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add python_lda

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xianhu 2016-10-21 15:47:10 +08:00
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### python_spider.py: 一个很“水”的Python爬虫入门代码文件
### python_weibo.py: “史上最详细”的Python模拟登录新浪微博流程
### python_lda.py: 玩点高级的--带你入门Topic模型LDA（小改进+附源码）
============================================================
### 您可以fork该项目,并在修改后提交Pull request

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# _*_ 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",
"co aircloud 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()

4
python_weibo.py
View File

@ -1,5 +1,9 @@
# _*_ coding: utf-8 _*_
"""
python_weibo.py by xianhu
"""
import re
import rsa
import ssl