Is it possible to get the number of support vectors and (or) their values for an RBF SVC when it is fit using a sklearn Pipeline object? My pipeline looks like this
dim_reduction = TruncatedSVD( n_components = dim_reduction_n_comp, random_state = 611 )
classifier_obj = sklearn.pipeline.Pipeline([
('scaler', sklearn.preprocessing.StandardScaler()),
( 'dim_reduction', dim_reduction ),
( 'svc', sklearn.svm.SVC(C= svc_c, gamma = svc_gamma, probability = True ) )
])
I want to calculate the footprint of the SVC by getting the support vectors and their coefficients and use it as a parameter to optuna optimization study.