update multilabel QUIRE

libact/query_strategies/multilabel/multilabel_quire.py

@@ -104,9 +104,9 @@ def __init__(self, dataset, lamba=1.0, kernel='rbf', gamma=1., coef0=1.,
         # label correlation matrix
         R = np.corrcoef(np.array(lbled_Y).T)
         R = np.nan_to_num(R)
+        self.RK = np.kron(R, self.K)
 
-        self.L = lamba * (np.linalg.pinv(np.kron(R, self.K) \
-                          + lamba * np.eye(n*m)))
+        self.L = lamba * (np.linalg.pinv(self.RK + lamba * np.eye(n*m)))
 
     @inherit_docstring_from(QueryStrategy)
     def make_query(self):
@@ -115,7 +115,7 @@ def make_query(self):
         _, lbled_Y = zip(*dataset.get_labeled_entries())
 
         X = np.array(X)
-        K = self.K
+        RK = self.RK
         n_instance = len(X)
         m = np.shape(lbled_Y)[1]
         lamba = self.lamba
@@ -132,18 +132,41 @@ def make_query(self):
         L = self.L
         vecY = np.reshape(np.array([y for y in Y if y is not None]).T, (-1, 1))
         detLaa = np.linalg.det(L[np.ix_(a_id, a_id)])
-
+        #invLaa = np.linalg.pinv(L[np.ix_(a_id, a_id)])
+        invLaa = (lamba * np.eye(len(a_id)) + RK[np.ix_(a_id, a_id)]) \
+                 - np.dot(np.dot(RK[np.ix_(a_id, l_id)],
+                                 np.linalg.pinv(lamba * np.eye(len(l_id)) \
+                                                + RK[np.ix_(l_id, l_id)])),
+                          RK[np.ix_(l_id, a_id)])
+
+        b = np.zeros((len(a_id)-1))
         score = []
+        D = np.zeros((len(a_id)-1, len(a_id)-1))
+        D[...] = invLaa[1:, 1:]
         for i, s in enumerate(a_id):
+            # L -> s, Laa -> i
             u_id = a_id[:i] + a_id[i+1:]
-            invLuu = L[np.ix_(u_id, u_id)] \
-                     - 1./L[s, s] * np.dot(L[u_id, s], L[u_id, s].T)
+            #D = np.delete(np.delete(invLaa, i, axis=0), i, axis=1)
+            if i > 0:
+                D[(i-1), :i] = invLaa[(i-1), :i]
+                D[(i-1), i:] = invLaa[(i-1), (i+1):]
+                D[:i, (i-1)] = invLaa[:i, (i-1)]
+                D[i:, (i-1)] = invLaa[(i+1):, (i-1)]
+            #D[:i, :i] = invLaa[:i, :i]
+            #D[i:, i:] = invLaa[i+1:, i+1:]
+            #D[:i, i:] = invLaa[:i, i+1:]
+            #D[i:, :i] = invLaa[i+1:, :i]
+
+            #b = np.delete(invLaa, i, axis=0)[:, i]
+            b[:i] = invLaa[:i, i]
+            b[i:] = invLaa[i+1:, i]
+            invLuu = D - 1./invLaa[i, i] * np.dot(b, b.T)
+
             score.append(L[s, s] - detLaa / L[s, s] \
-                         + 2 * np.abs(np.dot(L[np.ix_([s], l_id)] \
+                         + 2 * np.abs(np.dot(L[s, l_id] \
                                       - np.dot(np.dot(L[s, u_id], invLuu),
-                                        L[np.ix_(u_id, l_id)]), vecY))[0][0])
+                                        L[np.ix_(u_id, l_id)]), vecY)))
 
-        import ipdb; ipdb.set_trace()
         score = np.sum(np.array(score).reshape(m, -1).T, axis=1)
 
         ask_idx = self.random_state_.choice(np.where(score == np.min(score))[0])