Is there a way to get the sample weights that were used to fit a scikit-learn estimator? Many sklearn estimators support two weighting schemes:


Per-class weights: given when creating the estimator object (e.g. clf = DecisionTreeClassifier(class_weight={'Cat':0.4, 'Dog':0.6})
Per-sample weights: given when fitting a created object (e.g. clf.fit(X, y, sample_weight=[0.1, 0.1, 0.8])


For an already fitted estimator, I can get the per-class weights with clf.get_params()['class_weight']. 

But what's the right way (if any) to get, given a fitted estimator, the per-sample weights that were used?
A quick search of the source code, e.g. RandomForestClassifier's, doesn't find anywhere that sample_weight gets saved as a class attribute. I suspect that was a conscious decision: the sample weights are directly tied to the dataset, which also doesn't get saved for later use; that's why sample_weight appears in the fit method rather than the class instantiation.