callsite.
- Can Atlas handle complex scikit-learn refactors like moving a scaler inside a Pipeline?
- Yes, Atlas can handle complex scikit-learn refactors. For instance, it can move a scaler that was fit on the full dataset inside a Pipeline, addressing classic data leakage bugs, by applying precise, context-aware patches.
- What happens if Atlas tries to apply a patch to a scikit-learn file that has changed?
- If a scikit-learn file has drifted, `apply_patch` will throw 'Failed to find expected lines' rather than misapplying the patch. This ensures no accidental corruption of your codebase.
- How does Atlas integrate with scikit-learn's testing and formatting tools?
- Atlas integrates directly by running `pytest` via the `bash` tool after each file modification to validate functional correctness, and applies `ruff format` to maintain code style, all behind permission prompts.
- Is my scikit-learn code sent to third-party servers when using Atlas?
- No, Atlas can build its code index with local Ollama embeddings, keeping your scikit-learn code off third-party servers and ensuring privacy. Every Atlas tool call is permission-gated.
- How does Atlas help me review the changes it makes to my scikit-learn project?
- Atlas computes a unified diff for every file edit and surfaces it for your approval before writing. It also snapshots file changes as git patches, allowing edits to be diffed and rolled back easily.
- Can Atlas commit changes to my scikit-learn repository?
- Yes, Atlas reads git branches, status, and diffs, and can stage and create commits on your behalf after you approve the proposed changes, streamlining your workflow.
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