python实现lda模型共词聚类 lda python

# _*_ coding: utf-8 _*_
"""
python_lda.py by xianhu
"""
import os
import numpy
import logging
from collections import defaultdict
# 全局变量
MAX_ITER_NUM = 10000 # 最大迭代次数
VAR_NUM = 20 # 自动计算迭代次数时,计算方差的区间大小
class BiDictionary(object):
"""
定义双向字典,通过key可以得到value,通过value也可以得到key
"""
def __init__(self):
"""
:key: 双向字典初始化
"""
self.dict = {} # 正向的数据字典,其key为self的key
self.dict_reversed = {} # 反向的数据字典,其key为self的value
return
def __len__(self):
"""
:key: 获取双向字典的长度
"""
return len(self.dict)
def __str__(self):
"""
:key: 将双向字典转化为字符串对象
"""
str_list = ["%s\t%s" % (key, self.dict[key]) for key in self.dict]
return "\n".join(str_list)
def clear(self):
"""
:key: 清空双向字典对象
"""
self.dict.clear()
self.dict_reversed.clear()
return
def add_key_value(self, key, value):
"""
:key: 更新双向字典,增加一项
"""
self.dict[key] = value
self.dict_reversed[value] = key
return
def remove_key_value(self, key, value):
"""
:key: 更新双向字典,删除一项
"""
if key in self.dict:
del self.dict[key]
del self.dict_reversed[value]
return
def get_value(self, key, default=None):
"""
:key: 通过key获取value,不存在返回default
"""
return self.dict.get(key, default)
def get_key(self, value, default=None):
"""
:key: 通过value获取key,不存在返回default
"""
return self.dict_reversed.get(value, default)
def contains_key(self, key):
"""
:key: 判断是否存在key值
"""
return key in self.dict
def contains_value(self, value):
"""
:key: 判断是否存在value值
"""
return value in self.dict_reversed
def keys(self):
"""
:key: 得到双向字典全部的keys
"""
return self.dict.keys()
def values(self):
"""
:key: 得到双向字典全部的values
"""
return self.dict_reversed.keys()
def items(self):
"""
:key: 得到双向字典全部的items
"""
return self.dict.items()
class CorpusSet(object):
"""
定义语料集类,作为LdaBase的基类
"""
def __init__(self):
"""
:key: 初始化函数
"""
# 定义关于word的变量
self.local_bi = BiDictionary() # id和word之间的本地双向字典,key为id,value为word
self.words_count = 0 # 数据集中word的数量(排重之前的)
self.V = 0 # 数据集中word的数量(排重之后的)
# 定义关于article的变量
self.artids_list = [] # 全部article的id的列表,按照数据读取的顺序存储
self.arts_Z = [] # 全部article中所有词的id信息,维数为 M * art.length()
self.M = 0 # 数据集中article的数量
# 定义推断中用到的变量(可能为空)
self.global_bi = None # id和word之间的全局双向字典,key为id,value为word
self.local_2_global = {} # 一个字典,local字典和global字典之间的对应关系
return
def init_corpus_with_file(self, file_name):
"""
:key: 利用数据文件初始化语料集数据。文件每一行的数据格式: id[tab]word1 word2 word3......
"""
with open(file_name, "r", encoding="utf-8") as file_iter:
self.init_corpus_with_articles(file_iter)
return
def init_corpus_with_articles(self, article_list):
"""
:key: 利用article的列表初始化语料集。每一篇article的格式为: id[tab]word1 word2 word3......
"""
# 清理数据--word数据
self.local_bi.clear()
self.words_count = 0
self.V = 0
# 清理数据--article数据
self.artids_list.clear()
self.arts_Z.clear()
self.M = 0
# 清理数据--清理local到global的映射关系
self.local_2_global.clear()
# 读取article数据
for line in article_list:
frags = line.strip().split()
if len(frags) < 2:
continue
# 获取article的id
art_id = frags[0].strip()
# 获取word的id
art_wordid_list = []
for word in [w.strip() for w in frags[1:] if w.strip()]:
local_id = self.local_bi.get_key(word) if self.local_bi.contains_value(word) else len(self.local_bi)
# 这里的self.global_bi为None和为空是有区别的
if self.global_bi is None:
# 更新id信息
self.local_bi.add_key_value(local_id, word)
art_wordid_list.append(local_id)
else:
if self.global_bi.contains_value(word):
# 更新id信息
self.local_bi.add_key_value(local_id, word)
art_wordid_list.append(local_id)
# 更新local_2_global
self.local_2_global[local_id] = self.global_bi.get_key(word)
# 更新类变量: 必须article中word的数量大于0
if len(art_wordid_list) > 0:
self.words_count += len(art_wordid_list)
self.artids_list.append(art_id)
self.arts_Z.append(art_wordid_list)
# 做相关初始计算--word相关
self.V = len(self.local_bi)
logging.debug("words number: " + str(self.V) + ", " + str(self.words_count))
# 做相关初始计算--article相关
self.M = len(self.artids_list)
logging.debug("articles number: " + str(self.M))
return
def save_wordmap(self, file_name):
"""
:key: 保存word字典,即self.local_bi的数据
"""
with open(file_name, "w", encoding="utf-8") as f_save:
f_save.write(str(self.local_bi))
return
def load_wordmap(self, file_name):
"""
:key: 加载word字典,即加载self.local_bi的数据
"""
self.local_bi.clear()
with open(file_name, "r", encoding="utf-8") as f_load:
for _id, _word in [line.strip().split() for line in f_load if line.strip()]:
self.local_bi.add_key_value(int(_id), _word.strip())
self.V = len(self.local_bi)
return
class LdaBase(CorpusSet):
"""
LDA模型的基类,相关说明:
》article的下标范围为[0, self.M), 下标为 m
》wordid的下标范围为[0, self.V), 下标为 w
》topic的下标范围为[0, self.K), 下标为 k 或 topic
》article中word的下标范围为[0, article.size()), 下标为 n
"""
def __init__(self):
"""
:key: 初始化函数
"""
CorpusSet.__init__(self)
# 基础变量--1
self.dir_path = "" # 文件夹路径,用于存放LDA运行的数据、中间结果等
self.model_name = "" # LDA训练或推断的模型名称,也用于读取训练的结果
self.current_iter = 0 # LDA训练或推断的模型已经迭代的次数,用于继续模型训练过程
self.iters_num = 0 # LDA训练或推断过程中Gibbs抽样迭代的总次数,整数值或者"auto"
self.topics_num = 0 # LDA训练或推断过程中的topic的数量,即self.K值
self.K = 0 # LDA训练或推断过程中的topic的数量,即self.topics_num值
self.twords_num = 0 # LDA训练或推断结束后输出与每个topic相关的word的个数
# 基础变量--2
self.alpha = numpy.zeros(self.K) # 超参数alpha,K维的float值,默认为50/K
self.beta = numpy.zeros(self.V) # 超参数beta,V维的float值,默认为0.01
# 基础变量--3
self.Z = [] # 所有word的topic信息,即Z(m, n),维数为 M * article.size()
# 统计计数(可由self.Z计算得到)
self.nd = numpy.zeros((self.M, self.K)) # nd[m, k]用于保存第m篇article中第k个topic产生的词的个数,其维数为 M * K
self.ndsum = numpy.zeros((self.M, 1)) # ndsum[m, 0]用于保存第m篇article的总词数,维数为 M * 1
self.nw = numpy.zeros((self.K, self.V)) # nw[k, w]用于保存第k个topic产生的词中第w个词的数量,其维数为 K * V
self.nwsum = numpy.zeros((self.K, 1)) # nwsum[k, 0]用于保存第k个topic产生的词的总数,维数为 K * 1
# 多项式分布参数变量
self.theta = numpy.zeros((self.M, self.K)) # Doc-Topic多项式分布的参数,维数为 M * K,由alpha值影响
self.phi = numpy.zeros((self.K, self.V)) # Topic-Word多项式分布的参数,维数为 K * V,由beta值影响
# 辅助变量,目的是提高算法执行效率
self.sum_alpha = 0.0 # 超参数alpha的和
self.sum_beta = 0.0 # 超参数beta的和
# 先验知识,格式为{word_id: [k1, k2, ...], ...}
self.prior_word = defaultdict(list)
# 推断时需要的训练模型
self.train_model = None
return
# --------------------------------------------------辅助函数---------------------------------------------------------
def init_statistics_document(self):
"""
:key: 初始化关于article的统计计数。先决条件: self.M, self.K, self.Z
"""
assert self.M > 0 and self.K > 0 and self.Z
# 统计计数初始化
self.nd = numpy.zeros((self.M, self.K), dtype=numpy.int)
self.ndsum = numpy.zeros((self.M, 1), dtype=numpy.int)
# 根据self.Z进行更新,更新self.nd[m, k]和self.ndsum[m, 0]
for m in range(self.M):
for k in self.Z[m]:
self.nd[m, k] += 1
self.ndsum[m, 0] = len(self.Z[m])
return
def init_statistics_word(self):
"""
:key: 初始化关于word的统计计数。先决条件: self.V, self.K, self.Z, self.arts_Z
"""
assert self.V > 0 and self.K > 0 and self.Z and self.arts_Z
# 统计计数初始化
self.nw = numpy.zeros((self.K, self.V), dtype=numpy.int)
self.nwsum = numpy.zeros((self.K, 1), dtype=numpy.int)
# 根据self.Z进行更新,更新self.nw[k, w]和self.nwsum[k, 0]
for m in range(self.M):
for k, w in zip(self.Z[m], self.arts_Z[m]):
self.nw[k, w] += 1
self.nwsum[k, 0] += 1
return
def init_statistics(self):
"""
:key: 初始化全部的统计计数。上两个函数的综合函数。
"""
self.init_statistics_document()
self.init_statistics_word()
return
def sum_alpha_beta(self):
"""
:key: 计算alpha、beta的和
"""
self.sum_alpha = self.alpha.sum()
self.sum_beta = self.beta.sum()
return
def calculate_theta(self):
"""
:key: 初始化并计算模型的theta值(M*K),用到alpha值
"""
assert self.sum_alpha > 0
self.theta = (self.nd + self.alpha) / (self.ndsum + self.sum_alpha)
return
def calculate_phi(self):
"""
:key: 初始化并计算模型的phi值(K*V),用到beta值
"""
assert self.sum_beta > 0
self.phi = (self.nw + self.beta) / (self.nwsum + self.sum_beta)
return
# ---------------------------------------------计算Perplexity值------------------------------------------------------
def calculate_perplexity(self):
"""
:key: 计算Perplexity值,并返回
"""
# 计算theta和phi值
self.calculate_theta()
self.calculate_phi()
# 开始计算
preplexity = 0.0
for m in range(self.M):
for w in self.arts_Z[m]:
preplexity += numpy.log(numpy.sum(self.theta[m] * self.phi[:, w]))
return numpy.exp(-(preplexity / self.words_count))
# --------------------------------------------------静态函数---------------------------------------------------------
@staticmethod
def multinomial_sample(pro_list):
"""
:key: 静态函数,多项式分布抽样,此时会改变pro_list的值
:param pro_list: [0.2, 0.7, 0.4, 0.1],此时说明返回下标1的可能性大,但也不绝对
"""
# 将pro_list进行累加
for k in range(1, len(pro_list)):
pro_list[k] += pro_list[k-1]
# 确定随机数 u 落在哪个下标值,此时的下标值即为抽取的类别(random.rand()返回: [0, 1.0))
u = numpy.random.rand() * pro_list[-1]
return_index = len(pro_list) - 1
for t in range(len(pro_list)):
if pro_list[t] > u:
return_index = t
break
return return_index
# ----------------------------------------------Gibbs抽样算法--------------------------------------------------------
def gibbs_sampling(self, is_calculate_preplexity):
"""
:key: LDA模型中的Gibbs抽样过程
:param is_calculate_preplexity: 是否计算preplexity值
"""
# 计算preplexity值用到的变量
pp_list = []
pp_var = numpy.inf
# 开始迭代
last_iter = self.current_iter + 1
iters_num = self.iters_num if self.iters_num != "auto" else MAX_ITER_NUM
for self.current_iter in range(last_iter, last_iter+iters_num):
info = "......"
# 是否计算preplexity值
if is_calculate_preplexity:
pp = self.calculate_perplexity()
pp_list.append(pp)
# 计算列表最新VAR_NUM项的方差
pp_var = numpy.var(pp_list[-VAR_NUM:]) if len(pp_list) >= VAR_NUM else numpy.inf
info = (", preplexity: " + str(pp)) + ((", var: " + str(pp_var)) if len(pp_list) >= VAR_NUM else "")
# 输出Debug信息
logging.debug("\titeration " + str(self.current_iter) + info)
# 判断是否跳出循环
if self.iters_num == "auto" and pp_var < (VAR_NUM / 2):
break
# 对每篇article的每个word进行一次抽样,抽取合适的k值
for m in range(self.M):
for n in range(len(self.Z[m])):
w = self.arts_Z[m][n]
k = self.Z[m][n]
# 统计计数减一
self.nd[m, k] -= 1
self.ndsum[m, 0] -= 1
self.nw[k, w] -= 1
self.nwsum[k, 0] -= 1
if self.prior_word and (w in self.prior_word):
# 带有先验知识,否则进行正常抽样
k = numpy.random.choice(self.prior_word[w])
else:
# 计算theta值--下边的过程为抽取第m篇article的第n个词w的topic,即新的k
theta_p = (self.nd[m] + self.alpha) / (self.ndsum[m, 0] + self.sum_alpha)
# 计算phi值--判断是训练模型,还是推断模型(注意self.beta[w_g])
if self.local_2_global and self.train_model:
w_g = self.local_2_global[w]
phi_p = (self.train_model.nw[:, w_g] + self.nw[:, w] + self.beta[w_g]) / \
(self.train_model.nwsum[:, 0] + self.nwsum[:, 0] + self.sum_beta)
else:
phi_p = (self.nw[:, w] + self.beta[w]) / (self.nwsum[:, 0] + self.sum_beta)
# multi_p为多项式分布的参数,此时没有进行标准化
multi_p = theta_p * phi_p
# 此时的topic即为Gibbs抽样得到的topic,它有较大的概率命中多项式概率大的topic
k = LdaBase.multinomial_sample(multi_p)
# 统计计数加一
self.nd[m, k] += 1
self.ndsum[m, 0] += 1
self.nw[k, w] += 1
self.nwsum[k, 0] += 1
# 更新Z值
self.Z[m][n] = k
# 抽样完毕
return
# -----------------------------------------Model数据存储、读取相关函数-------------------------------------------------
def save_parameter(self, file_name):
"""
:key: 保存模型相关参数数据,包括: topics_num, M, V, K, words_count, alpha, beta
"""
with open(file_name, "w", encoding="utf-8") as f_param:
for item in ["topics_num", "M", "V", "K", "words_count"]:
f_param.write("%s\t%s\n" % (item, str(self.__dict__[item])))
f_param.write("alpha\t%s\n" % ",".join([str(item) for item in self.alpha]))
f_param.write("beta\t%s\n" % ",".join([str(item) for item in self.beta]))
return
def load_parameter(self, file_name):
"""
:key: 加载模型相关参数数据,和上一个函数相对应
"""
with open(file_name, "r", encoding="utf-8") as f_param:
for line in f_param:
key, value = line.strip().split()
if key in ["topics_num", "M", "V", "K", "words_count"]:
self.__dict__[key] = int(value)
elif key in ["alpha", "beta"]:
self.__dict__[key] = numpy.array([float(item) for item in value.split(",")])
return
def save_zvalue(self, file_name):
"""
:key: 保存模型关于article的变量,包括: arts_Z, Z, artids_list等
"""
with open(file_name, "w", encoding="utf-8") as f_zvalue:
for m in range(self.M):
out_line = [str(w) + ":" + str(k) for w, k in zip(self.arts_Z[m], self.Z[m])]
f_zvalue.write(self.artids_list[m] + "\t" + " ".join(out_line) + "\n")
return
def load_zvalue(self, file_name):
"""
:key: 读取模型的Z变量。和上一个函数相对应
"""
self.arts_Z = []
self.artids_list = []
self.Z = []
with open(file_name, "r", encoding="utf-8") as f_zvalue:
for line in f_zvalue:
frags = line.strip().split()
art_id = frags[0].strip()
w_k_list = [value.split(":") for value in frags[1:]]
# 添加到类中
self.artids_list.append(art_id)
self.arts_Z.append([int(item[0]) for item in w_k_list])
self.Z.append([int(item[1]) for item in w_k_list])
return
def save_twords(self, file_name):
"""
:key: 保存模型的twords数据,要用到phi的数据
"""
self.calculate_phi()
out_num = self.V if self.twords_num > self.V else self.twords_num
with open(file_name, "w", encoding="utf-8") as f_twords:
for k in range(self.K):
words_list = sorted([(w, self.phi[k, w]) for w in range(self.V)], key=lambda x: x[1], reverse=True)
f_twords.write("Topic %dth:\n" % k)
f_twords.writelines(["\t%s %f\n" % (self.local_bi.get_value(w), p) for w, p in words_list[:out_num]])
return
def load_twords(self, file_name):
"""
:key: 加载模型的twords数据,即先验数据
"""
self.prior_word.clear()
topic = -1
with open(file_name, "r", encoding="utf-8") as f_twords:
for line in f_twords:
if line.startswith("Topic"):
topic = int(line.strip()[6:-3])
else:
word_id = self.local_bi.get_key(line.strip().split()[0].strip())
self.prior_word[word_id].append(topic)
return
def save_tag(self, file_name):
"""
:key: 输出模型最终给数据打标签的结果,用到theta值
"""
self.calculate_theta()
with open(file_name, "w", encoding="utf-8") as f_tag:
for m in range(self.M):
f_tag.write("%s\t%s\n" % (self.artids_list[m], " ".join([str(item) for item in self.theta[m]])))
return
def save_model(self):
"""
:key: 保存模型数据
"""
name_predix = "%s-%05d" % (self.model_name, self.current_iter)
# 保存训练结果
self.save_parameter(os.path.join(self.dir_path, "%s.%s" % (name_predix, "param")))
self.save_wordmap(os.path.join(self.dir_path, "%s.%s" % (name_predix, "wordmap")))
self.save_zvalue(os.path.join(self.dir_path, "%s.%s" % (name_predix, "zvalue")))
#保存额外数据
self.save_twords(os.path.join(self.dir_path, "%s.%s" % (name_predix, "twords")))
self.save_tag(os.path.join(self.dir_path, "%s.%s" % (name_predix, "tag")))
return
def load_model(self):
"""
:key: 加载模型数据
"""
name_predix = "%s-%05d" % (self.model_name, self.current_iter)
# 加载训练结果
self.load_parameter(os.path.join(self.dir_path, "%s.%s" % (name_predix, "param")))
self.load_wordmap(os.path.join(self.dir_path, "%s.%s" % (name_predix, "wordmap")))
self.load_zvalue(os.path.join(self.dir_path, "%s.%s" % (name_predix, "zvalue")))
return
class LdaModel(LdaBase):
"""
LDA模型定义,主要实现训练、继续训练、推断的过程
"""
def init_train_model(self, dir_path, model_name, current_iter, iters_num=None, topics_num=10, twords_num=200,
alpha=-1.0, beta=0.01, data_file="", prior_file=""):
"""
:key: 初始化训练模型,根据参数current_iter(是否等于0)决定是初始化新模型,还是加载已有模型
:key: 当初始化新模型时,除了prior_file先验文件外,其余所有的参数都需要,且current_iter等于0
:key: 当加载已有模型时,只需要dir_path, model_name, current_iter(不等于0), iters_num, twords_num即可
:param iters_num: 可以为整数值或者“auto”
"""
if current_iter == 0:
logging.debug("init a new train model")
# 初始化语料集
self.init_corpus_with_file(data_file)
# 初始化部分变量
self.dir_path = dir_path
self.model_name = model_name
self.current_iter = current_iter
self.iters_num = iters_num
self.topics_num = topics_num
self.K = topics_num
self.twords_num = twords_num
# 初始化alpha和beta
self.alpha = numpy.array([alpha if alpha > 0 else (50.0/self.K) for k in range(self.K)])
self.beta = numpy.array([beta if beta > 0 else 0.01 for w in range(self.V)])
# 初始化Z值,以便统计计数
self.Z = [[numpy.random.randint(self.K) for n in range(len(self.arts_Z[m]))] for m in range(self.M)]
else:
logging.debug("init an existed model")
# 初始化部分变量
self.dir_path = dir_path
self.model_name = model_name
self.current_iter = current_iter
self.iters_num = iters_num
self.twords_num = twords_num
# 加载已有模型
self.load_model()
# 初始化统计计数
self.init_statistics()
# 计算alpha和beta的和值
self.sum_alpha_beta()
# 初始化先验知识
if prior_file:
self.load_twords(prior_file)
# 返回该模型
return self
def begin_gibbs_sampling_train(self, is_calculate_preplexity=True):
"""
:key: 训练模型,对语料集中的所有数据进行Gibbs抽样,并保存最后的抽样结果
"""
# Gibbs抽样
logging.debug("sample iteration start, iters_num: " + str(self.iters_num))
self.gibbs_sampling(is_calculate_preplexity)
logging.debug("sample iteration finish")
# 保存模型
logging.debug("save model")
self.save_model()
return
def init_inference_model(self, train_model):
"""
:key: 初始化推断模型
"""
self.train_model = train_model
# 初始化变量: 主要用到self.topics_num, self.K
self.topics_num = train_model.topics_num
self.K = train_model.K
# 初始化变量self.alpha, self.beta,直接沿用train_model的值
self.alpha = train_model.alpha # K维的float值,训练和推断模型中的K相同,故可以沿用
self.beta = train_model.beta # V维的float值,推断模型中用于计算phi的V值应该是全局的word的数量,故可以沿用
self.sum_alpha_beta() # 计算alpha和beta的和
# 初始化数据集的self.global_bi
self.global_bi = train_model.local_bi
return
def inference_data(self, article_list, iters_num=100, repeat_num=3):
"""
:key: 利用现有模型推断数据
:param article_list: 每一行的数据格式为: id[tab]word1 word2 word3......
:param iters_num: 每一次迭代的次数
:param repeat_num: 重复迭代的次数
"""
# 初始化语料集
self.init_corpus_with_articles(article_list)
# 初始化返回变量
return_theta = numpy.zeros((self.M, self.K))
# 重复抽样
for i in range(repeat_num):
logging.debug("inference repeat_num: " + str(i+1))
# 初始化变量
self.current_iter = 0
self.iters_num = iters_num
# 初始化Z值,以便统计计数
self.Z = [[numpy.random.randint(self.K) for n in range(len(self.arts_Z[m]))] for m in range(self.M)]
# 初始化统计计数
self.init_statistics()
# 开始推断
self.gibbs_sampling(is_calculate_preplexity=False)
# 计算theta
self.calculate_theta()
return_theta += self.theta
# 计算结果,并返回
return return_theta / repeat_num
if __name__ == "__main__":
"""
测试代码
"""
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s\t%(levelname)s\t%(message)s")
# train或者inference
test_type = "train"
# test_type = "inference"
# 测试新模型
if test_type == "train":
model = LdaModel()
# 由prior_file决定是否带有先验知识
model.init_train_model("data/", "model", current_iter=0, iters_num="auto", topics_num=10, data_file="corpus.txt")
# model.init_train_model("data/", "model", current_iter=0, iters_num="auto", topics_num=10, data_file="corpus.txt", prior_file="prior.twords")
model.begin_gibbs_sampling_train()
elif test_type == "inference":
model = LdaModel()
model.init_inference_model(LdaModel().init_train_model("data/", "model", current_iter=134))
data = [
"cn咪咕 漫画 咪咕 漫画 漫画 更名 咪咕 漫画 资源 偷星 国漫 全彩 日漫 实时 在线看 随心所欲 登陆 漫画 资源 黑白 全彩 航海王",
"coaircloud aircloud 硬件 设备 wifi 智能 手要 平板电脑 电脑 存储 aircloud 文件 远程 型号 aircloud 硬件 设备 wifi"
]
result = model.inference_data(data)
# 退出程序
exit()