全新Gensim4.0代码实战(01)-安装与快速上手
2021-01-23 本文已影响0人
致Great
本节代码地址：https://www.kesci.com/mw/project/600ade02e455800015b7e609
Gensim简介
Image Name
- 专门训练词向量的Python接口。 Gensim中的核心算法使用了核心算力，高度优化和并行化的C例程。
- Gensim可以使用数据流算法处理任意大的语料库。没有“数据集必须适合RAM”的限制。
- Gensim可在Linux，Windows和OS X以及任何其他支持Python和NumPy的平台上运行。
- 每天都有成千上万的公司使用Gensim，每周有2600多个学术引用和100万次下载，Gensim是最成熟的ML库之一。
- Gensim的所有源代码均由GNU LGPL许可证托管在Github上，并由其开源社区维护。有关商业安排，请参阅业务支持。
- Gensim社区还通过Gensim-data项目发布了针对特定领域（例如法律或健康）的预训练模型。
Gensim安装
安装非常简单；一种是pip另外可以通过conda安装：
pip install --upgrade gensimconda install -c conda-forge gensim
In [8]:
!pip install gensim==4.0.0b0 -i https://pypi.tuna.tsinghua.edu.cn/simple
安装成功
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting gensim==4.0.0b0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d0/86/2d03cb418e9fae6dbc17bafd7524d407be0691a703829cecca23e2bc31a9/gensim-4.0.0b0-cp38-cp38-manylinux1_x86_64.whl (24.0 MB)
|████████████████████████████████| 24.0 MB 8.7 MB/s eta 0:00:01
Requirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.8/site-packages (from gensim==4.0.0b0) (1.19.1)
Requirement already satisfied: smart-open>=1.8.1 in /opt/conda/lib/python3.8/site-packages (from gensim==4.0.0b0) (4.1.2)
Requirement already satisfied: scipy>=0.18.1 in /opt/conda/lib/python3.8/site-packages (from gensim==4.0.0b0) (1.5.2)
Installing collected packages: gensim
Attempting uninstall: gensim
Found existing installation: gensim 3.8.3
Uninstalling gensim-3.8.3:
Successfully uninstalled gensim-3.8.3
Successfully installed gensim-4.0.0b0
快速上手
Gensim相关的概念：
- 文档:一些文本。
- 语料库:文档的集合。
- 向量:一种数学上方便的文档表示。
- 模型:一种将向量从一种表示形式转换为另一种表示形式的算法
import pprint
文档 Document
document = "Human machine interface for lab abc computer applications"
语料 Corpus
text_corpus = [
"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",
]
# Create a set of frequent words
stoplist = set('for a of the and to in'.split(' '))
# Lowercase each document, split it by white space and filter out stopwords
texts = [[word for word in document.lower().split() if word not in stoplist]
for document in text_corpus]
# Count word frequencies
from collections import defaultdict
frequency = defaultdict(int)
for text in texts:
for token in text:
frequency[token] += 1
# Only keep words that appear more than once
processed_corpus = [[token for token in text if frequency[token] > 1] for text in texts]
pprint.pprint(processed_corpus)
from gensim import corpora
dictionary = corpora.Dictionary(processed_corpus)
print(dictionary)
Dictionary(12 unique tokens: ['computer', 'human', 'interface', 'response', 'survey']...)
向量 Vector
pprint.pprint(dictionary.token2id)
{'computer': 0,
'eps': 8,
'graph': 10,
'human': 1,
'interface': 2,
'minors': 11,
'response': 3,
'survey': 4,
'system': 5,
'time': 6,
'trees': 9,
'user': 7}
doc2bow将token转换为id表示，（第一代表单词的索引，第二个代表出现的次数）
new_doc = "Human computer interaction"
new_vec = dictionary.doc2bow(new_doc.lower().split())
print(new_vec)
[(0, 1), (1, 1)]
接下来我们表示所有的文档
bow_corpus = [dictionary.doc2bow(text) for text in processed_corpus]
pprint.pprint(bow_corpus)
[[(0, 1), (1, 1), (2, 1)],
[(0, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)],
[(2, 1), (5, 1), (7, 1), (8, 1)],
[(1, 1), (5, 2), (8, 1)],
[(3, 1), (6, 1), (7, 1)],
[(9, 1)],
[(9, 1), (10, 1)],
[(9, 1), (10, 1), (11, 1)],
[(4, 1), (10, 1), (11, 1)]]
模型 Model
导入Tfidf模型
from gensim import models
# train the model
tfidf = models.TfidfModel(bow_corpus)
# transform the "system minors" string
words = "system minors".lower().split()
print(tfidf[dictionary.doc2bow(words)])
我们可以得到对应词语索引的tfidf值
[(5, 0.5898341626740045), (11, 0.8075244024440723)]
使用tfidf表示所有语料
from gensim import similarities
index = similarities.SparseMatrixSimilarity(tfidf[bow_corpus], num_features=12)
计算查询文档中与所有语料中文档的相似性
query_document = 'system engineering'.split()
query_bow = dictionary.doc2bow(query_document)
sims = index[tfidf[query_bow]]
print(list(enumerate(sims)))
得到所有的相似性
[(0, 0.0), (1, 0.32448703), (2, 0.41707572), (3, 0.7184812), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0)]
for document_number, score in sorted(enumerate(sims), key=lambda x: x[1], reverse=True):
print(document_number, score)
3 0.7184812
2 0.41707572
1 0.32448703
0 0.0
4 0.0
5 0.0
6 0.0
7 0.0
8 0.0
