fm.py 文件源码

def score(self, user, candidates, context):
        # i_mat is (n_item_context, n_item) for all possible items
        # extract only target items
        i_mat = self.i_mat[:, candidates]

        n_target = len(candidates)

        u_vec = user.encode(dim=self.n_user,
                            index=self.use_index,
                            feature=True,
                            vertical=True)
        u_vec = np.concatenate((u_vec, np.array([context]).T))
        u_mat = sp.csr_matrix(np.repeat(u_vec, n_target, axis=1))

        mat = sp.vstack((u_mat, i_mat))

        # Matrix A and B should be dense (numpy array; rather than scipy CSR matrix) because V is dense.
        V = sp.csr_matrix(self.V)
        A = safe_sparse_dot(V.T, mat)
        A.data[:] = A.data ** 2

        sq_mat = mat.copy()
        sq_mat.data[:] = sq_mat.data ** 2
        sq_V = V.copy()
        sq_V.data[:] = sq_V.data ** 2
        B = safe_sparse_dot(sq_V.T, sq_mat)

        interaction = (A - B).sum(axis=0)
        interaction /= 2.  # (1, n_item); numpy matrix form

        pred = self.w0 + safe_sparse_dot(self.w, mat, dense_output=True) + interaction

        return np.abs(1. - np.ravel(pred))