pprtf.py 文件源码

def __init__(self, sess, n, filename, jump_prob=0.05, drop_tol=1e-8, verbose=False):
        """
        Computes PPR using LU decomposition.

        Args:
            sess (Session): tensorflow session.
            n (int): Number of nodes.
            filename (str): A csv file denoting the graph.
            jump_prob (float): Jumping probability of PPR.
            drop_tol (float): Drops entries with absolute value lower than this value when computing inverse of LU.
            verbose (bool): Prints step messages if True.
        """
        self.alias = 'ludc'
        self.verbose = verbose
        self.pp("initializing")
        self.sess = sess
        self.n = n
        self.c = jump_prob
        d = 1 - self.c
        t = drop_tol
        exact = False
        if t is None:
            t = np.power(n, -0.5)
        elif t == 0:
            exact = True
        self.pp("reading")
        self.node2index, H = read_matrix(filename, d=-d, add_identity=True)
        self.pp("sorting H")
        self.perm = degree_reverse_rank_perm(H)
        H = reorder_matrix(H, self.perm).tocsc()
        self.pp("computing LU decomposition")
        if exact:
            self.LU = splu(H)
        else:
            self.LU = spilu(H, drop_tol=t)
        Linv = inv(self.LU.L).tocoo()
        Uinv = inv(self.LU.U).tocoo()
        self.pp("tf init")
        with tf.variable_scope('ppr_lu_decomposition_tf'):
            t_Linv = tf.SparseTensorValue(list(zip(Linv.row, Linv.col)), Linv.data, dense_shape=self.LU.L.shape)
            t_Uinv = tf.SparseTensorValue(list(zip(Uinv.row, Uinv.col)), Uinv.data, dense_shape=self.LU.U.shape)
            self.t_q = tf.placeholder(tf.float64, shape=[self.n, 1])
            self.t_r = _sdmm(t_Uinv, _sdmm(t_Linv, self.c * self.t_q))