feat: adopt scikit-learn API conventions for 1.0.0

[vc1492a]

Summary

• Moves data parameters (data, distance_matrix, neighbor_matrix, cluster_labels) from __init__() to fit(), aligning with scikit-learn estimator conventions. Configuration parameters (extent, n_neighbors, use_numba, n_jobs, progress_bar) remain in the constructor.
• Adds backward compatibility: passing data params to __init__() still works but emits a FutureWarning.
• Adds LoOP class alias and exports __version__ from the package.
• Supports re-fitting (calling fit() multiple times with different data).
• Bumps version to 1.0.0.

Closes #5, Closes #7

Test plan

• All 40 existing tests updated to new API style and passing
• New tests for backward compat (FutureWarning), numerical equivalence, re-fitting, fit() override precedence, and LoOP alias
• CI passes across Python 3.8–3.14

Made with Cursor

[Copilot]

Pull request overview

This PR refactors PyNomaly’s LocalOutlierProbability estimator toward scikit-learn-style usage by moving data-bearing inputs from __init__() to fit(), while preserving backward compatibility via FutureWarnings and updating the public API/docs for the 1.0.0 release.

Changes:

• Shift data parameters (data, distance_matrix, neighbor_matrix, cluster_labels) to fit() and add FutureWarning-based backward compatibility for passing them to __init__().
• Refactor the implementation into mixins/modules (_validation, _distance, _pipeline, _utils) and add LoOP alias + __version__ export.
• Update tests and documentation/examples to the new API and bump version references to 1.0.0.

Reviewed changes

Copilot reviewed 14 out of 14 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
tests/test_loop.py	Updates existing tests to call fit(X) and adds new tests for compatibility/refit/alias behavior.
setup.py	Bumps package version to 1.0.0 and updates download URL.
README.md	Updates badges and usage examples to the new fit()-centric API and LoOP alias.
PyNomaly/loop.py	Implements the new estimator API shape (data args in fit()), deprecation warnings, refit reset behavior, and LoOP alias.
PyNomaly/exceptions.py	Extracts custom exceptions into a dedicated module.
PyNomaly/_validation.py	Adds validation mixin + accepts decorator used by the estimator.
PyNomaly/_utils.py	Extracts progress bar helper into a utility module.
PyNomaly/_pipeline.py	Extracts the LoOP scoring pipeline computations into a mixin.
PyNomaly/_distance.py	Extracts distance/neighbor computation into a mixin (vectorized + Numba paths).
PyNomaly/init.py	Re-exports LoOP and __version__ (plus exceptions) as public API.
docs/user-guide.md	Updates documentation code snippets to pass data to fit().
docs/getting-started.md	Updates quickstart snippets to use LoOP and fit(data).
docs/changelog.md	Adds 1.0.0 changelog entry describing the API change and additions.
changelog.md	Mirrors the 1.0.0 changelog entry at repo root.

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[SaritNike]

There are many compatibility issues with scikit-learn. Most of them are easy to fix and scikit learn provides helper functions to check the compatibility first. Instead of listing them all here, i will add a commit that includes this check in the unit tests.

[SaritNike]

Scikit learn requires following a certain inheritance structure (e.g. BaseEstimator) etc.
As it stands the code is currently not compatible with scikit-learn API