GLM negative log likelihood method returns incorrect log likelihood

[tomasfryda]

model.negative_log_likelihood() reports the fast calculated log likelihood (just the part that's used for the parameter estimation) even when calc_like=True. model.model_performance().loglikelihood() reports the correct log likelihood.

This is confusing especially when AIC is orders of magnitude different than reported by model.negative_log_likelihood(). I think we should make model.negative_log_likelihood() return the correct log likelihood.

[wendycwong]

[wendycwong]

I think @tomasfryda suggestion is the way to go. We will through an warning when user calls the function model.negative_log_likelihood(). Please add a warning to model.negative_log_likelihood() call that says:

This is the simplified negative log likelihood function used during training for speedup. To see the correct values (for loglikelihood and AIC), set calc_like=True and call model.model_performance().loglikelihood() and model.model_performance().aic()