Atlas empowers scikit-learn developers in 2026 to upgrade dependencies and repair all resulting compile and test failures by driving your existing toolchain, including `uv` for package management, `pytest` for testing, and `ruff format` for code style, directly from the terminal.
How Atlas upgrades scikit-learn dependencies with uv
In 2026, Atlas streamlines upgrading scikit-learn dependencies by driving `uv` directly through its `bash` tool. This process begins with Atlas executing `uv update` within your `pyproject.toml` project, capturing the full output, even if it exceeds 1000 lines, to a file for your review.
Atlas integrates directly with your scikit-learn project's existing package manager, `uv`, to initiate dependency upgrades. When you instruct Atlas to bump a library to a new major version, it uses its `bash` tool to execute the `uv update <package-name>` command. This ensures that the upgrade process leverages your project's `pyproject.toml` configuration and adheres to your defined dependency pins. Atlas is designed to capture the complete output from `uv`, regardless of its length. If the output exceeds typical terminal limits, Atlas automatically saves it to a file, which you can then read using the `read` tool, providing a comprehensive record of the upgrade process and any initial warnings or errors from `uv`.
How Atlas identifies and repairs scikit-learn breaking changes
Atlas identifies breaking changes in scikit-learn by reading real compiler and test output, not by guessing, a critical step in 2026. After an upgrade, Atlas uses `bash` to run `pytest` or a build command, then `webfetch` to retrieve the library's release notes, ensuring fixes align with actual breaking changes.
After a dependency upgrade, Atlas systematically identifies breakage by running your scikit-learn project's tests and build processes. It uses the `bash` tool to execute `pytest`, your project's designated test runner, capturing all failures. Rather than making assumptions, Atlas analyzes the actual error messages from `pytest` to understand the nature of the breakage. To provide context for these errors, Atlas then employs its `webfetch` tool to retrieve the upgraded library's release notes or changelog. This crucial step ensures that Atlas's subsequent fixes are informed by the documented breaking changes, allowing it to address specific API changes, deprecations, or new requirements, such as changes to `fit`, `transform`, or `predict` signatures, accurately.
Fixing scikit-learn API breakages with Atlas's edit and lsp
Atlas fixes scikit-learn API breakages by leveraging its `edit` tool, guided by the `lsp` tool's `goToDefinition` operation, a precise method for 2026 development. This allows Atlas to inspect new method signatures, like those for `fit` or `transform`, directly within the upgraded package, ensuring accurate code modifications.
When scikit-learn API calls break due to a major version upgrade, Atlas uses its `edit` tool to apply targeted fixes. For instance, if a `Pipeline` or `ColumnTransformer` definition, or a custom estimator's `fit` method, now expects different parameters, Atlas can navigate directly to the new signature. It achieves this by integrating with Model Context Protocol servers, exposing the `lsp` tool's `goToDefinition` operation. This allows Atlas to jump to the definition of a function or method within the upgraded package, inspect its new signature, and then use the `edit` tool to modify the call site in your scikit-learn codebase. This precise, context-aware approach ensures that fixes, such as adjusting parameters for `get_params` or `set_params` in a custom estimator, are correct and align with the upgraded library's API.
Iterative testing and formatting scikit-learn with Atlas
Atlas ensures scikit-learn code quality by iteratively running `pytest` and applying `ruff format` until all issues are resolved, a robust approach for 2026. After each round of fixes, Atlas re-executes `pytest` to confirm test pass rates and then applies `ruff format` to maintain consistent code style across your project.
The process of fixing a major dependency upgrade in scikit-learn is iterative, and Atlas manages this cycle efficiently. After applying a set of fixes with the `edit` tool, Atlas uses `bash` to re-run `pytest`. This immediate feedback loop confirms whether the recent changes have resolved existing test failures or introduced new ones. Atlas continues this cycle of fixing and re-testing until all `pytest` runs are clean. Once functional correctness is established, Atlas then applies your project's formatter, `ruff format`, to the modified files. This ensures that all changes adhere to your team's coding standards, maintaining a consistent and readable codebase, which is crucial for collaborative scikit-learn development.
Securely reviewing and committing scikit-learn changes with Atlas
Atlas provides a secure and transparent workflow for reviewing and committing scikit-learn changes, a key feature for 2026 development. Every Atlas tool call is permission-gated, and all file edits are presented as a unified diff for your approval, ensuring you retain full control over your codebase.
Security and developer control are paramount in Atlas's workflow for scikit-learn projects. Before any tool call, such as `bash` to run `pytest` or `edit` to modify a `Pipeline` definition, Atlas prompts for permission based on allow, ask, or deny rules. All proposed file edits are presented as a unified diff, allowing you to review every change before it's written to disk. Atlas also snapshots file changes as git patches, enabling easy diffing and rollback if needed. Once you approve the changes, Atlas can stage and create commits on your behalf, integrating direct with your git workflow. This transparent process, combined with a read-only plan agent that drafts actions before execution, ensures you have complete oversight and control over every modification to your scikit-learn codebase.
Step by step
- 01Initiate Atlas in your scikit-learn project with a `pyproject.toml` file that pins your dependencies.
- 02Ask Atlas to 'bump <package-name> to <new-major-version>' and let it execute `uv update <package-name>` via `bash`, capturing the full output.
- 03Instruct Atlas to 'webfetch release notes for <package-name>' to understand the documented breaking changes.
- 04Have Atlas run `pytest` via `bash` to enumerate all test failures caused by the upgrade.
- 05For each reported error, ask Atlas to 'fix this error' using its `edit` tool, leveraging `lsp`'s `goToDefinition` to inspect new scikit-learn API signatures like `fit` or `transform`.
- 06After Atlas applies fixes, re-run `pytest` via `bash` to confirm test pass rates, repeating until all tests are clean.
- 07Ask Atlas to 'apply `ruff format` to all changed files' to ensure code style consistency.
- 08Review the unified diff of all changes presented by Atlas and approve the edits.
- 09Instruct Atlas to 'stage and commit these changes' to finalize the dependency upgrade.
Frequently asked questions
- How does Atlas handle `uv` dependency conflicts in scikit-learn projects?
- Atlas drives `uv` through its `bash` tool, allowing `uv` to report any dependency conflicts. Atlas captures this full output, which you can `read`, and then you can instruct Atlas to resolve specific conflicts by modifying your `pyproject.toml`.
- Can Atlas fix custom scikit-learn estimators that fail `check_estimator` after an upgrade?
- Yes, Atlas can fix custom estimators. It uses `lsp`'s `goToDefinition` to inspect new `get_params` or `set_params` requirements in the upgraded library and then applies precise `edit` operations to make your custom estimator pass `check_estimator`.
- What if the scikit-learn library's changelog is not on a public website?
- Atlas's `webfetch` tool is designed for public web resources. If the changelog is local or internal, you would use Atlas's `read` tool to access the file, providing the necessary context for fixes.
- How does Atlas ensure I approve all code changes in my scikit-learn project?
- Atlas operates with a read-only plan agent and presents all proposed file edits as a unified diff for your explicit approval before writing any changes. Every tool call is also permission-gated.
- Can Atlas fix issues in scikit-learn `Pipeline` or `ColumnTransformer` definitions?
- Absolutely. Atlas can read your `Pipeline` and `ColumnTransformer` definitions. If an upgrade breaks their API, Atlas uses `edit` and `lsp` to inspect new method signatures and apply the necessary corrections to your code.
- Does Atlas integrate with my existing git workflow for scikit-learn projects?
- Yes, Atlas reads git branches, status, and diffs. After you approve changes, Atlas can stage and create commits on your behalf, integrating direct with your existing git workflow for scikit-learn development.
- How does Atlas handle large `pytest` outputs when fixing scikit-learn tests?
- Atlas captures the full output from `pytest` when run via `bash`. If the output exceeds terminal limits, Atlas automatically saves it to a file, which you can then `read` to review all test failures and details.
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