[Hierarchical] Allow computing projection based on a non-Euclidean norm #2960
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melopeo
requested changes
Aug 11, 2023
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Thanks for this PR!
A question I have is: is there any way the user can provide the proposed matrix D through the estimator? Currently the matrix S is provided, which is used to compute the projection matrix A. I guess we need to provide the option to give the matrix D as a parameter in the estimator.
melopeo
reviewed
Aug 11, 2023
| def null_space_projection_mat(A: np.ndarray) -> np.ndarray: | ||
| def null_space_projection_mat( | ||
| A: np.ndarray, | ||
| D: Optional[np.ndarray], |
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Why do we give this matrix as optional?
What are the advantages against D: np.array = None? With the current approach tests are failing.
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ya, =None is missing actually. Let me fix.
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Issue #, if available:
Description of changes:
Currently the forecasts are reconciled by projecting them on to the feasible set based on the Euclidean norm. Sometimes it is desirable to weigh the distances (errors) between the unreconciled and reconciled forecasts differently for each level (because there are more number of terms for the bottom level compared to the top level).
This PR adds an option to provide such weights via a diagonal matrix.
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Please tag this pr with at least one of these labels to make our release process faster: BREAKING, new feature, bug fix, other change, dev setup