Harden trainer edge cases, vectorize input mapping, and unify pytest configuration

[Copilot]

Summary

This PR addresses concrete quality issues by tightening trainer/runtime invariants, removing hot-path inefficiencies in input preparation, and resolving config drift between pytest config files. Scope is surgical: correctness under edge conditions, lower per-step overhead, and cleaner project structure.

Changes

• Trainer safety invariants

  • Validate gradient_accumulation_steps in train_batch (int and >= 1).
  • Validate fit inputs early:
    • batch_size >= 1
    • non-empty dataset
    • input_features / target_values length match.

• Input pipeline performance

  • Replaced per-feature Python loops in prepare_input with vectorized indexed assignment for 2D and 3D cases.

• Test/config alignment

  • Added trainer tests for invalid accumulation steps, empty dataset, length mismatch, and invalid batch size.
  • Removed duplicate pytest config from pyproject.toml so pytest.ini remains the single source of truth.

# before: O(num_assigned) Python loop
for k in range(num_assigned):
    x_input[:, model_input_ids[k]] = input_features[:, k]

# after: vectorized assignment
target_ids = model_input_ids[:num_assigned]
x_input[:, target_ids] = input_features[:, :num_assigned]

Checklist

All contributions

• Code follows the project conventions (see CONTRIBUTING.md)
• Tests pass (python -m pytest tests/or pytest tests/)
• No sys.path.append hacks — imports use from odyssnet import ... directly

Library changes (odyssnet/)

• Corresponding test added/updated under tests/
• Documentation updated in relevant markdown files (LIBRARY.md, CONTRIBUTING.md)

New/modified example scripts (examples/)

• set_seed(42) is called as the first line of main()
• Placed in correct folder (examples/ for core validations, examples/advanced/ for complex tasks)
• Uses OdyssNetTrainer (not a manual training loop)
• Uses TrainingHistory to record metrics and calls history.plot() at the end
• File paths are relative to __file__, not hardcoded

[Copilot]

Pull request overview

This PR tightens trainer input/runtime invariants, reduces per-step overhead in the input preparation path by vectorizing feature-to-neuron mapping, and removes duplicated pytest configuration to avoid config drift.

Changes:

• Added early validation in OdyssNetTrainer.train_batch() and fit() for common invalid/edge-case inputs.
• Vectorized prepare_input() feature assignment for both 2D and 3D inputs.
• Added regression tests for the new trainer invariants and removed duplicate pytest config from pyproject.toml.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.

File	Description
odyssnet/training/trainer.py	Adds early validation for gradient_accumulation_steps, batch_size, and dataset shape invariants.
odyssnet/utils/data.py	Replaces per-feature Python loops with vectorized indexed assignment in prepare_input().
tests/training/test_trainer.py	Adds tests covering invalid accumulation steps, empty dataset, length mismatch, and invalid batch size.
pyproject.toml	Removes [tool.pytest.ini_options] so pytest.ini is the single source of truth.

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