#150 Return SHAP values with ShapRFECV fit_compute call #171
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Return SHAP values with ShapRFECV fit_compute call. Add new method to get shap values of a given reduced features set. Update tutorial notebook.
eduardo-lourenco
changed the title
| @@ -673,6 +685,37 @@ def get_reduced_features_set(self, num_features): | |||
| else: | |||
| return self.report_df[self.report_df.num_features == num_features]["features_set"].values[0] | |||
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| def get_reduced_features_set_shap_values(self, num_features, abs_shap_values=True): | |||
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Maybe good to rename it to get_feature_importance?
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Some small unit test using very very simple handcrafted dataset, to get these shap values would be nice.
| @@ -673,6 +685,37 @@ def get_reduced_features_set(self, num_features): | |||
| else: | |||
| return self.report_df[self.report_df.num_features == num_features]["features_set"].values[0] | |||
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| def get_reduced_features_set_shap_values(self, num_features, abs_shap_values=True): | |||
| """ | |||
| Gets the shap values of a features set after the feature elimination process, for a given number of features. | |||
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Get feature importance (mean abs shap values)
| "execution_count": 6, | ||
| "source": [ | ||
| "#First 5 rows of first 5 columns\n", | ||
| "report[['num_features', 'features_set', 'val_metric_mean']]" |
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Here you can also add the mean abs shap values.
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As i am thinking about this now, let's keep this part as it is, but somewhere later in the notebook, add a cell showing feature importances for the final feature set :)
| @@ -292,6 +292,8 @@ def _report_current_results( | |||
| train_metric_std, | |||
| val_metric_mean, | |||
| val_metric_std, | |||
| features_mean_abs_shap_value, | |||
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Is this a dictionary or a list?
If a list, please make sure that this corresponds well to the current feature list. the feature set might be sorted based on the shap values. Something to double check :)
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Also please have a look what has changed in, just to make sure your PR is aligned #175 |
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@eduardo-lourenco Are you going to still work on this one PR? |
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hi @eduardo-lourenco thanks for your contributions, could you update your PR to be made compatible with the latest changes on main? |
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This PR is closed due to inactivity. Please reach out if you want to continue with this PR. |
This PR targets issue #150 and comprises the following changes:
I will update the related tests if this solution seems appropriate (current tests are still passing, we just need to add new tests).