Comments (6)
Implement a base class that will follow an estimator in a pipeline (keep in mind that there could be more than one of these things stacked up along each other). This is not a transformer. It needs to extend BaseEstimator
, VisualizerMixin
. We also need to create a ScoringMixin
. ScoringMixin
has scoring function that saves the state of the scoring to the class so that we can draw it. This will have:
fit
predict
score
draw
Need to think how we'll access the model from ScoreVisualizer
. Best option is to have it be instantiated with a model form - this makes sense because we would call fit down into the estimator. The trick will be that the visual pipeline needs to know not to run fit twice.
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@bbengfort ok, preliminary implementation here: https://github.com/DistrictDataLabs/yellowbrick/blob/develop/yellowbrick/base.py#L104
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ClassifierScoreVisualizers:
RegressionScoreVisualizers:
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@rebeccabilbro ok I took a look at these, and things are looking great! There are a few things that we should resolve/discuss and this issue will be done!
Also - I really like how you've separated score
and draw
and poof
- it was the basis for everything I did in the feature visualizers!
- decide on self.classes or self.classes_ - I guess I was thinking we'd act a bit more like Scikit-Learn; I'm open to other opinions.
- In order to make the isestimator check happen (from the super class), we'll have to call super in
__init__
- We should elevate the model wrapping logic to
ScoreVisualizer
as in #62 - What is the difference between
ScoreVisualizer
andModelVisualizer
?
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@bbengfort have addressed the first two checkboxes in commits d57102c and 76d2d1f
Will tackle the third in the next sprint.
As for the 4th, I've clarified the difference in base.py here. Essentially the main difference is that ScoreVisualizer
takes a fitted model and ModelVisualizer
takes a model form.
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Marking as complete, with the understanding that issue #62 will resolve the fig/ax hook question that came up in the design of this class.
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Related Issues (20)
- Use classification visualizers directly from predictions, targets and logits? HOT 1
- [SilhouetteVisualizer] Constructor argument is_fitted is ignored during initialization HOT 1
- ConfusionMatrix visualizer error with sklearn models HOT 3
- Is there a way to hide the figure from KElbowVisualizer? HOT 3
- Let `KElbowVisualizer` use all the distance metrics supported by sklearn HOT 5
- The PredictionError can't be visualized due to the dim error HOT 2
- Adjusting markersize in `prediction_error` HOT 2
- Matplotlib warning about color usage in Datasaurus
- No figure output of the show method and produce a lot of findfont: Generic family 'sans-serif' not found warnings HOT 2
- Unable to use Silhouette Visualizer with Gaussian Mixture Model HOT 7
- Can't plot class report with trained model HOT 1
- Interactive plots - support plotly backend. HOT 4
- InterclusterDistance AttributeError: 'NoneType' object has no attribute '_get_renderer' HOT 2
- Add arguments to change PCA biplot arrow and arrow label colors and other properties HOT 1
- yellowbrics conflict with matplotlib: use_line_collection in cause!
- Classification report shows classes inverted if passed from sklearn label encoder
- CooksDistance regressor generating error
- Yellowbrick no updated since 1 year ago?! HOT 1
- Conda install fails
- DiscriminationThreshold() for catboost HOT 3
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