Enhance posterior viz #58
Conversation
There was a problem hiding this comment.
I made a few comments. In general, I think I'd prefer a less "thin" wrapper, i.e. make the API more like brian2modelfitting (use dictionaries with parameter names and quantities) than sbi (use PyTorch tensors). Also, maybe add example calls for these functions somewhere, e.g. in the advanced sbi example?
But if this PR keeps you from advancing, feel free to merge it as it is and we can deal with these details later.
brian2modelfitting/inferencer.py
Outdated
| try: | ||
| d = self.posterior | ||
| except AttributeError as e: | ||
| print(e, '\nDensity is not available.') |
There was a problem hiding this comment.
I think this should rather be something like
raise AttributeError('Density is not available.') from einstead of print + raise
brian2modelfitting/inferencer.py
Outdated
|
|
||
| Parameters | ||
| ---------- | ||
| condition : torch.tensor |
There was a problem hiding this comment.
In the rest of the code we tried to avoid that the user has to deal with pytorch tensors themselves. Maybe this could be something like a dictionary mapping parameter names to values (like the parameters in generate, for example)? But then this should be probably what sample returns as well...
brian2modelfitting/inferencer.py
Outdated
| condition : torch.tensor | ||
| Condition that all but the one/two regarded parameters are | ||
| fixed to. | ||
| limits : list or torch.tensor |
There was a problem hiding this comment.
Couldn't we make this optional and reuse the bounds here?
brian2modelfitting/inferencer.py
Outdated
| condition : torch.tensor | ||
| Condition that all but the one/two regarded parameters are | ||
| fixed to. | ||
| limits : list or torch.tensor | ||
| Limits in between which each parameter will be evaulated. |
|
All your comments are taken into account. As we discuss, I will merge this to |
Resolves #51
Added method
conditional_pairplotandconditional_corrcoefftoInferencerclass, which are basically thin wrappers aroundsbi.analysis.plot.conditional_pairplotandsbi.analysis.conditional_corrcoeff, respectively.Also features are now optional argument, if
featuresare not provided, the density estimator will employ a MLP by default in order to automatically extract features from high-D data.