crf的Python实现代码
作者：金良（[email protected]） csdn博客： http://blog.csdn.net/u012176591
对数域操作函数
class Logspace:
def __init__(self):
self.LOGZERO =np.nan
def eexp(self,x): if np.isnan(x): return 0 else: return np.exp(x) def eln(self,x): if x == 0: return self.LOGZERO elif x>0: return np.log(x) else: print 'Wrong!!!\n\t negative input error' return np.nan def elnsum(self,elnx,elny): if np.isnan(elnx): return elny elif np.isnan(elny): return elnx elif elnx > elny: return elnx + self.eln(1+np.exp(elny-elnx)) else: return elny + self.eln(1+np.exp(elnx-elny)) def elnproduct(self,elnx,elny): if np.isnan(elnx) or np.isnan(elny): return self.LOGZERO else: return elnx + elny def elnmatprod(self,elnx,elny): #array([[ 0.]])其size是2 xsize = np.size(np.shape(elnx)) ysize = np.size(np.shape(elny)) if xsize == 1 and ysize == 1: r = self.LOGZERO for i in range(np.shape(elnx)[0]): r = self.elnsum(r,self.elnproduct(elnx[i],elny[i])) return r elif xsize == 1 and not ysize == 1: n = np.shape(elny)[1] r = np.zeros(n) for i in range(n): r[i] = self.elnmatprod(elnx,elny[:,i]) return r elif not xsize == 1 and ysize == 1: n = np.shape(elnx)[0] r = np.zeros(n) for i in range(n): r[i] = self.elnmatprod(elnx[i,:],elny) return r else: m,n= np.shape(elnx) p = np.shape(elny)[1] r = np.zeros((m,p)) for i in range(m): for j in range(p): r[i][j] = self.elnmatprod(elnx[i,:],elny[:,j]) return r def eexpmat(self,elny): expy = np.copy(elny) if np.size(np.shape(elny)) == 1: for i in range(np.shape(elny)[0]): expy[i] = self.eexp(expy[i]) else: for i in range(np.shape(elny)[0]): for j in range(np.shape(elny)[1]): expy[i][j] = self.eexp(expy[i][j]) return expy def elnmat(self,x): elnx = np.copy(x) if np.size(np.shape(x)) == 1: for i in range(np.shape(x)[0]): elnx[i] = self.eln(x[i]) else: for i in range(np.shape(x)[0]): for j in range(np.shape(x)[1]): elnx[i,j] = self.eln(x[i,j]) return elnx 测试举例
logspace = Logspace()
M1 = np.array([1,0.5])
M2 = np.array([[1.3,1.5],[1.8,0.5]])
M3 = np.array([[0.8,1.5],[1.8,0.7]])
M4 = np.array([0,0])
print logspace.eexpmat(logspace.elnmatprod(M1,M2))
print np.dot(logspace.eexpmat(M1),logspace.eexpmat(M2))- [ 19.94836491 14.90077579]
[ 19.94836491 14.90077579]
条件随机场的函数
def read_corps(corpsfile='testchunk.data'):
#http://www.chokkan.org/software/crfsuite/tutorial.html,该页面有两个网址可下载数据集，该数据集量很大
#http://blog.dpdearing.com/2011/12/opennlp-part-of-speech-pos-tags-penn-english-treebank/
tagids = defaultdict(lambda: len(tagids))
tagids[""] = 0 corps=[] onesentence = [] words = [ "" ] tags = [ 0 ] #wordnumcount = 0 with open(corpsfile,'r') as f: for line in f: if len(line)<=1: pass elif line != '. . O\n': # '. . O\n'表示一句话结束，当一句话未结束则将该单词加入列表onesentence onesentence.append(line) else: #如果一句话结束，则对该句话的所有出现的单词进行处理，将处理结果存入列表corps for texts in onesentence: #wordnumcount += 1 w_t = texts.strip().split(" ") #print w_t try: #由于表示数字的字符串变化较多，为了减少其干扰，这里将其检测出来并替换掉 float(w_t[0].strip().replace(',','')); #print w_t words.append('#CD#') except: words.append(w_t[0].lower()) #if w_t[1] in{ '``',',',"''",'$','#',')','('}: # print w_t tags.append(tagids[w_t[1]]) words.append("") #words是一句话的单词组成的列表 tags.append(0) #tags是一句话的标注组成的列表，与单词列表words一一对应 if np.shape(words)[0] > 2: #排除掉空句子 corps.append((words,tags)) #对onesentence，words和tags重新初始化 onesentence = [] words = [ "" ] tags = [ 0 ] #print '一共出现的单词个数：'+np.str(wordnumcount) #一共出现的单词个数：40377 return corps,tagids def getfeatureTS(corps): featuresets = set() #特征的集合 featureT = [] #转移特征的列表，比如列表元素('T', 2, 3)表示从状态2转到特征3 featureS = [] #状态特征的列表，比如列表元素('S','Confidence', 1) for corp in corps: for i in range(np.shape(corp[0])[0]): if corp[0][i] == '': continue if ('S',corp[0][i],corp[1][i]) not in featuresets: featuresets.add(('S',corp[0][i],corp[1][i])) featureS.append(('S',corp[0][i],corp[1][i])) if corp[0][i-1] != '': if ('T',corp[1][i-1],corp[1][i]) not in featuresets: featuresets.add(('T',corp[1][i-1],corp[1][i])) featureT.append(('T',corp[1][i-1],corp[1][i])) featureTS = featureT+featureS words2tagids = words2tagidfromfeatureS(featureS) return featureTS,words2tagids def getpriorfeatureE(corps,featureTS): #计算先验特征期望值 N = np.shape(corps)[0] #训练样本数 K = np.shape(featureTS)[0] #特征数 priorfeatureE = np.zeros(K) for corp in corps: for i in range(np.shape(corp[0])[0]): if corp[0][i] == '': continue try: idex = featureTS.index(('S', corp[0][i], corp[1][i])) priorfeatureE[idex] += 1.0 except: pass try: idex = featureTS.index(('T', corp[1][i-1], corp[1][i])) priorfeatureE[idex] += 1.0 except: pass priorfeatureE /=N #plt.plot(priorfeatureE) #从特征的先验期望值可以看出无论是转移特征(从横坐标0开始)还是状态特征(从横坐标318开始)，先被记录的先验期望值越大 return priorfeatureE def words2tagidfromfeatureS(featureS): #统计所有单词分别对应的词性列表 words2tagids = {} for feature in featureS: word = feature[1] state = feature[2] if word in words2tagids: words2tagids[word].append(state) else: words2tagids[word] = [state] #lennums列表统计单词对应的词性的长度的分布 #lennums = [[lenlist.count(i) for i in range(1,max(lenlist)+1)] # for lenlist in [[len(words2tagids[i]) for i in words2tagids]]][0] #lennums = [3760, 389, 32, 1] return words2tagids def getpostfeatureE(weights,corps,featureTS,words2tagids): K = np.shape(featureTS)[0] #特征数 postfeatureE = np.zeros(K) #特征的后验期望值 N = np.shape(corps)[0] for corpidx in range(N): corp = corps[corpidx][0][1:-1] lencorp = np.size(corp) #语料长度，即句子中的单词数 Mlist = {} Mlist['mat'] = ['']*(lencorp+1) Mlist['dim'] = [words2tagids[corp[i]] for i in range(lencorp)] Mlist['len'] = [np.size(words2tagids[corp[i]]) for i in range(lencorp)] for i in range(lencorp+1): if i == 0:#第一个矩阵，只有状态特征的行向量 d = Mlist['len'][0] Mlist['mat'][i] = np.zeros((1,d)) for j in range(d): Mlist['mat'][i][0,j] = weights[featureTS.index(('S', corp[0], words2tagids[corp[0]][j]))] continue if i == lencorp:#最后一个矩阵，元素为0的列向量矩阵 Mlist['mat'][i] = np.zeros((Mlist['len'][-1],1)) continue #既非第一个矩阵，亦非第二个矩阵，每个元素要计算状态特征和转移特征 Mlist['mat'][i] = np.zeros((Mlist['len'][i-1],Mlist['len'][i])) for d1 in range(Mlist['len'][i-1]): for d2 in range(Mlist['len'][i]): id1 = words2tagids[corp[i-1]][d1] id2 = words2tagids[corp[i]][d2] try: Sweight = weights[featureTS.index(('S', corp[i], id2))] except: Sweight = 0 try: Tweight = weights[featureTS.index(('T', id1, id2))] except: Tweight = 0 Mlist['mat'][i][d1,d2] = Sweight + Tweight #return Mlist,corps[0] #return 0 z = np.array([[0]]) for i in range(lencorp+1): z = logspace.elnmatprod(z,Mlist['mat'][i]) Alphalist = ['']*(lencorp+2) Betalist = ['']*(lencorp+2) Alphalist[0] = np.zeros((1,1)) # 第一个前向向量：1*1的矩阵 Betalist[-1] = np.zeros((Mlist['len'][-1],1)) #Alphalist里的元素是单行矩阵，Betalist里的元素是单列矩阵 for i in range(1,lencorp+2): #print i,np.shape(Alphalist[i-1]),np.shape(Mlist['mat'][i-1]) Alphalist[i] = logspace.elnmatprod(Alphalist[i-1],Mlist['mat'][i-1]) for i in range(lencorp,-1,-1): Betalist[i] = logspace.elnmatprod(Mlist['mat'][i],Betalist[i+1]) for i in range(1,lencorp+1): d1,d2 = np.shape(Mlist['mat'][i-1]) #print d1,d2,Mlist['dim'][i-2],Mlist['dim'][i-1] # 3,2,34 #print '================' for di in range(d1): for dj in range(d2): # i=1时，没有转移特征；i=lencorp+1时，转移特征和状态特征都没有 plocal = logspace.eexp(logspace.elnproduct(logspace.elnproduct(logspace.elnproduct(Alphalist[i-1][0,di], Mlist['mat'][i-1][di,dj]),Betalist[i][dj,0]),-z[0,0])) if i == 1:#只有状态特征 try: Sidex = featureTS.index(('S', corp[i-1], Mlist['dim'][i-1][dj])) postfeatureE[Sidex] += plocal except: pass else: try: Sidex = featureTS.index(('S', corp[i-1], Mlist['dim'][i-1][dj])) postfeatureE[Sidex] += plocal except: pass try: Tidex = featureTS.index(('T', Mlist['dim'][i-2][di], Mlist['dim'][i-1][dj])) postfeatureE[Tidex] += plocal except:#如果该转移特征bucunza不存在，直接忽略 pass #aM = logspace.elnmatprod(Alphalist[i-1],Mlist['mat'][i-1]) #aMb = logspace.elnmatprod(aM,Betalist[i]) #print promat #backuppromat.append(promat) postfeatureE /= N return postfeatureE def getliknegvalue(weights,corps,featureTS,words2tagids): #目标函数是对对数似然函数取负，故要使其最小化 K = np.shape(featureTS)[0] #特征数 N = np.shape(corps)[0] liknegvalue = 0 for corpidx in range(N): corp = corps[corpidx][0][1:-1] tag = corps[corpidx][1][1:-1] lencorp = np.size(corp) #语料长度，即句子中的单词数 Mlist = {} Mlist['mat'] = ['']*(lencorp+1) Mlist['dim'] = [words2tagids[corp[i]] for i in range(lencorp)] Mlist['len'] = [np.size(words2tagids[corp[i]]) for i in range(lencorp)] for i in range(lencorp+1): if i == 0:#第一个矩阵，只有状态特征的行向量 d = Mlist['len'][0] Mlist['mat'][i] = np.zeros((1,d)) for j in range(d): Mlist['mat'][i][0,j] = weights[featureTS.index(('S', corp[0], words2tagids[corp[0]][j]))] continue if i == lencorp:#最后一个矩阵，元素为0的列向量矩阵 Mlist['mat'][i] = np.zeros((Mlist['len'][-1],1)) continue #既非第一个矩阵，亦非第二个矩阵，每个元素要计算状态特征和转移特征 Mlist['mat'][i] = np.zeros((Mlist['len'][i-1],Mlist['len'][i])) for d1 in range(Mlist['len'][i-1]): for d2 in range(Mlist['len'][i]): id1 = words2tagids[corp[i-1]][d1] id2 = words2tagids[corp[i]][d2] try: Sweight = weights[featureTS.index(('S', corp[i], id2))] except: Sweight = 0 try: Tweight = weights[featureTS.index(('T', id1, id2))] except: Tweight = 0 Mlist['mat'][i][d1,d2] = Sweight + Tweight numerator = 0 denominator= np.array([[0]]) for i in range(lencorp+1): denominator = logspace.elnmatprod(denominator,Mlist['mat'][i]) if i == 0: numerator = logspace.elnproduct(numerator,Mlist['mat'][i][0,Mlist['dim'][i].index(tag[i])]) elif i < lencorp: numerator = logspace.elnproduct(numerator,Mlist['mat'][i][Mlist['dim'][i-1].index(tag[i-1]),Mlist['dim'][i].index(tag[i])]) liknegvalue += (denominator - numerator)/N return liknegvalue[0,0] def getgradients(priorfeatureE,weights,corps,featureTS,words2tagids): postfeatureE = getpostfeatureE(weights,corps,featureTS,words2tagids) return postfeatureE - priorfeatureEL-BFGS函数用于数值优化
def twoloop(s, y, rho,gk):
# 被lbfgs函数调用
n = len(s) #向量序列的长度
if np.shape(s)[0] >= 1:
#h0是标量，而非矩阵 h0 = 1.0*np.dot(s[-1],y[-1])/np.dot(y[-1],y[-1]) else: h0 = 1 a = np.empty((n,)) q = gk.copy() for i in range(n - 1, -1, -1): a[i] = rho[i] * np.dot(s[i], q) q -= a[i] * y[i] z = h0*q for i in range(n): b = rho[i] * np.dot(y[i], z) z += s[i] * (a[i] - b) return z def lbfgs(fun = getliknegvalue,gfun = getgradients,x0 = weights,corps = corps, featureTS = featureTS,words2tagids = words2tagids, priorfeatureE = priorfeatureE,m=10,maxk = 20): # fun和gfun分别是目标函数及其一阶导数,x0是初值,m为储存的序列的大小 rou = 0.55 sigma = 0.4 epsilon = 1e-5 k = 0 n = np.shape(x0)[0] #自变量的维度 s, y, rho = [], [], [] while k < maxk : gk = gfun(priorfeatureE,x0,corps,featureTS,words2tagids) if np.linalg.norm(gk) < epsilon: break dk = -1.0*twoloop(s, y, rho,gk) m0=0; mk=0 funcvalue = fun(x0,corps,featureTS,words2tagids) while m0 < 20: # 用Armijo搜索求步长 if fun(x0+rou**m0*dk,corps,featureTS,words2tagids) < funcvalue+sigma*rou**m0*np.dot(gk,dk): mk = m0 break m0 += 1 x = x0 + rou**mk*dk sk = x - x0 yk = gfun(priorfeatureE,x,corps,featureTS,words2tagids) - gk if np.dot(sk,yk) > 0: #增加新的向量 rho.append(1.0/np.dot(sk,yk)) s.append(sk) y.append(yk) if np.shape(rho)[0] > m: #弃掉最旧向量 rho.pop(0) s.pop(0) y.pop(0) k += 1 x0 = x print '迭代次数：%d, 函数值：%f'%(k,funcvalue) return x0, fun(x0,corps,featureTS,words2tagids)#,k#分别是最优点坐标，最优值，迭代次数条件随机场的测试
from collections import defaultdict
corps,tagids = read_corps('mycrfdata.data')
featureTS,words2tagids = getfeatureTS(corps) #得到总的特征列表featureTS
K = np.shape(featureTS)[0] #总的特征数
N = np.shape(corps)[0] #训练样本数
priorfeatureE = getpriorfeatureE(corps,featureTS) #计算特征的先验期望值
weights = np.array([1.0/K]*K)
#postfeatureE = getpostfeatureE(weights,corps,featureTS,words2tagids)
#liknegvalue = getliknegvalue(weights,corps,featureTS,words2tagids)
weights,likelyfuncvalue = lbfgs(fun = getliknegvalue,gfun = getgradients,x0 = weights,corps = corps,
featureTS = featureTS,words2tagids = words2tagids,
priorfeatureE = priorfeatureE,m=10,maxk = 40)- 迭代次数：1, 函数值：4.517425
迭代次数：2, 函数值：3.402287
迭代次数：3, 函数值：2.591947
迭代次数：4, 函数值：1.961000
迭代次数：5, 函数值：1.511211
迭代次数：6, 函数值：1.164718
迭代次数：7, 函数值：1.011021
迭代次数：8, 函数值：0.863806
迭代次数：9, 函数值：0.764431
迭代次数：10, 函数值：0.685292
迭代次数：11, 函数值：0.610862
迭代次数：12, 函数值：0.567107
迭代次数：13, 函数值：0.524796
迭代次数：14, 函数值：0.495254
迭代次数：15, 函数值：0.466203
迭代次数：16, 函数值：0.443137
迭代次数：17, 函数值：0.422248
迭代次数：18, 函数值：0.406402
迭代次数：19, 函数值：0.396005
迭代次数：20, 函数值：0.386036
迭代次数：21, 函数值：0.380390
迭代次数：22, 函数值：0.380207
迭代次数：23, 函数值：0.376401
迭代次数：24, 函数值：0.375102
迭代次数：25, 函数值：0.370988
迭代次数：26, 函数值：0.366604
迭代次数：27, 函数值：0.360824
迭代次数：28, 函数值：0.355004
迭代次数：29, 函数值：0.351590
迭代次数：30, 函数值：0.347119
迭代次数：31, 函数值：0.344447
迭代次数：32, 函数值：0.341149
迭代次数：33, 函数值：0.337679
迭代次数：34, 函数值：0.335245
迭代次数：35, 函数值：0.332701
迭代次数：36, 函数值：0.329436
迭代次数：37, 函数值：0.326451
迭代次数：38, 函数值：0.324949
迭代次数：39, 函数值：0.321441
迭代次数：40, 函数值：0.319166
迭代次数：41, 函数值：0.315978
迭代次数：42, 函数值：0.312033
迭代次数：43, 函数值：0.308039
迭代次数：44, 函数值：0.305588
迭代次数：45, 函数值：0.302214
import codecs
#读取中文文本，首先要把文本文件保存成utf-8格式，默认的ANSI格式文件读取后不能正确打印中文字符
likelihoodlist = []
with codecs.open('loglikelihood.txt','r','utf-8') as f: for line in f: #u'\uff1a'是中文符号“：” likelihoodlist.append(float(line.split(u'\uff1a')[-1].split()[0])) plt.plot(likelihoodlist[:100],'-k') plt.plot(likelihoodlist[:100],'+r') plt.title(u'L-BFGS训练CRF的收敛曲线',{'fontname':'STFangsong','fontsize':18}) plt.xlabel(u'迭代次数',{'fontname':'STFangsong','fontsize':18}) plt.ylabel(u'对数似然函数取负值',{'fontname':'STFangsong','fontsize':18})
from scipy.stats.kde import gaussian_kde
# this create the kernel, given an array it will estimate the probability over that values
kde = gaussian_kde(priorfeatureE)
# these are the values over wich your kernel will be evaluated
dist_space = linspace( min(priorfeatureE)-0.01*(max(priorfeatureE)-min(priorfeatureE)), max(priorfeatureE), 100 )
# plot the results
plt.plot(dist_space, kde(dist_space))
plt.title(u'特征的先验期望取值的密度分布',{'fontname':'STFangsong','fontsize':18})
plt.xlabel(u'特征的先验期望取值',{'fontname':'STFangsong','fontsize':18})
plt.ylabel(u'密度估计',{'fontname':'STFangsong','fontsize':18})
from scipy.stats.kde import gaussian_kde
#weights是训练的权值列表,由于训练时间长，得到并不容易，故先保存
np.savetxt('crfweights.out', weights, delimiter=',') #
data = np.genfromtxt('crfweights.out', delimiter=',') # this create the kernel, given an array it will estimate the probability over that values kde = gaussian_kde(data) # these are the values over wich your kernel will be evaluated dist_space = linspace( min(data)-0.01*(max(data)-min(data)), max(data), 400 ) fig,axes = plt.subplots(nrows=2,ncols=1,figsize=(12,10)) plt.subplots_adjust(wspace = None,hspace=0.3) axes[0].plot(data) axes[0].set_title(u'迭代训练500次的特征权值图',{'fontname':'STFangsong','fontsize':18}) axes[0].set_xlabel(u'特征(5331个)',{'fontname':'STFangsong','fontsize':18}) axes[0].set_ylabel(u'权值大小',{'fontname':'STFangsong','fontsize':18}) axes[1].plot(dist_space, kde(dist_space),'k',marker = u'$\circ$') axes[1].set_title(u'迭代训练500次的特征权值密度分布',{'fontname':'STFangsong','fontsize':18}) axes[1].set_xlabel(u'特征权值大小',{'fontname':'STFangsong','fontsize':18}) axes[1].set_ylabel(u'密度估计',{'fontname':'STFangsong','fontsize':18})- 势函数：势，英语potential，就是有一种潜力，由一种能量转化为别的能量的潜力，描述这种潜力的函数，应该就是叫势函数。势函数到处可见，凡是涉及到能量描述和转换的地方，都会涉及到势函数，还有生物势、化学势。统计物理里面涉及到很多这方面的知识。
- 标注问题：在自然语言处理中有一个常见的任务，即标注。常见的有：1）词性标注（Part-Of-Speech Tagging），将句子中的每个词标注词性，例如名词、动词等；2）实体标注（Name Entity Tagging），将句子中的特殊词标注，例如地址、日期、人物姓名等。
http://blog.csdn.net/lanxu_yy/article/details/36245161 - 条件随机场（Conditional random fields）
http://blog.csdn.net/chlele0105/article/details/14897761 - 条件随机场简介(Introduction to Conditional Random Fields)
说明了特征函数的内容
http://blog.echen.me/2012/01/03/introduction-to-conditional-random-fields/ - 条件随机场的Python例子
https://github.com/huangzhengsjtu/pcrf/ - http://flexcrfs.sourceforge.net/flexcrfs.pdf
- CRF++的简单使用
http://blog.csdn.net/felomeng/article/details/4288492 - Using CRF for Image Segmentation in Python
http://sloblog.io/~ankl/B-SrKYr2qJw/using-crf-for-image-segmentation-in-python-step-1 - http://www.inference.phy.cam.ac.uk/hmw26/crf/
《Conditional Random Fields: An Introduction》内容不错