Implement HTFA parameter support: n_levels, backend, random_state

[jeremymanning]

Description

The current HTFA implementation is missing several expected parameters that are tested in the disabled test suite.

Missing Parameters

• n_levels - Control number of hierarchical levels (currently assumes 2)
• backend - Support for different computational backends (NumPy, JAX, PyTorch)
• random_state - Ensure reproducible results
• max_iter - Control maximum iterations for convergence

Expected Behavior

# Should work:
model = HTFA(n_factors=5, n_levels=3, backend="numpy", random_state=42, max_iter=100)

Current Status

These parameters cause TypeError: unexpected keyword argument errors.

Tests to Re-enable

When this is implemented, re-enable the following tests in tests/test_htfa_core.py.disabled:

• test_htfa_initialization
• test_htfa_different_levels
• test_htfa_reproducibility
• test_invalid_backend
• All parametrized tests using n_levels

Implementation Notes

• Backend support should start with NumPy and be extensible
• n_levels=1 should work like standard factor analysis
• Random state should control all random operations for reproducibility

Related Files

• htfa/core/htfa.py - Main implementation
• tests/test_htfa_core.py.disabled - Tests to re-enable

[jeremymanning]

Status Update: Partially implemented ⚠️

Evidence from codebase analysis:

• ✅ random_state: Implemented in htfa/core/htfa.py:53,62
• ❌ n_levels: Not found in current implementation
• ❌ backend: Backend architecture exists (htfa/backend_base.py) but not integrated into HTFA class

Current backend architecture:

• Abstract HTFABackend class defined
• NumPy backend implemented
• JAX/PyTorch backends are stubs only

Next steps: Integrate backend parameter into HTFA constructor and add n_levels support for hierarchical depth control.