Comments (5)
So, the reason I kept them as html files was that the notebooks do not show the visualizations (because of the weird way in which I generate them with javascript).
Did you have a fix for that?
Thanks,
from lime.
Ahh I see your point. So the notebooks, when uploaded to Github, do not show the visualizations. But on running the notebooks locally, the visualizations render fine. I have attached a screenshot for the same.
So maybe we can add the notebooks with the disclaimer that visualizations render properly only when they are run locally (as of now). In current scenario, where the demo is not available as executable code, it is harder to experiment and play around with.
from lime.
Hey @marcotcr I would love to know your feedback on the proposed issue and PR.
from lime.
Right now, we do have the tutorials in the repository, under doc/notebooks. I guess if you couldn't find it, it means we should create a link in the README.
I prefer to keep the gh-pages as html so that the visualizations work.
from lime.
Ouch I never realized that. The PR does exactly this :)
from lime.
Related Issues (20)
- Is this Repo Maintained? HOT 2
- Can LIME only be used for text classification tasks?
- How to explain prediction for a data with just a few features (from all features of training dataset)? HOT 2
- Trying to explaine a simple Neural Network using LIME HOT 1
- submodular pick
- ans = self.domain_mapper.map_exp_ids(self.local_exp[label_to_use], **kwargs) KeyError: 1
- Getting error when my predict_fn is actually a method from a class HOT 1
- `show_in_notebook` shows no bars
- importance score for all tokens
- Unexpected keyword argument 'progress_bar' for lime_image.LimeImageExplainer().explain_instance()
- Issues with LIME Implementation in Image Classification
- How to use lime with 3 dim time series data and lstm
- Distances derivation, explain_instance() method in Tabular LIME
- How to handle binary features
- RecurrentTabularExplainer for regression task
- Question: using LIME for text generation HOT 1
- LIME and multi-correlated variables
- update Python support
- Inverse scaling/ normalization to get actual unscaled values in explanation : Old issue but I made a way around
- Met the issue about TypeError when using lime_image.LimeImageExplainer()
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from lime.