標籤傳播演算法（Label Propagation）及 Python 實現

發表於2017-08-31

眾所周知，機器學習可以大體分為三大類：監督學習、非監督學習和半監督學習。監督學習可以認為是我們有非常多的labeled標註資料來train一個模型，期待這個模型能學習到資料的分佈，以期對未來沒有見到的樣本做預測。那這個效能的源頭–訓練資料，就顯得非常感覺。你必須有足夠的訓練資料，以覆蓋真正現實資料中的樣本分佈才可以，這樣學習到的模型才有意義。那非監督學習就是沒有任何的labeled資料，就是平時所說的聚類了，利用他們本身的資料分佈，給他們劃分類別。而半監督學習，顧名思義就是處於兩者之間的，只有少量的labeled資料，我們試圖從這少量的labeled資料和大量的unlabeled資料中學習到有用的資訊。

一、半監督學習

半監督學習（Semi-supervised learning）發揮作用的場合是：你的資料有一些有label，一些沒有。而且一般是絕大部分都沒有，只有少許幾個有label。半監督學習演算法會充分的利用unlabeled資料來捕捉我們整個資料的潛在分佈。它基於三大假設：

Smoothness平滑假設：相似的資料具有相同的label。
Cluster聚類假設：處於同一個聚類下的資料具有相同label。
Manifold流形假設：處於同一流形結構下的資料具有相同label。

例如下圖，只有兩個labeled資料，如果直接用他們來訓練一個分類器，例如LR或者SVM，那麼學出來的分類面就是左圖那樣的。如果現實中，這個資料是右圖那邊分佈的話，豬都看得出來，左圖訓練的這個分類器爛的一塌糊塗、慘不忍睹。因為我們的labeled訓練資料太少了，都沒辦法覆蓋我們未來可能遇到的情況。但是，如果右圖那樣，把大量的unlabeled資料（黑色的）都考慮進來，有個全域性觀念，牛逼的演算法會發現，哎喲，原來是兩個圈圈（分別處於兩個圓形的流形之上）！那演算法就很聰明，把大圈的資料都歸類為紅色類別，把內圈的資料都歸類為藍色類別。因為，實踐中，labeled資料是昂貴，很難獲得的，但unlabeled資料就不是了，寫個指令碼在網上爬就可以了，因此如果能充分利用大量的unlabeled資料來輔助提升我們的模型學習，這個價值就非常大。

半監督學習演算法有很多，下面我們介紹最簡單的標籤傳播演算法（label propagation），最喜歡簡單了，哈哈。

二、標籤傳播演算法

標籤傳播演算法（label propagation）的核心思想非常簡單：相似的資料應該具有相同的label。LP演算法包括兩大步驟：1）構造相似矩陣；2）勇敢的傳播吧。

2.1、相似矩陣構建

LP演算法是基於Graph的，因此我們需要先構建一個圖。我們為所有的資料構建一個圖，圖的節點就是一個資料點，包含labeled和unlabeled的資料。節點i和節點j的邊表示他們的相似度。這個圖的構建方法有很多，這裡我們假設這個圖是全連線的，節點i和節點j的邊權重為：

這裡，α是超參。

還有個非常常用的圖構建方法是knn圖，也就是隻保留每個節點的k近鄰權重，其他的為0，也就是不存在邊，因此是稀疏的相似矩陣。

2.2、LP演算法

標籤傳播演算法非常簡單：通過節點之間的邊傳播label。邊的權重越大，表示兩個節點越相似，那麼label越容易傳播過去。我們定義一個NxN的概率轉移矩陣P：

P_ij表示從節點i轉移到節點j的概率。假設有C個類和L個labeled樣本，我們定義一個LxC的label矩陣Y_L，第i行表示第i個樣本的標籤指示向量，即如果第i個樣本的類別是j，那麼該行的第j個元素為1，其他為0。同樣，我們也給U個unlabeled樣本一個UxC的label矩陣Y_U。把他們合併，我們得到一個NxC的soft label矩陣F=[Y_L;Y_U]。soft label的意思是，我們保留樣本i屬於每個類別的概率，而不是互斥性的，這個樣本以概率1只屬於一個類。當然了，最後確定這個樣本i的類別的時候，是取max也就是概率最大的那個類作為它的類別的。那F裡面有個Y_U，它一開始是不知道的，那最開始的值是多少？無所謂，隨便設定一個值就可以了。

千呼萬喚始出來，簡單的LP演算法如下：

執行傳播：F=PF
重置F中labeled樣本的標籤：F_L=Y_L
重複步驟1）和2）直到F收斂。

步驟1）就是將矩陣P和矩陣F相乘，這一步，每個節點都將自己的label以P確定的概率傳播給其他節點。如果兩個節點越相似（在歐式空間中距離越近），那麼對方的label就越容易被自己的label賦予，就是更容易拉幫結派。步驟2）非常關鍵，因為labeled資料的label是事先確定的，它不能被帶跑，所以每次傳播完，它都得迴歸它本來的label。隨著labeled資料不斷的將自己的label傳播出去，最後的類邊界會穿越高密度區域，而停留在低密度的間隔中。相當於每個不同類別的labeled樣本劃分了勢力範圍。

2.3、變身的LP演算法

我們知道，我們每次迭代都是計算一個soft label矩陣F=[Y_L;Y_U]，但是Y_L是已知的，計算它沒有什麼用，在步驟2）的時候，還得把它弄回來。我們關心的只是Y_U，那我們能不能只計算Y_U呢？Yes。我們將矩陣P做以下劃分：

這時候，我們的演算法就一個運算：

迭代上面這個步驟直到收斂就ok了，是不是很cool。可以看到F_U不但取決於labeled資料的標籤及其轉移概率，還取決了unlabeled資料的當前label和轉移概率。因此LP演算法能額外運用unlabeled資料的分佈特點。

這個演算法的收斂性也非常容易證明，具體見參考文獻[1]。實際上，它是可以收斂到一個凸解的：

所以我們也可以直接這樣求解，以獲得最終的Y_U。但是在實際的應用過程中，由於矩陣求逆需要O(n³)的複雜度，所以如果unlabeled資料非常多，那麼I – P_UU矩陣的求逆將會非常耗時，因此這時候一般選擇迭代演算法來實現。

三、LP演算法的Python實現

Python環境的搭建就不囉嗦了，可以參考前面的部落格。需要額外依賴的庫是經典的numpy和matplotlib。程式碼中包含了兩種圖的構建方法：RBF和KNN指定。同時，自己生成了兩個toy資料庫：兩條長形形狀和兩個圈圈的資料。第四部分我們用大點的資料庫來做實驗，先簡單的視覺化驗證程式碼的正確性，再前線。

演算法程式碼：

#***************************************************************************  
#*   
#* Description: label propagation  
#* Author: Zou Xiaoyi (zouxy09@qq.com)  
#* Date:   2015-10-15  
#* HomePage: http://blog.csdn.net/zouxy09  
#*   
#**************************************************************************  
  
import time  
import numpy as np  
  
# return k neighbors index  
def navie_knn(dataSet, query, k):  
    numSamples = dataSet.shape[0]  
  
    ## step 1: calculate Euclidean distance  
    diff = np.tile(query, (numSamples, 1)) - dataSet  
    squaredDiff = diff ** 2  
    squaredDist = np.sum(squaredDiff, axis = 1) # sum is performed by row  
  
    ## step 2: sort the distance  
    sortedDistIndices = np.argsort(squaredDist)  
    if k > len(sortedDistIndices):  
        k = len(sortedDistIndices)  
  
    return sortedDistIndices[0:k]  
  
  
# build a big graph (normalized weight matrix)  
def buildGraph(MatX, kernel_type, rbf_sigma = None, knn_num_neighbors = None):  
    num_samples = MatX.shape[0]  
    affinity_matrix = np.zeros((num_samples, num_samples), np.float32)  
    if kernel_type == 'rbf':  
        if rbf_sigma == None:  
            raise ValueError('You should input a sigma of rbf kernel!')  
        for i in xrange(num_samples):  
            row_sum = 0.0  
            for j in xrange(num_samples):  
                diff = MatX[i, :] - MatX[j, :]  
                affinity_matrix[i][j] = np.exp(sum(diff**2) / (-2.0 * rbf_sigma**2))  
                row_sum += affinity_matrix[i][j]  
            affinity_matrix[i][:] /= row_sum  
    elif kernel_type == 'knn':  
        if knn_num_neighbors == None:  
            raise ValueError('You should input a k of knn kernel!')  
        for i in xrange(num_samples):  
            k_neighbors = navie_knn(MatX, MatX[i, :], knn_num_neighbors)  
            affinity_matrix[i][k_neighbors] = 1.0 / knn_num_neighbors  
    else:  
        raise NameError('Not support kernel type! You can use knn or rbf!')  
      
    return affinity_matrix  
  
  
# label propagation  
def labelPropagation(Mat_Label, Mat_Unlabel, labels, kernel_type = 'rbf', rbf_sigma = 1.5, \  
                    knn_num_neighbors = 10, max_iter = 500, tol = 1e-3):  
    # initialize  
    num_label_samples = Mat_Label.shape[0]  
    num_unlabel_samples = Mat_Unlabel.shape[0]  
    num_samples = num_label_samples + num_unlabel_samples  
    labels_list = np.unique(labels)  
    num_classes = len(labels_list)  
      
    MatX = np.vstack((Mat_Label, Mat_Unlabel))  
    clamp_data_label = np.zeros((num_label_samples, num_classes), np.float32)  
    for i in xrange(num_label_samples):  
        clamp_data_label[i][labels[i]] = 1.0  
      
    label_function = np.zeros((num_samples, num_classes), np.float32)  
    label_function[0 : num_label_samples] = clamp_data_label  
    label_function[num_label_samples : num_samples] = -1  
      
    # graph construction  
    affinity_matrix = buildGraph(MatX, kernel_type, rbf_sigma, knn_num_neighbors)  
      
    # start to propagation  
    iter = 0; pre_label_function = np.zeros((num_samples, num_classes), np.float32)  
    changed = np.abs(pre_label_function - label_function).sum()  
    while iter < max_iter and changed > tol:  
        if iter % 1 == 0:  
            print "---> Iteration %d/%d, changed: %f" % (iter, max_iter, changed)  
        pre_label_function = label_function  
        iter += 1  
          
        # propagation  
        label_function = np.dot(affinity_matrix, label_function)  
          
        # clamp  
        label_function[0 : num_label_samples] = clamp_data_label  
          
        # check converge  
        changed = np.abs(pre_label_function - label_function).sum()  
      
    # get terminate label of unlabeled data  
    unlabel_data_labels = np.zeros(num_unlabel_samples)  
    for i in xrange(num_unlabel_samples):  
        unlabel_data_labels[i] = np.argmax(label_function[i+num_label_samples])  
      
    return unlabel_data_labels

100

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#***************************************************************************

#* Description: label propagation

#* Author: Zou Xiaoyi (zouxy09@qq.com)

#* Date: 2015-10-15

#* HomePage: http://blog.csdn.net/zouxy09

#**************************************************************************

import time

import numpy as np

# return k neighbors index

def navie_knn(dataSet, query, k):

numSamples = dataSet.shape[0]

## step 1: calculate Euclidean distance

diff = np.tile(query, (numSamples, 1)) - dataSet

squaredDiff = diff ** 2

squaredDist = np.sum(squaredDiff, axis = 1) # sum is performed by row

## step 2: sort the distance

sortedDistIndices = np.argsort(squaredDist)

if k > len(sortedDistIndices):

k = len(sortedDistIndices)

return sortedDistIndices[0:k]

# build a big graph (normalized weight matrix)

def buildGraph(MatX, kernel_type, rbf_sigma = None, knn_num_neighbors = None):

num_samples = MatX.shape[0]

affinity_matrix = np.zeros((num_samples, num_samples), np.float32)

if kernel_type == 'rbf':

if rbf_sigma == None:

raise ValueError('You should input a sigma of rbf kernel!')

for i in xrange(num_samples):

row_sum = 0.0

for j in xrange(num_samples):

diff = MatX[i, :] - MatX[j, :]

affinity_matrix[i][j] = np.exp(sum(diff**2) / (-2.0 * rbf_sigma**2))

row_sum += affinity_matrix[i][j]

affinity_matrix[i][:] /= row_sum

elif kernel_type == 'knn':

if knn_num_neighbors == None:

raise ValueError('You should input a k of knn kernel!')

for i in xrange(num_samples):

k_neighbors = navie_knn(MatX, MatX[i, :], knn_num_neighbors)

affinity_matrix[i][k_neighbors] = 1.0 / knn_num_neighbors

else:

raise NameError('Not support kernel type! You can use knn or rbf!')

return affinity_matrix

# label propagation

def labelPropagation(Mat_Label, Mat_Unlabel, labels, kernel_type = 'rbf', rbf_sigma = 1.5, \

knn_num_neighbors = 10, max_iter = 500, tol = 1e-3):

# initialize

num_label_samples = Mat_Label.shape[0]

num_unlabel_samples = Mat_Unlabel.shape[0]

num_samples = num_label_samples + num_unlabel_samples

labels_list = np.unique(labels)

num_classes = len(labels_list)

MatX = np.vstack((Mat_Label, Mat_Unlabel))

clamp_data_label = np.zeros((num_label_samples, num_classes), np.float32)

for i in xrange(num_label_samples):

clamp_data_label[i][labels[i]] = 1.0

label_function = np.zeros((num_samples, num_classes), np.float32)

label_function[0 : num_label_samples] = clamp_data_label

label_function[num_label_samples : num_samples] = -1

# graph construction

affinity_matrix = buildGraph(MatX, kernel_type, rbf_sigma, knn_num_neighbors)

# start to propagation

iter = 0; pre_label_function = np.zeros((num_samples, num_classes), np.float32)

changed = np.abs(pre_label_function - label_function).sum()

while iter < max_iter and changed > tol:

if iter % 1 == 0:

print "---> Iteration %d/%d, changed: %f" % (iter, max_iter, changed)

pre_label_function = label_function

iter += 1

# propagation

label_function = np.dot(affinity_matrix, label_function)

# clamp

label_function[0 : num_label_samples] = clamp_data_label

# check converge

changed = np.abs(pre_label_function - label_function).sum()

# get terminate label of unlabeled data

unlabel_data_labels = np.zeros(num_unlabel_samples)

for i in xrange(num_unlabel_samples):

unlabel_data_labels[i] = np.argmax(label_function[i+num_label_samples])

return unlabel_data_labels

測試程式碼：

#***************************************************************************  
#*   
#* Description: label propagation  
#* Author: Zou Xiaoyi (zouxy09@qq.com)  
#* Date:   2015-10-15  
#* HomePage: http://blog.csdn.net/zouxy09  
#*   
#**************************************************************************  
  
import time  
import math  
import numpy as np  
from label_propagation import labelPropagation  
  
# show  
def show(Mat_Label, labels, Mat_Unlabel, unlabel_data_labels):   
    import matplotlib.pyplot as plt   
      
    for i in range(Mat_Label.shape[0]):  
        if int(labels[i]) == 0:    
            plt.plot(Mat_Label[i, 0], Mat_Label[i, 1], 'Dr')    
        elif int(labels[i]) == 1:    
            plt.plot(Mat_Label[i, 0], Mat_Label[i, 1], 'Db')  
        else:  
            plt.plot(Mat_Label[i, 0], Mat_Label[i, 1], 'Dy')  
      
    for i in range(Mat_Unlabel.shape[0]):  
        if int(unlabel_data_labels[i]) == 0:    
            plt.plot(Mat_Unlabel[i, 0], Mat_Unlabel[i, 1], 'or')    
        elif int(unlabel_data_labels[i]) == 1:    
            plt.plot(Mat_Unlabel[i, 0], Mat_Unlabel[i, 1], 'ob')  
        else:  
            plt.plot(Mat_Unlabel[i, 0], Mat_Unlabel[i, 1], 'oy')  
      
    plt.xlabel('X1'); plt.ylabel('X2')   
    plt.xlim(0.0, 12.)  
    plt.ylim(0.0, 12.)  
    plt.show()    
  
  
def loadCircleData(num_data):  
    center = np.array([5.0, 5.0])  
    radiu_inner = 2  
    radiu_outer = 4  
    num_inner = num_data / 3  
    num_outer = num_data - num_inner  
      
    data = []  
    theta = 0.0  
    for i in range(num_inner):  
        pho = (theta % 360) * math.pi / 180  
        tmp = np.zeros(2, np.float32)  
        tmp[0] = radiu_inner * math.cos(pho) + np.random.rand(1) + center[0]  
        tmp[1] = radiu_inner * math.sin(pho) + np.random.rand(1) + center[1]  
        data.append(tmp)  
        theta += 2  
      
    theta = 0.0  
    for i in range(num_outer):  
        pho = (theta % 360) * math.pi / 180  
        tmp = np.zeros(2, np.float32)  
        tmp[0] = radiu_outer * math.cos(pho) + np.random.rand(1) + center[0]  
        tmp[1] = radiu_outer * math.sin(pho) + np.random.rand(1) + center[1]  
        data.append(tmp)  
        theta += 1  
      
    Mat_Label = np.zeros((2, 2), np.float32)  
    Mat_Label[0] = center + np.array([-radiu_inner + 0.5, 0])  
    Mat_Label[1] = center + np.array([-radiu_outer + 0.5, 0])  
    labels = [0, 1]  
    Mat_Unlabel = np.vstack(data)  
    return Mat_Label, labels, Mat_Unlabel  
  
  
def loadBandData(num_unlabel_samples):  
    #Mat_Label = np.array([[5.0, 2.], [5.0, 8.0]])  
    #labels = [0, 1]  
    #Mat_Unlabel = np.array([[5.1, 2.], [5.0, 8.1]])  
      
    Mat_Label = np.array([[5.0, 2.], [5.0, 8.0]])  
    labels = [0, 1]  
    num_dim = Mat_Label.shape[1]  
    Mat_Unlabel = np.zeros((num_unlabel_samples, num_dim), np.float32)  
    Mat_Unlabel[:num_unlabel_samples/2, :] = (np.random.rand(num_unlabel_samples/2, num_dim) - 0.5) * np.array([3, 1]) + Mat_Label[0]  
    Mat_Unlabel[num_unlabel_samples/2 : num_unlabel_samples, :] = (np.random.rand(num_unlabel_samples/2, num_dim) - 0.5) * np.array([3, 1]) + Mat_Label[1]  
    return Mat_Label, labels, Mat_Unlabel  
  
  
# main function  
if __name__ == "__main__":  
    num_unlabel_samples = 800  
    #Mat_Label, labels, Mat_Unlabel = loadBandData(num_unlabel_samples)  
    Mat_Label, labels, Mat_Unlabel = loadCircleData(num_unlabel_samples)  
      
    ## Notice: when use 'rbf' as our kernel, the choice of hyper parameter 'sigma' is very import! It should be  
    ## chose according to your dataset, specific the distance of two data points. I think it should ensure that  
    ## each point has about 10 knn or w_i,j is large enough. It also influence the speed of converge. So, may be  
    ## 'knn' kernel is better!  
    #unlabel_data_labels = labelPropagation(Mat_Label, Mat_Unlabel, labels, kernel_type = 'rbf', rbf_sigma = 0.2)  
    unlabel_data_labels = labelPropagation(Mat_Label, Mat_Unlabel, labels, kernel_type = 'knn', knn_num_neighbors = 10, max_iter = 400)  
    show(Mat_Label, labels, Mat_Unlabel, unlabel_data_labels)

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#***************************************************************************

#* Description: label propagation

#* Author: Zou Xiaoyi (zouxy09@qq.com)

#* Date: 2015-10-15

#* HomePage: http://blog.csdn.net/zouxy09

#**************************************************************************

import time

import math

import numpy as np

from label_propagation import labelPropagation

# show

def show(Mat_Label, labels, Mat_Unlabel, unlabel_data_labels):

import matplotlib.pyplot as plt

for i in range(Mat_Label.shape[0]):

if int(labels[i]) == 0:

plt.plot(Mat_Label[i, 0], Mat_Label[i, 1], 'Dr')

elif int(labels[i]) == 1:

plt.plot(Mat_Label[i, 0], Mat_Label[i, 1], 'Db')

else:

plt.plot(Mat_Label[i, 0], Mat_Label[i, 1], 'Dy')

for i in range(Mat_Unlabel.shape[0]):

if int(unlabel_data_labels[i]) == 0:

plt.plot(Mat_Unlabel[i, 0], Mat_Unlabel[i, 1], 'or')

elif int(unlabel_data_labels[i]) == 1:

plt.plot(Mat_Unlabel[i, 0], Mat_Unlabel[i, 1], 'ob')

else:

plt.plot(Mat_Unlabel[i, 0], Mat_Unlabel[i, 1], 'oy')

plt.xlabel('X1'); plt.ylabel('X2')

plt.xlim(0.0, 12.)

plt.ylim(0.0, 12.)

plt.show()

def loadCircleData(num_data):

center = np.array([5.0, 5.0])

radiu_inner = 2

radiu_outer = 4

num_inner = num_data / 3

num_outer = num_data - num_inner

data = []

theta = 0.0

for i in range(num_inner):

pho = (theta % 360) * math.pi / 180

tmp = np.zeros(2, np.float32)

tmp[0] = radiu_inner * math.cos(pho) + np.random.rand(1) + center[0]

tmp[1] = radiu_inner * math.sin(pho) + np.random.rand(1) + center[1]

data.append(tmp)

theta += 2

theta = 0.0

for i in range(num_outer):

pho = (theta % 360) * math.pi / 180

tmp = np.zeros(2, np.float32)

tmp[0] = radiu_outer * math.cos(pho) + np.random.rand(1) + center[0]

tmp[1] = radiu_outer * math.sin(pho) + np.random.rand(1) + center[1]

data.append(tmp)

theta += 1

Mat_Label = np.zeros((2, 2), np.float32)

Mat_Label[0] = center + np.array([-radiu_inner + 0.5, 0])

Mat_Label[1] = center + np.array([-radiu_outer + 0.5, 0])

labels = [0, 1]

Mat_Unlabel = np.vstack(data)

return Mat_Label, labels, Mat_Unlabel

def loadBandData(num_unlabel_samples):

#Mat_Label = np.array([[5.0, 2.], [5.0, 8.0]])

#labels = [0, 1]

#Mat_Unlabel = np.array([[5.1, 2.], [5.0, 8.1]])

Mat_Label = np.array([[5.0, 2.], [5.0, 8.0]])

labels = [0, 1]

num_dim = Mat_Label.shape[1]

Mat_Unlabel = np.zeros((num_unlabel_samples, num_dim), np.float32)

Mat_Unlabel[:num_unlabel_samples/2, :] = (np.random.rand(num_unlabel_samples/2, num_dim) - 0.5) * np.array([3, 1]) + Mat_Label[0]

Mat_Unlabel[num_unlabel_samples/2 : num_unlabel_samples, :] = (np.random.rand(num_unlabel_samples/2, num_dim) - 0.5) * np.array([3, 1]) + Mat_Label[1]

return Mat_Label, labels, Mat_Unlabel

# main function

if __name__ == "__main__":

num_unlabel_samples = 800

#Mat_Label, labels, Mat_Unlabel = loadBandData(num_unlabel_samples)

Mat_Label, labels, Mat_Unlabel = loadCircleData(num_unlabel_samples)

## Notice: when use 'rbf' as our kernel, the choice of hyper parameter 'sigma' is very import! It should be

## chose according to your dataset, specific the distance of two data points. I think it should ensure that

## each point has about 10 knn or w_i,j is large enough. It also influence the speed of converge. So, may be

## 'knn' kernel is better!

#unlabel_data_labels = labelPropagation(Mat_Label, Mat_Unlabel, labels, kernel_type = 'rbf', rbf_sigma = 0.2)

unlabel_data_labels = labelPropagation(Mat_Label, Mat_Unlabel, labels, kernel_type = 'knn', knn_num_neighbors = 10, max_iter = 400)

show(Mat_Label, labels, Mat_Unlabel, unlabel_data_labels)

該註釋的，程式碼都註釋的，有看不明白的，歡迎交流。不同迭代次數時候的結果如下：

是不是很漂亮的傳播過程？！在數值上也是可以看到隨著迭代的進行逐漸收斂的，迭代的數值變化過程如下：

---> Iteration 0/400, changed: 1602.000000  
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---> Iteration 0/400, changed: 1602.000000

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四、LP演算法MPI並行實現

這裡，我們測試的是LP的變身版本。從公式，我們可以看到，第二項P_ULY_L迭代過程並沒有發生變化，所以這部分實際上從迭代開始就可以計算好，從而避免重複計算。不過，不管怎樣，LP演算法都要計算一個UxU的矩陣P_UU和一個UxC矩陣F_U的乘積。當我們的unlabeled資料非常多，而且類別也很多的時候，計算是很慢的，同時佔用的記憶體量也非常大。另外，構造Graph需要計算兩兩的相似度，也是O(n²)的複雜度，當我們資料的特徵維度很大的時候，這個計算量也是非常客觀的。所以我們就得考慮並行處理了。而且最好是能放到叢集上並行。那如何並行呢？

對演算法的並行化，一般分為兩種：資料並行和模型並行。

資料並行很好理解，就是將資料劃分，每個節點只處理一部分資料，例如我們構造圖的時候，計算每個資料的k近鄰。例如我們有1000個樣本和20個CPU節點，那麼就平均分發，讓每個CPU節點計算50個樣本的k近鄰，然後最後再合併大家的結果。可見這個加速比也是非常可觀的。

模型並行一般發生在模型很大，無法放到單機的記憶體裡面的時候。例如龐大的深度神經網路訓練的時候，就需要把這個網路切開，然後分別求解梯度，最後有個leader的節點來收集大家的梯度，再反饋給大家去更新。當然了，其中存在更細緻和高效的工程處理方法。在我們的LP演算法中，也是可以做模型並行的。假如我們的類別數C很大，把類別數切開，讓不同的CPU節點處理，實際上就相當於模型並行了。

那為啥不切大矩陣P_UU，而是切小點的矩陣F_U，因為大矩陣P_UU沒法獨立分塊，並行的一個原則是處理必須是獨立的。矩陣F_U依賴的是所有的U，而把P_UU切開分發到其他節點的時候，每次F_U的更新都需要和其他的節點通訊，這個通訊的代價是很大的（實際上，很多並行系統沒法達到線性的加速度的瓶頸是通訊！線性加速比是，我增加了n臺機器，速度就提升了n倍）。但是對類別C也就是矩陣F_U切分，就不會有這個問題，因為他們的計算是獨立的。只是決定樣本的最終類別的時候，將所有的F_U收集回來求max就可以了。

所以，在下面的程式碼中，是同時包含了資料並行和模型並行的雛形的。另外，還值得一提的是，我們是迭代演算法，那決定什麼時候迭代演算法停止？除了判斷收斂外，我們還可以讓每迭代幾步，就用測試label測試一次結果，看模型的整體訓練效能如何。特別是判斷訓練是否過擬合的時候非常有效。因此，程式碼中包含了這部分內容。

好了，程式碼終於來了。大家可以搞點大資料庫來測試，如果有MPI叢集條件的話就更好了。

下面的程式碼依賴numpy、scipy（用其稀疏矩陣加速計算）和mpi4py。其中mpi4py需要依賴openmpi和Cpython，可以參考我之前的部落格進行安裝。

#***************************************************************************  
#*   
#* Description: label propagation  
#* Author: Zou Xiaoyi (zouxy09@qq.com)  
#* Date:   2015-10-15  
#* HomePage: http://blog.csdn.net/zouxy09  
#*   
#**************************************************************************  
  
import os, sys, time  
import numpy as np  
from scipy.sparse import csr_matrix, lil_matrix, eye  
import operator  
import cPickle as pickle  
import mpi4py.MPI as MPI  
  
#  
#   Global variables for MPI  
#  
  
# instance for invoking MPI related functions  
comm = MPI.COMM_WORLD  
# the node rank in the whole community  
comm_rank = comm.Get_rank()  
# the size of the whole community, i.e., the total number of working nodes in the MPI cluster  
comm_size = comm.Get_size()  
  
# load mnist dataset  
def load_MNIST():  
    import gzip  
    f = gzip.open("mnist.pkl.gz", "rb")  
    train, val, test = pickle.load(f)  
    f.close()  
      
    Mat_Label = train[0]  
    labels = train[1]  
    Mat_Unlabel = test[0]  
    groundtruth = test[1]  
    labels_id = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]  
  
    return Mat_Label, labels, labels_id, Mat_Unlabel, groundtruth  
  
# return k neighbors index  
def navie_knn(dataSet, query, k):  
    numSamples = dataSet.shape[0]  
  
    ## step 1: calculate Euclidean distance  
    diff = np.tile(query, (numSamples, 1)) - dataSet  
    squaredDiff = diff ** 2  
    squaredDist = np.sum(squaredDiff, axis = 1) # sum is performed by row  
  
    ## step 2: sort the distance  
    sortedDistIndices = np.argsort(squaredDist)  
    if k > len(sortedDistIndices):  
        k = len(sortedDistIndices)  
    return sortedDistIndices[0:k]  
  
  
# build a big graph (normalized weight matrix)  
# sparse U x (U + L) matrix  
def buildSubGraph(Mat_Label, Mat_Unlabel, knn_num_neighbors):  
    num_unlabel_samples = Mat_Unlabel.shape[0]  
    data = []; indices = []; indptr = [0]  
    Mat_all = np.vstack((Mat_Label, Mat_Unlabel))  
    values = np.ones(knn_num_neighbors, np.float32) / knn_num_neighbors  
    for i in xrange(num_unlabel_samples):  
        k_neighbors = navie_knn(Mat_all, Mat_Unlabel[i, :], knn_num_neighbors)  
        indptr.append(np.int32(indptr[-1]) + knn_num_neighbors)  
        indices.extend(k_neighbors)  
        data.append(values)   
    return csr_matrix((np.hstack(data), indices, indptr))  
  
  
# build a big graph (normalized weight matrix)  
# sparse U x (U + L) matrix  
def buildSubGraph_MPI(Mat_Label, Mat_Unlabel, knn_num_neighbors):  
    num_unlabel_samples = Mat_Unlabel.shape[0]  
    local_data = []; local_indices = []; local_indptr = [0]  
    Mat_all = np.vstack((Mat_Label, Mat_Unlabel))  
    values = np.ones(knn_num_neighbors, np.float32) / knn_num_neighbors  
    sample_offset = np.linspace(0, num_unlabel_samples, comm_size + 1).astype('int')  
    for i in range(sample_offset[comm_rank], sample_offset[comm_rank+1]):  
        k_neighbors = navie_knn(Mat_all, Mat_Unlabel[i, :], knn_num_neighbors)  
        local_indptr.append(np.int32(local_indptr[-1]) + knn_num_neighbors)  
        local_indices.extend(k_neighbors)  
        local_data.append(values)  
    data = np.hstack(comm.allgather(local_data))  
    indices = np.hstack(comm.allgather(local_indices))  
    indptr_tmp = comm.allgather(local_indptr)  
    indptr = []  
    for i in range(len(indptr_tmp)):  
        if i == 0:  
            indptr.extend(indptr_tmp[i])  
        else:  
            last_indptr = indptr[-1]  
            del(indptr[-1])  
            indptr.extend(indptr_tmp[i] + last_indptr)  
    return csr_matrix((np.hstack(data), indices, indptr), dtype = np.float32)  
  
  
# label propagation  
def run_label_propagation_sparse(knn_num_neighbors = 20, max_iter = 100, tol = 1e-4, test_per_iter = 1):  
    # load data and graph  
    print "Processor %d/%d loading graph file..." % (comm_rank, comm_size)  
    #Mat_Label, labels, Mat_Unlabel, groundtruth = loadFourBandData()  
    Mat_Label, labels, labels_id, Mat_Unlabel, unlabel_data_id = load_MNIST()  
    if comm_size > len(labels_id):  
        raise ValueError("Sorry, the processors must be less than the number of classes")  
    #affinity_matrix = buildSubGraph(Mat_Label, Mat_Unlabel, knn_num_neighbors)  
    affinity_matrix = buildSubGraph_MPI(Mat_Label, Mat_Unlabel, knn_num_neighbors)  
      
    # get some parameters  
    num_classes = len(labels_id)  
    num_label_samples = len(labels)  
    num_unlabel_samples = Mat_Unlabel.shape[0]  
  
    affinity_matrix_UL = affinity_matrix[:, 0:num_label_samples]  
    affinity_matrix_UU = affinity_matrix[:, num_label_samples:num_label_samples+num_unlabel_samples]  
  
    if comm_rank == 0:  
        print "Have %d labeled images, %d unlabeled images and %d classes" % (num_label_samples, num_unlabel_samples, num_classes)  
      
    # divide label_function_U and label_function_L to all processors  
    class_offset = np.linspace(0, num_classes, comm_size + 1).astype('int')  
      
    # initialize local label_function_U  
    local_start_class = class_offset[comm_rank]  
    local_num_classes = class_offset[comm_rank+1] - local_start_class  
    local_label_function_U = eye(num_unlabel_samples, local_num_classes, 0, np.float32, format='csr')  
      
    # initialize local label_function_L  
    local_label_function_L = lil_matrix((num_label_samples, local_num_classes), dtype = np.float32)  
    for i in xrange(num_label_samples):  
        class_off = int(labels[i]) - local_start_class  
        if class_off >= 0 and class_off < local_num_classes:  
            local_label_function_L[i, class_off] = 1.0  
    local_label_function_L = local_label_function_L.tocsr()  
    local_label_info = affinity_matrix_UL.dot(local_label_function_L)  
    print "Processor %d/%d has to process %d classes..." % (comm_rank, comm_size, local_label_function_L.shape[1])  
      
    # start to propagation  
    iter = 1; changed = 100.0;  
    evaluation(num_unlabel_samples, local_start_class, local_label_function_U, unlabel_data_id, labels_id)  
    while True:  
        pre_label_function = local_label_function_U.copy()  
          
        # propagation  
        local_label_function_U = affinity_matrix_UU.dot(local_label_function_U) + local_label_info  
          
        # check converge  
        local_changed = abs(pre_label_function - local_label_function_U).sum()  
        changed = comm.reduce(local_changed, root = 0, op = MPI.SUM)  
        status = 'RUN'  
        test = False  
        if comm_rank == 0:  
            if iter % 1 == 0:  
                norm_changed = changed / (num_unlabel_samples * num_classes)  
                print "---> Iteration %d/%d, changed: %f" % (iter, max_iter, norm_changed)  
            if iter >= max_iter or changed < tol:  
                status = 'STOP'  
                print "************** Iteration over! ****************"  
            if iter % test_per_iter == 0:  
                test = True  
            iter += 1  
        test = comm.bcast(test if comm_rank == 0 else None, root = 0)  
        status = comm.bcast(status if comm_rank == 0 else None, root = 0)  
        if status == 'STOP':  
            break  
        if test == True:  
            evaluation(num_unlabel_samples, local_start_class, local_label_function_U, unlabel_data_id, labels_id)  
    evaluation(num_unlabel_samples, local_start_class, local_label_function_U, unlabel_data_id, labels_id)  
  
  
def evaluation(num_unlabel_samples, local_start_class, local_label_function_U, unlabel_data_id, labels_id):  
    # get local label with max score  
    if comm_rank == 0:  
        print "Start to combine local result..."  
    local_max_score = np.zeros((num_unlabel_samples, 1), np.float32)   
    local_max_label = np.zeros((num_unlabel_samples, 1), np.int32)  
    for i in xrange(num_unlabel_samples):  
        local_max_label[i, 0] = np.argmax(local_label_function_U.getrow(i).todense())  
        local_max_score[i, 0] = local_label_function_U[i, local_max_label[i, 0]]  
        local_max_label[i, 0] += local_start_class  
          
    # gather the results from all the processors  
    if comm_rank == 0:  
        print "Start to gather results from all processors"  
    all_max_label = np.hstack(comm.allgather(local_max_label))  
    all_max_score = np.hstack(comm.allgather(local_max_score))  
      
    # get terminate label of unlabeled data  
    if comm_rank == 0:  
        print "Start to analysis the results..."  
        right_predict_count = 0  
        for i in xrange(num_unlabel_samples):  
            if i % 1000 == 0:  
                print "***", all_max_score[i]  
            max_idx = np.argmax(all_max_score[i])  
            max_label = all_max_label[i, max_idx]  
            if int(unlabel_data_id[i]) == int(labels_id[max_label]):  
                right_predict_count += 1  
        accuracy = float(right_predict_count) * 100.0 / num_unlabel_samples  
        print "Have %d samples, accuracy: %.3f%%!" % (num_unlabel_samples, accuracy)  
  
  
if __name__ == '__main__':  
    run_label_propagation_sparse(knn_num_neighbors = 20, max_iter = 30)

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#***************************************************************************

#* Description: label propagation

#* Author: Zou Xiaoyi (zouxy09@qq.com)

#* Date: 2015-10-15

#* HomePage: http://blog.csdn.net/zouxy09

#**************************************************************************

import os, sys, time

import numpy as np

from scipy.sparse import csr_matrix, lil_matrix, eye

import operator

import cPickle as pickle

import mpi4py.MPI as MPI

# Global variables for MPI

# instance for invoking MPI related functions

comm = MPI.COMM_WORLD

# the node rank in the whole community

comm_rank = comm.Get_rank()

# the size of the whole community, i.e., the total number of working nodes in the MPI cluster

comm_size = comm.Get_size()

# load mnist dataset

def load_MNIST():

import gzip

f = gzip.open("mnist.pkl.gz", "rb")

train, val, test = pickle.load(f)

f.close()

Mat_Label = train[0]

labels = train[1]

Mat_Unlabel = test[0]

groundtruth = test[1]

labels_id = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

return Mat_Label, labels, labels_id, Mat_Unlabel, groundtruth

# return k neighbors index

def navie_knn(dataSet, query, k):

numSamples = dataSet.shape[0]

## step 1: calculate Euclidean distance

diff = np.tile(query, (numSamples, 1)) - dataSet

squaredDiff = diff ** 2

squaredDist = np.sum(squaredDiff, axis = 1) # sum is performed by row

## step 2: sort the distance

sortedDistIndices = np.argsort(squaredDist)

if k > len(sortedDistIndices):

k = len(sortedDistIndices)

return sortedDistIndices[0:k]

# build a big graph (normalized weight matrix)

# sparse U x (U + L) matrix

def buildSubGraph(Mat_Label, Mat_Unlabel, knn_num_neighbors):

num_unlabel_samples = Mat_Unlabel.shape[0]

data = []; indices = []; indptr = [0]

Mat_all = np.vstack((Mat_Label, Mat_Unlabel))

values = np.ones(knn_num_neighbors, np.float32) / knn_num_neighbors

for i in xrange(num_unlabel_samples):

k_neighbors = navie_knn(Mat_all, Mat_Unlabel[i, :], knn_num_neighbors)

indptr.append(np.int32(indptr[-1]) + knn_num_neighbors)

indices.extend(k_neighbors)

data.append(values)

return csr_matrix((np.hstack(data), indices, indptr))

# build a big graph (normalized weight matrix)

# sparse U x (U + L) matrix

def buildSubGraph_MPI(Mat_Label, Mat_Unlabel, knn_num_neighbors):

num_unlabel_samples = Mat_Unlabel.shape[0]

local_data = []; local_indices = []; local_indptr = [0]

Mat_all = np.vstack((Mat_Label, Mat_Unlabel))

values = np.ones(knn_num_neighbors, np.float32) / knn_num_neighbors

sample_offset = np.linspace(0, num_unlabel_samples, comm_size + 1).astype('int')

for i in range(sample_offset[comm_rank], sample_offset[comm_rank+1]):

k_neighbors = navie_knn(Mat_all, Mat_Unlabel[i, :], knn_num_neighbors)

local_indptr.append(np.int32(local_indptr[-1]) + knn_num_neighbors)

local_indices.extend(k_neighbors)

local_data.append(values)

data = np.hstack(comm.allgather(local_data))

indices = np.hstack(comm.allgather(local_indices))

indptr_tmp = comm.allgather(local_indptr)

indptr = []

for i in range(len(indptr_tmp)):

if i == 0:

indptr.extend(indptr_tmp[i])

else:

last_indptr = indptr[-1]

del(indptr[-1])

indptr.extend(indptr_tmp[i] + last_indptr)

return csr_matrix((np.hstack(data), indices, indptr), dtype = np.float32)

# label propagation

def run_label_propagation_sparse(knn_num_neighbors = 20, max_iter = 100, tol = 1e-4, test_per_iter = 1):

# load data and graph

print "Processor %d/%d loading graph file..." % (comm_rank, comm_size)

#Mat_Label, labels, Mat_Unlabel, groundtruth = loadFourBandData()

Mat_Label, labels, labels_id, Mat_Unlabel, unlabel_data_id = load_MNIST()

if comm_size > len(labels_id):

raise ValueError("Sorry, the processors must be less than the number of classes")

#affinity_matrix = buildSubGraph(Mat_Label, Mat_Unlabel, knn_num_neighbors)

affinity_matrix = buildSubGraph_MPI(Mat_Label, Mat_Unlabel, knn_num_neighbors)

# get some parameters

num_classes = len(labels_id)

num_label_samples = len(labels)

num_unlabel_samples = Mat_Unlabel.shape[0]

affinity_matrix_UL = affinity_matrix[:, 0:num_label_samples]

affinity_matrix_UU = affinity_matrix[:, num_label_samples:num_label_samples+num_unlabel_samples]

if comm_rank == 0:

print "Have %d labeled images, %d unlabeled images and %d classes" % (num_label_samples, num_unlabel_samples, num_classes)

# divide label_function_U and label_function_L to all processors

class_offset = np.linspace(0, num_classes, comm_size + 1).astype('int')

# initialize local label_function_U

local_start_class = class_offset[comm_rank]

local_num_classes = class_offset[comm_rank+1] - local_start_class

local_label_function_U = eye(num_unlabel_samples, local_num_classes, 0, np.float32, format='csr')

# initialize local label_function_L

local_label_function_L = lil_matrix((num_label_samples, local_num_classes), dtype = np.float32)

for i in xrange(num_label_samples):

class_off = int(labels[i]) - local_start_class

if class_off >= 0 and class_off < local_num_classes:

local_label_function_L[i, class_off] = 1.0

local_label_function_L = local_label_function_L.tocsr()

local_label_info = affinity_matrix_UL.dot(local_label_function_L)

print "Processor %d/%d has to process %d classes..." % (comm_rank, comm_size, local_label_function_L.shape[1])

# start to propagation

iter = 1; changed = 100.0;

evaluation(num_unlabel_samples, local_start_class, local_label_function_U, unlabel_data_id, labels_id)

while True:

pre_label_function = local_label_function_U.copy()

# propagation

local_label_function_U = affinity_matrix_UU.dot(local_label_function_U) + local_label_info

# check converge

local_changed = abs(pre_label_function - local_label_function_U).sum()

changed = comm.reduce(local_changed, root = 0, op = MPI.SUM)

status = 'RUN'

test = False

if comm_rank == 0:

if iter % 1 == 0:

norm_changed = changed / (num_unlabel_samples * num_classes)

print "---> Iteration %d/%d, changed: %f" % (iter, max_iter, norm_changed)

if iter >= max_iter or changed < tol:

status = 'STOP'

print "************** Iteration over! ****************"

if iter % test_per_iter == 0:

test = True

iter += 1

test = comm.bcast(test if comm_rank == 0 else None, root = 0)

status = comm.bcast(status if comm_rank == 0 else None, root = 0)

if status == 'STOP':

break

if test == True:

evaluation(num_unlabel_samples, local_start_class, local_label_function_U, unlabel_data_id, labels_id)

def evaluation(num_unlabel_samples, local_start_class, local_label_function_U, unlabel_data_id, labels_id):

# get local label with max score

if comm_rank == 0:

print "Start to combine local result..."

local_max_score = np.zeros((num_unlabel_samples, 1), np.float32)

local_max_label = np.zeros((num_unlabel_samples, 1), np.int32)

for i in xrange(num_unlabel_samples):

local_max_label[i, 0] = np.argmax(local_label_function_U.getrow(i).todense())

local_max_score[i, 0] = local_label_function_U[i, local_max_label[i, 0]]

local_max_label[i, 0] += local_start_class

# gather the results from all the processors

if comm_rank == 0:

print "Start to gather results from all processors"

all_max_label = np.hstack(comm.allgather(local_max_label))

all_max_score = np.hstack(comm.allgather(local_max_score))

# get terminate label of unlabeled data

if comm_rank == 0:

print "Start to analysis the results..."

right_predict_count = 0

for i in xrange(num_unlabel_samples):

if i % 1000 == 0:

print "***", all_max_score[i]

max_idx = np.argmax(all_max_score[i])

max_label = all_max_label[i, max_idx]

if int(unlabel_data_id[i]) == int(labels_id[max_label]):

right_predict_count += 1

accuracy = float(right_predict_count) * 100.0 / num_unlabel_samples

print "Have %d samples, accuracy: %.3f%%!" % (num_unlabel_samples, accuracy)

if __name__ == '__main__':

run_label_propagation_sparse(knn_num_neighbors = 20, max_iter = 30)

五、參考資料

[1]Semi-SupervisedLearning with Graphs.pdf

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標籤傳播演算法（Label Propagation）及 Python 實現

一、半監督學習

二、標籤傳播演算法

2.1、相似矩陣構建

2.2、LP演算法

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四、LP演算法MPI並行實現

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