gifts_py

礼物

搜索具有查询具有共同特征的元素。

 query = [ 'A' , 'B' ]

elements = [
    [ 'N' , 'A' , 'M' ],  # common features: 'A'
    [ 'C' , 'B' , 'A' ],  # common features: 'A', 'B'  
    [ 'X' , 'Y' ]  # no common features
]

在这种情况下，返回['C', 'B', 'A']和['N', 'A', 'M']的搜索以该特定顺序。

用于全文搜索

查找包含查询单词的文档。

 from gifts import SmoothFts

fts = SmoothFts ()

fts . add ([ "wait" , "mister" , "postman" ],
        doc_id = "doc1" )

fts . add ([ "please" , "mister" , "postman" , "look" , "and" , "see" ],
        doc_id = "doc2" )

fts . add ([ "oh" , "yes" , "wait" , "a" , "minute" , "mister" , "postman" ],
        doc_id = "doc3" )

# print IDs of documents in which at least one word of the query occurs, 
# starting with the most relevant matches
for doc_id in fts . search ([ 'postman' , 'wait' ]):
    print ( doc_id )

用于抽象数据挖掘

在上面的示例中，单词实际上是字符串。但是它们可以是适合dict Keys的任何对象。

 from gifts import SmoothFts

fts = SmoothFts ()

fts . add ([ 3 , 1 , 4 , 1 , 5 , 9 , 2 ], doc_id = "doc1" )
fts . add ([ 6 , 5 , 3 , 5 ], doc_id = "doc2" )
fts . add ([ 8 , 9 , 7 , 9 , 3 , 2 ], doc_id = "doc3" )

for doc_id in fts . search ([ 5 , 3 , 7 ]):
    print ( doc_id )

实施详细信息

在排名结果时，算法会考虑::

• 匹配单词的数量
• 数据库中此类单词的稀有性
• 文档中单词出现的频率

光滑

 from gifts import SmoothFts

它使用对数TF-IDF来加权单词和余弦相似性来评分比赛。

简单

 from gifts import SimpleFts

简约方法：称重，乘以，比较。该对象明显比SmoothFts更快。

安装

pip

pip3 install git+https://github.com/rtmigo/gifts_py#egg=gifts

setup.py

 install_requires = [
    "gifts@ git+https://github.com/rtmigo/gifts_py"
]

参见

Skifts软件包进行了相同的搜索，但是使用Scikit-Learn和Numpy来更好地性能。实际上，它更快了数百次。