def feature_importance(self):
if not self.model:
raise Exception("There are no predictions yet!")
weights = PermutationImportance(self.model).fit(
self.x_test.values, self.y_test.values)
weights = pd.DataFrame({
'Features': list(self.features),
'Importance': weights.feature_importances_
})
weights = weights.reindex(
weights['Importance'].abs().sort_values(ascending=False).index)
# weights = weights[(weights["Importance"] != 0)]
self.plot(weights)
def surrogate(self, white_box):
white_box.fit(self.x_train, self.model.predict(self.x_train))
prediction = white_box.predict(self.x_test)
print('~~~~ Global surrogate ~~~~')
print("Fidelity: ", accuracy_score(self.y_test, prediction))
weights = PermutationImportance(white_box).fit(self.x_test.values,
self.y_test.values)
weights = pd.DataFrame({
'Features': list(self.features),
'Importance': weights.feature_importances_
})
weights = weights.reindex(
weights['Importance'].abs().sort_values(ascending=False).index)
weights = weights[(weights["Importance"] != 0)]
self.plot(weights)
