本文主要是介绍lda模型：官方处理方式和自己处理数据对比，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
自己处理数据，然后分批训练，第一步先对比自己处理的方式和官方是否一致。
官方的代码
import gensim
from gensim import corpora
from gensim.models import LdaModel# 示例数据
documents = ["Human machine interface for lab abc computer applications","A survey of user opinion of computer system response time","The EPS user interface management system","System and human system engineering testing of EPS","Relation of user perceived response time to error measurement","The generation of random binary unordered trees","The intersection graph of paths in trees","Graph minors IV Widths of trees and well quasi ordering","Graph minors A survey"
]# 预处理数据
texts = [[word for word in document.lower().split()] for document in documents]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]# 训练 LDA 模型
lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary, passes=15, random_state=2024)# 打印每个主题的关键词
for idx, topic in lda_model.print_topics(-1):print(f"Topic: {idx}\nWords: {topic}\n")# 推断新文档的主题分布
new_doc = "Human computer interaction"
new_doc_processed = [word for word in new_doc.lower().split()]
new_doc_bow = dictionary.doc2bow(new_doc_processed)
print(new_doc_bow)
print("New document topic distribution:", lda_model.get_document_topics(new_doc_bow))
结果
Topic: 0
Words: 0.078*"graph" + 0.078*"trees" + 0.078*"the" + 0.078*"of" + 0.078*"in" + 0.078*"intersection" + 0.078*"paths" + 0.013*"minors" + 0.013*"interface" + 0.013*"survey"Topic: 1
Words: 0.062*"of" + 0.034*"measurement" + 0.034*"relation" + 0.034*"to" + 0.034*"error" + 0.034*"perceived" + 0.034*"lab" + 0.034*"applications" + 0.034*"for" + 0.034*"machine"Topic: 2
Words: 0.062*"minors" + 0.062*"trees" + 0.062*"the" + 0.062*"binary" + 0.062*"random" + 0.062*"generation" + 0.062*"unordered" + 0.062*"a" + 0.062*"survey" + 0.062*"graph"Topic: 3
Words: 0.134*"system" + 0.073*"human" + 0.073*"eps" + 0.073*"and" + 0.073*"of" + 0.073*"engineering" + 0.073*"testing" + 0.012*"time" + 0.012*"user" + 0.012*"response"Topic: 4
Words: 0.090*"of" + 0.090*"user" + 0.090*"system" + 0.049*"computer" + 0.049*"response" + 0.049*"time" + 0.049*"survey" + 0.049*"a" + 0.049*"interface" + 0.049*"management"[(2, 1), (4, 1)]
New document topic distribution: [(0, 0.066698), (1, 0.7288686), (2, 0.06669144), (3, 0.06943816), (4, 0.068303764)]print(dictionary.token2id)'''
{'abc': 0, 'applications': 1, 'computer': 2, 'for': 3, 'human': 4, 'interface': 5, 'lab': 6, 'machine': 7, 'a': 8, 'of': 9, 'opinion': 10, 'response': 11, 'survey': 12, 'system': 13, 'time': 14, 'user': 15, 'eps': 16, 'management': 17, 'the': 18, 'and': 19, 'engineering': 20, 'testing': 21, 'error': 22, 'measurement': 23, 'perceived': 24, 'relation': 25, 'to': 26, 'binary': 27, 'generation': 28, 'random': 29, 'trees': 30, 'unordered': 31, 'graph': 32, 'in': 33, 'intersection': 34, 'paths': 35, 'iv': 36, 'minors': 37, 'ordering': 38, 'quasi': 39, 'well': 40, 'widths': 41}
'''print(corpus)'''
[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)], [(2, 1), (8, 1), (9, 2), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1)], [(5, 1), (13, 1), (15, 1), (16, 1), (17, 1), (18, 1)], [(4, 1), (9, 1), (13, 2), (16, 1), (19, 1), (20, 1), (21, 1)], [(9, 1), (11, 1), (14, 1), (15, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1)], [(9, 1), (18, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1)], [(9, 1), (18, 1), (30, 1), (32, 1), (33, 1), (34, 1), (35, 1)], [(9, 1), (19, 1), (30, 1), (32, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1)], [(8, 1), (12, 1), (32, 1), (37, 1)]]
'''自己处理方式
def get_dictionary(input_data):output_dict = {}count = 0for l in input_data:l_list = l.strip().lower().split(" ")sorted_l_list = sorted(l_list)for k in sorted_l_list:if k not in output_dict:output_dict[k] = countcount += 1return output_dictmy_dict = get_dictionary(documents)
print(my_dict)def get_corpus(input_dict, input_data):output_list = []for l in input_data:tmp_dict = {}l_list = l.strip().lower().split(" ")for k in l_list:if k not in tmp_dict:tmp_dict[k] = 0tmp_dict[k] += 1tmp_list = []for k, v in tmp_dict.items():if k in input_dict.keys():tmp_list.append((input_dict[k], v))else:continueoutput_list.append(sorted(tmp_list))return output_listmy_corpus = get_corpus(my_dict, documents)
print(my_corpus)def get_predict_corpus(input_dict, input_data):tmp_dict = {}l_list = input_data.strip().lower().split(" ")for k in l_list:if k not in tmp_dict:tmp_dict[k] = 0tmp_dict[k] += 1tmp_list = []for k, v in tmp_dict.items():if k in input_dict.keys():tmp_list.append((input_dict[k], v))else:continuereturn sorted(tmp_list)'''
{'abc': 0, 'applications': 1, 'computer': 2, 'for': 3, 'human': 4, 'interface': 5, 'lab': 6, 'machine': 7, 'a': 8, 'of': 9, 'opinion': 10, 'response': 11, 'survey': 12, 'system': 13, 'time': 14, 'user': 15, 'eps': 16, 'management': 17, 'the': 18, 'and': 19, 'engineering': 20, 'testing': 21, 'error': 22, 'measurement': 23, 'perceived': 24, 'relation': 25, 'to': 26, 'binary': 27, 'generation': 28, 'random': 29, 'trees': 30, 'unordered': 31, 'graph': 32, 'in': 33, 'intersection': 34, 'paths': 35, 'iv': 36, 'minors': 37, 'ordering': 38, 'quasi': 39, 'well': 40, 'widths': 41}
[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)], [(2, 1), (8, 1), (9, 2), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1)], [(5, 1), (13, 1), (15, 1), (16, 1), (17, 1), (18, 1)], [(4, 1), (9, 1), (13, 2), (16, 1), (19, 1), (20, 1), (21, 1)], [(9, 1), (11, 1), (14, 1), (15, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1)], [(9, 1), (18, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1)], [(9, 1), (18, 1), (30, 1), (32, 1), (33, 1), (34, 1), (35, 1)], [(9, 1), (19, 1), (30, 1), (32, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1)], [(8, 1), (12, 1), (32, 1), (37, 1)]]
'''my_dict == dictionary.token2id'''
True
'''my_corpus == corpus'''
True
'''
# 训练 LDA 模型
my_lda_model = LdaModel(my_corpus, num_topics=5, passes=15, random_state=2024)
print(my_lda_model)# 打印每个主题的关键词
for idx, topic in my_lda_model.print_topics(-1):print(f"Topic: {idx}\nWords: {topic}\n")# 推断新文档的主题分布
new_doc = "Human computer interaction"
new_doc_bow = get_predict_corpus(my_dict, new_doc)
print(new_doc_bow)
print("New document topic distribution:", lda_model.get_document_topics(new_doc_bow))
结果
LdaModel<num_terms=42, num_topics=5, decay=0.5, chunksize=2000>
Topic: 0
Words: 0.078*"32" + 0.078*"30" + 0.078*"18" + 0.078*"9" + 0.078*"33" + 0.078*"34" + 0.078*"35" + 0.013*"37" + 0.013*"5" + 0.013*"12"Topic: 1
Words: 0.062*"9" + 0.034*"23" + 0.034*"25" + 0.034*"26" + 0.034*"22" + 0.034*"24" + 0.034*"6" + 0.034*"1" + 0.034*"3" + 0.034*"7"Topic: 2
Words: 0.062*"37" + 0.062*"30" + 0.062*"18" + 0.062*"27" + 0.062*"29" + 0.062*"28" + 0.062*"31" + 0.062*"8" + 0.062*"12" + 0.062*"32"Topic: 3
Words: 0.134*"13" + 0.073*"4" + 0.073*"16" + 0.073*"19" + 0.073*"9" + 0.073*"20" + 0.073*"21" + 0.012*"14" + 0.012*"15" + 0.012*"11"Topic: 4
Words: 0.090*"9" + 0.090*"15" + 0.090*"13" + 0.049*"2" + 0.049*"11" + 0.049*"14" + 0.049*"12" + 0.049*"8" + 0.049*"5" + 0.049*"17"[(2, 1), (4, 1)]
New document topic distribution: [(0, 0.06669798), (1, 0.72894156), (2, 0.06669143), (3, 0.06936743), (4, 0.06830162)]这篇关于lda模型：官方处理方式和自己处理数据对比的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
