revert to orig

backend/disentangle_concepts.py

@@ -35,7 +35,7 @@ def get_separation_space(type_bin, annotations, df, samples=100, method='LR', C=
         print('Val performance SVM', svc.score(x_val, y_val))
         imp_features = (np.abs(svc.coef_) > 0.2).sum()
         imp_nodes = np.where(np.abs(svc.coef_) > 0.2)[1]
-        return svc.coef_, imp_features, imp_nodes, np.round(clf.score(x_val, y_val),2)
+        return svc.coef_ / np.linalg.norm(clf.coef_), imp_features, imp_nodes, np.round(clf.score(x_val, y_val),2)
     elif method == 'LR':
         clf = LogisticRegression(random_state=0, C=C)
         clf.fit(x_train, y_train)
@@ -145,6 +145,6 @@ def get_concepts_vectors(concepts, annotations, df, samples=100, method='LR', C=
     #            )
 
     # projection = reducer.fit_transform(vectors)
-    nodes_in_common = imp_nodes.intersection(*important_nodes)
+    nodes_in_common = set.intersection(*important_nodes)
     return vectors, nodes_in_common, performances