def categorical_correlations(spn, dictionary):
categoricals = f.get_categoricals(spn)
corr = f.get_full_correlation(spn)
all_combinations = [(i,j) for i,j in itertools.product(range(spn.numFeatures), range(spn.numFeatures)) if i > j and np.abs(corr[i,j]) > correlation_threshold]
if isinstance(feature_combinations, int):
After Change
def categorical_correlations(spn, dictionary):
context = dictionary["context"]
categoricals = context.get_categoricals()
corr = get_full_correlation(spn, context)
num_features = len(spn.scope)
feature_names = context.feature_names
all_combinations = [(i, j) for i, j in
itertools.product(range(num_features),
range(num_features)) if
