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documentationgood-first-issueThese are issues which can be tackled by new users and contributors.These are issues which can be tackled by new users and contributors.priority-low
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Lines 74 to 88 in fd580c3
| for i, choice in enumerate(optim_paras["choices"]): | |
| if f"wage_{choice}" in optim_paras: | |
| wage_columns = optim_paras[f"wage_{choice}"].index | |
| log_wage = np.dot( | |
| states[wage_columns].to_numpy(dtype=COVARIATES_DOT_PRODUCT_DTYPE), | |
| optim_paras[f"wage_{choice}"].to_numpy(), | |
| ) | |
| wages[:, i] = np.exp(log_wage) | |
| if f"nonpec_{choice}" in optim_paras: | |
| nonpec_columns = optim_paras[f"nonpec_{choice}"].index | |
| nonpecs[:, i] = np.dot( | |
| states[nonpec_columns].to_numpy(dtype=COVARIATES_DOT_PRODUCT_DTYPE), | |
| optim_paras[f"nonpec_{choice}"].to_numpy(), | |
| ) |
This is a part of the code that will frequently throw errors due when dealing with large models.
If there is a small spelling mistake or a corrupted row in the params file there will be a key error here.
I think we could provide the user with a more informative message indicating that this is probably due to a variable that is misspelled or not in the covariates.
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documentationgood-first-issueThese are issues which can be tackled by new users and contributors.These are issues which can be tackled by new users and contributors.priority-low