Comments (1)
For computing the Mean Rank (MR), you first need to obtain all results (metrics) for all methods.
Assuming the results are obtained in the form of an array, results
, with methods for rows and task metrics for columns, you can compute mean rank as follow (not tested):
import numpy as np
results = np.array([...])
order = results.argsort(axis=0)
ranks = order.argsort(axis=0) + 1
method_rank = ranks.mean(1)
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Related Issues (14)
- Error about IMTLG method HOT 4
- About GradNorm HOT 2
- MT10 Training Code HOT 2
- Thank you for the great repository.If I don't know which parameter is shared and feed all parameters into method, can I get the right result? HOT 3
- Question about large weights HOT 6
- Logging HOT 4
- Questions about the property of convergence on theorem 5.4 and 5.5 HOT 1
- MTL for multiple modules? HOT 2
- hi,i want to apply nash-mtl to my multi-task learning net. HOT 20
- about two warnings when training the model HOT 2
- About an errror during training process HOT 3
- Possible error in the implementation of CAGrad HOT 1
- is there code for torch.distributed? HOT 6
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