Regression support [PATCH PR] #52
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| @@ -213,3 +213,142 @@ def jsonize(x): return json.dumps(x) | |||
| ''' % (random_id, predict_proba_js, exp_js, raw_js) | |||
| out += u'</body></html>' | |||
| return out | |||
| class RegressionsExplanation(object): | |||
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This class has a lot of overlap with the original Explanation. If the only difference is in as_html, it's better to add a parameter to init that specifies if the explanation is a Classification or Regression. I made ImageExplanation separate because it was very different than the others, but this seems similar enough.
| @@ -330,3 +332,107 @@ def __data_inverse(self, | |||
| inverse[1:] = self.discretizer.undiscretize(inverse[1:]) | |||
| inverse[0] = data_row | |||
| return data, inverse | |||
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| def explain_regressor_instance(self, data_row, predict_fn, num_features=10, | |||
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Again, I think there is enough code duplication here that it may be worth just having a parameter either in the init function or in explain_instance, specifying if it's regression or classification.
| @@ -0,0 +1,138 @@ | |||
| import d3 from 'd3'; | |||
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The bar in this visualization seems to be shifted way to the right, it seems that it should be under 'Predicted value'
I'm not sure I understand what you meant here.
Yeah, that would be fine.
These all sound great. Thanks : ) |
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I still haven't taken a look at the new changes, I'm hoping you'll push a version that passes Travis (i.e. flake8) : ) |
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@aikramer2 do you need help with this PR? Regression support is something I also need soon so I am happy to help if you don't have time to work on it. |
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Hey guys, sorry for the delay, I'll submit updates to the PR soon, nearly done. |
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@aikramer2 do you think you will get around to this anytime soon? |
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@aikramer2 since you don't seem to have the time, would it be okay if I took your changes in this PR, refactored them according to @marcotcr's suggestions and submitted a new PR? I really need to use LIME with regression models and can't really wait any longer. @marcotcr would that be okay with you? |
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@desilinguist Sorry about the delay, I've been quite sidetracked with other projects. Go for it! |
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Sorry to barge in on this PR. I'm currently thinking of adding regression support to. the R implementation, and just need to make sure that we are in line with the methodology. The main difference with the classifier is simply that we are training the ridge regressor on the raw predicted values rather than on the class probabilities, right (I know there are other differences in terms of the presentation, but I'm only interested in the pure modelling part at the moment)? |
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@desilinguist I'm in the same situation and am happy to help you out ASAP. |
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Thanks so much, @Aylr! I'm hoping to start working on this next week.
However, if you think you'll have time before then, please go for it :)
…On Fri, Jun 23, 2017 at 8:11 PM Aylr ***@***.***> wrote:
I've forked this branch, updated it with the current master, and made
minor changes to get flake8 to pass on travis. Note *I have not* made any
other changes that @marcotcr <https://github.com/marcotcr> suggested, but
at least it builds.
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FYI @desilinguist did some quick work and addressed @marcotcr 's concerns in PR #74 |
#3
This is a bare minimum code change for regression. Currently its structured as method of lime-tabular. Since the js was updated i had to recompile bundle.js. Ideally, I would restructure the code a bit further to:
base explanation
a.base_regressor
i. lime classifier tabular
etc
b.base_classifier
i. lime classifier tabular
etc
If you agree i can add these base classes.
I also think we should push down kernel arguments to the explanation methods rather than defining them on init. Users may want to do:
.explain(kernel_width = 1.)
.explain(kernel_width = 10.)
I have some additional thoughts on the module, in particular providing an optional, non-default stratified sampler, centering the neighbor around the instance rather than the population mean, providing an optional non-default local affinity scaling regime as a similarity measure, adding unit test runs to travis yml, and abstracting a dataset to a separate object. Ill create PR(s) for these in a bit.
With respect to visuals for regression, i removed the class probabilities, and replaced it with a predicted value inside a window representing the range of regression values from the neighborhood: