Negative Selection Algorithms for Outlier detections

[kishordgupta]

All Submissions Basics:

• Have you followed the guidelines in our Contributing document? -- Hope so
• Have you checked to ensure there aren't other open Pull Requests for the same update/change? -- N/A
• Have you checked all Issues to tie the PR to a specific one? -- “No existing issue found for NSA family models.”

All Submissions Cores:

• Have you added an explanation of what your changes do and why you'd like us to include them? --Adds a new Negative Selection Algorithm family implementation for PyOD, covering historical NSA variants such as binary NSA, real-valued NSA, randomized real-valued NSA, V-Detector, grid/matrix NSA, adaptive/evolutionary NSA, PSO/DE-based NSA, bidirectional inhibition NSA, and related variants. The goal is to add immune-inspired anomaly detection coverage to PyOD’s tabular detector set.
• Have you written new tests for your core changes, as applicable? -Added NSA family tests covering fit, scoring, prediction, detector consistency, and basic benchmark execution.
• Have you successfully ran tests with your changes locally? Local result: 2 passed in 13.67s.
• Does your submission pass tests, including CircleCI, Travis CI, and AppVeyor?
• Does your submission have appropriate code coverage? The cutoff threshold is 95% by Coversall.

New Model Submissions:

• Have you created a .py in ~/pyod/models/? Added pyod/models/nsa.py.
• Have you created a _example.py in ~/examples/? Added examples/nsa_family_benchmark.py
• Have you created a test_.py in ~/pyod/test/? Added in examples
• Have you lint your code locally prior to submission? yes Local result: 2 passed in 13.67s.

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Convert NSA example file into an executable example

The new examples/nsa_example.py is written as a pytest test module (test_* functions with asserts) instead of a runnable example script, and it has no if __name__ == "__main__": execution path. Running this file directly will not provide the usage/demo behavior expected from files in examples/, so users do not get an actual NSA example despite the new-model example requirement.

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[chatgpt-codex-connector]

Preserve original features when caching train data

When feature subsampling is active, _restore_feature_matrix fills dropped columns with zeros before inverse scaling, so X_train_original_ is not the original training set but a distorted version with omitted features forced to feature minima. Any later partial_fit retrains on this corrupted history, which can shift scaling and detector behavior unexpectedly.

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[chatgpt-codex-connector]

Keep prior training data for grid partial_fit path

In the grid variant, when feature subsampling is used (X.shape[1] != n_features_in_), X_train_original_ is set to an empty array instead of the fitted training data. A subsequent partial_fit then refits only on the new batch (vstack with empty), dropping all historical self samples despite the method contract saying it combines old and new profiles.

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