Isolation Forest Implementation

[shenxiangzhuang]

Close #127

Summary by CodeRabbit

• New Features

  • Introduced the Isolation Forest algorithm for anomaly detection, including the IsolationTree and IsolationForest classes.
  • Added methods for fitting models, predicting anomaly scores, and determining outliers.

• Documentation

  • Added comprehensive documentation for the Isolation Forest algorithm, detailing its components and usage.
  • Updated navigation to include the Isolation Forest documentation in the user guide.

• Tests

  • Implemented a suite of unit tests for the IsolationTree and IsolationForest classes to ensure functionality and correctness.

[coderabbitai]

Caution

Review failed

The pull request is closed.

Walkthrough

The changes introduce a new file, iforest.py, which implements the Isolation Forest algorithm for anomaly detection. It defines two main classes: IsolationTree, which structures the isolation tree and includes methods for fitting the tree and calculating sample path lengths, and IsolationForest, which manages a collection of IsolationTree instances, allowing for fitting on datasets and predicting anomaly scores. Additionally, documentation and tests for these classes have been added to support their functionality.

Changes

File Path	Change Summary
toyml/ensemble/iforest.py	Added IsolationTree and IsolationForest classes with methods for tree fitting, prediction, and sample path length calculations.
docs/algorithms/ensemble/iforest.md	New documentation for the Isolation Forest algorithm, detailing its components and usage.
mkdocs.yml	Added entry for "Isolation Forest" documentation in the navigation section.
tests/ensemble/test_iforest.py	Introduced unit tests for IsolationTree and IsolationForest classes, covering various functionalities.
toyml/ensemble/init.py	Updated __all__ list to include IsolationForest for module exports.

Assessment against linked issues

Objective	Addressed	Explanation
Implement Isolation Forest	✅

Poem

In the forest where anomalies hide,
A rabbit hops with a new guide.
With trees that isolate, paths so clear,
Anomalies tremble, they sense the fear!
Hopping through data, so brave and spry,
With Isolation Forest, we reach for the sky! 🌳✨

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[codecov]

Codecov Report

Attention: Patch coverage is 99.20000% with 1 line in your changes missing coverage. Please review.

Project coverage is 97.86%. Comparing base (e973902) to head (7ceaff6).
Report is 2 commits behind head on master.

Files with missing lines	Patch %	Lines
toyml/ensemble/iforest.py	99.19%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #130      +/-   ##
==========================================
+ Coverage   97.61%   97.86%   +0.24%     
==========================================
  Files          10       11       +1     
  Lines         670      795     +125     
==========================================
+ Hits          654      778     +124     
- Misses         16       17       +1     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[coderabbitai]

Actionable comments posted: 5

🧹 Outside diff range and nitpick comments (1)

toyml/ensemble/iforest.py (1)

158-162: Consider adding unit tests to improve code coverage and ensure correctness

The static analysis hints indicate that several parts of the code are not covered by tests. Writing comprehensive unit tests for key methods and edge cases will help catch bugs and ensure that the implementation behaves as expected.

Would you like assistance in creating unit tests for the IsolationTree and IsolationForest classes?

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

📥 Commits

Files that changed from the base of the PR and between e973902 and 73e86c8.

📒 Files selected for processing (1)

• toyml/ensemble/iforest.py (1 hunks)

🧰 Additional context used

🪛 GitHub Check: codecov/patch

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