Adjust per-series data pipeline, NB loss, and scheduler #191
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| alpha = torch.clamp(dispersion, min=eps) | ||
| mu = torch.clamp(rate, min=eps) | ||
| r = 1.0 / alpha | ||
| log_p = torch.log(r) - torch.log(r + mu) | ||
| log1m_p = torch.log(mu) - torch.log(r + mu) | ||
| log_prob = ( | ||
| torch.lgamma(y + r) | ||
| - torch.lgamma(r) | ||
| log1p_alpha_mu = torch.log1p(alpha * mu) | ||
| log_alpha = torch.log(alpha) | ||
| log_mu = torch.log(mu) | ||
| inv_alpha = torch.reciprocal(alpha) | ||
| ll = ( | ||
| torch.lgamma(y + inv_alpha) | ||
| - torch.lgamma(inv_alpha) | ||
| - torch.lgamma(y + 1.0) | ||
| + r * log_p | ||
| + y * log1m_p | ||
| + inv_alpha * (-log1p_alpha_mu) | ||
| + y * (log_alpha + log_mu - log1p_alpha_mu) |
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[P1] Restore missing log(alpha) term in NB likelihood
The refactored negative_binomial_nll rewrites the log‑likelihood but omits the -(1/alpha) * log(alpha) component that was implicitly present before through r * log_p. The new implementation only subtracts inv_alpha * log1p(alpha*mu) and thus produces a value that differs from the standard negative‑binomial log‑likelihood for any dispersion ≠ 1. This changes both the loss scale and the gradient with respect to dispersion, so training will optimize the wrong objective and can no longer match the behaviour of the previous formulation. Reintroducing the -inv_alpha * log_alpha term before masking fixes the derivation.
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Summary
Testing
https://chatgpt.com/codex/tasks/task_e_68d4df402ba88328b5937643c21c75d2