Add support for multiple loss functions in AADForest

[matwey]

No description provided.

[Copilot]

Pull Request Overview

This PR adds support for multiple loss functions to the AADForest class, refactoring the code to prepare for extensibility. The key changes include moving the _q_tau method from AADForest to AADEvaluator and adding a loss parameter to the constructor.

• Moves _q_tau method from AADForest to AADEvaluator for better encapsulation
• Adds loss parameter to AADForest constructor with validation
• Updates method calls to use the evaluator's _q_tau method

Reviewed Changes

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

File	Description
src/coniferest/aadforest.py	Refactors _q_tau method to AADEvaluator, adds loss parameter with validation
tests/test_aadforest.py	Updates test to call _q_tau through evaluator instead of forest directly

[Copilot]

[nitpick] The LOSSES list contains only one item, making the validation somewhat redundant. Consider either removing this validation until multiple loss functions are implemented, or add a comment explaining the future extensibility plan.

Suggested change

	# Currently, only the "hinge" loss function is supported.
	# This list is designed for future extensibility to include additional loss functions.

[Copilot]

The loss parameter is stored but never used in the implementation. Consider either implementing the loss function logic or adding a comment explaining how this will be used in future implementations.

Suggested change

	self.loss = loss
	self.loss = loss
	# The `loss` parameter is currently not used in the implementation.
	# It is reserved for future extensions where different loss functions
	# may be incorporated into the anomaly detection logic.

[codspeed-hq]

CodSpeed Performance Report

Merging #267 will improve performances by ×18

_{Comparing aad_loss_function (2b74f34) with master (c78dece)¹}

🎉 Hooray! pytest-codspeed just leveled up to 4.0.0!

A heads-up, this is a breaking change and it might affect your current performance baseline a bit. But here's the exciting part - it's packed with new, cool features and promises improved result stability 🥳!
Curious about what's new? Visit our releases page to delve into all the awesome details about this new version.

Summary

⚡ 46 improvements
✅ 5 untouched benchmarks

Benchmarks breakdown

	Benchmark	BASE	HEAD	Change
⚡	test_benchmark_fit_known	266.8 ms	205.8 ms	+29.65%
⚡	test_benchmark_loss_gradient[1024-1024]	4.4 ms	3.9 ms	+13.9%
⚡	test_benchmark_loss_gradient[1024-65536]	4.4 ms	3.9 ms	+13.85%
⚡	test_benchmark_feature_signature[128]	555.7 µs	409.1 µs	+35.84%
⚡	test_benchmark_feature_signature[2]	563.4 µs	414 µs	+36.06%
⚡	test_benchmark_fit_sklearn[1024]	5.3 s	4.5 s	+17.6%
⚡	test_benchmark_fit_sklearn[128]	668.1 ms	568.5 ms	+17.51%
⚡	test_benchmark_score[1024]	8.5 ms	7 ms	+22.72%
⚡	test_benchmark_score[1048576]	8.4 s	6.9 s	+21.5%
⚡	test_benchmark_score_samples[1-128-1024]	9.8 ms	8.3 ms	+18.46%
⚡	test_benchmark_score_samples[1-128-1048576]	9.7 s	8.3 s	+17.46%
⚡	test_benchmark_score_samples[1-128-1]	264.8 µs	125.7 µs	×2.1
⚡	test_benchmark_score_samples[1-128-32]	614.1 µs	456.3 µs	+34.6%
⚡	test_benchmark_score_samples[1-256-1024]	18 ms	15.1 ms	+19.68%
⚡	test_benchmark_score_samples[1-256-1048576]	17.9 s	15 s	+19.39%
⚡	test_benchmark_score_samples[1-256-1]	276.2 µs	158.2 µs	+74.56%
⚡	test_benchmark_score_samples[1-256-32]	978.7 µs	773.6 µs	+26.51%
⚡	test_benchmark_score_samples[1-64-1024]	5.6 ms	4.8 ms	+17.86%
⚡	test_benchmark_score_samples[1-64-1048576]	5.5 s	4.7 s	+15.62%
⚡	test_benchmark_score_samples[1-64-1]	223.6 µs	109.7 µs	×2
...	...	...	...	...

ℹ️ Only the first 20 benchmarks are displayed. Go to the app to view all benchmarks.

Footnotes

1. No successful run was found on master (b9ba72e) during the generation of this report, so c78dece was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩