While this would benefit from #446 it could be done with a bit of hacky logic as an MVP
GWT
Given I am a user that has a trained random forest model
When I call the method trained_rf.plot_feature_importances()
Then I should see the feature importance plot shown during training.
Given I am a user that has a trained model of any other type
When I call the method trained_foo.plot_feature_importances()
Then I should see a warning or error message saying that this is only for random forest models
Notes
The current implentation of plot_rf_features_from_tsm() requires the x_train data. This isn't something we want to store in a serialized model for PHI, data security and performance reasons. Therefore, this will require some thought and investigation.
Some possible solutions:
Generate the plot and serialize it somehow during training as part of the TrainedSupervisedModel
While this would benefit from #446 it could be done with a bit of hacky logic as an MVP
GWT
Given I am a user that has a trained random forest model
When I call the method
trained_rf.plot_feature_importances()Then I should see the feature importance plot shown during training.
Given I am a user that has a trained model of any other type
When I call the method
trained_foo.plot_feature_importances()Then I should see a warning or error message saying that this is only for random forest models
Notes
plot_rf_features_from_tsm()requires the x_train data. This isn't something we want to store in a serialized model for PHI, data security and performance reasons. Therefore, this will require some thought and investigation.TrainedSupervisedModel