我正在处理一个记录链接问题,并应用无监督算法,因为我没有外部标签。
我已经应用了ECM算法。使用的代码为:
import recordlinkage
indexer = recordlinkage.BlockIndex(on=['FirstName_CD','LastName_CD'])
pairs = indexer.index(data1, data2)
compare_cl = recordlinkage.Compare()
compare_cl.string('FirstName_CD', 'FirstName_CD', method='jarowinkler', threshold=0.50,label='given_name')
compare_cl.string('LastName_CD', 'LastName_CD', method='jarowinkler', threshold=0.50, label='surname')
compare_cl.exact('Date.Of.Birth_CD', 'Date.Of.Birth_CD', label='date_of_birth')
compare_cl.exact('Gender_CD', 'Gender_CD', label='gender')
compare_cl.exact('Profession_CD', 'Profession_CD', label='profession')
compare_cl.string('Address_CD', 'Address_CD', threshold=0.85, label='address_1')
features = compare_cl.compute(pairs,data1)
ecm = recordlinkage.ECMClassifier()
result_ecm=ecm.learn(features)
现在它返回一个多索引。我的问题是,我可以从中得出什么推论?如何获取匹配/不匹配信息?
发布于 2019-06-12 03:35:06
ecm.learn()
返回的MultiIndex基本上是一堆索引对,它们是分类器对哪些项匹配的猜测。(请注意,ecm.learn()
已弃用。新函数名为ecm.fit_predict()
。)
我不知道您的数据是什么样子,但这里有一个示例数据集:
from recordlinkage.datasets import load_febrl4
dfA, dfB = load_febrl4()
# Indexation step
indexer = recordlinkage.Index()
indexer.block('given_name')
candidate_links = indexer.index(dfA, dfB)
# Comparison step
compare_cl = recordlinkage.Compare()
compare_cl.string('surname', 'surname', method='jarowinkler', threshold=0.85, label='surname')
compare_cl.exact('date_of_birth', 'date_of_birth', label='date_of_birth')
compare_cl.exact('suburb', 'suburb', label='suburb')
compare_cl.exact('state', 'state', label='state')
compare_cl.string('address_1', 'address_1', threshold=0.85, label='address_1')
features = compare_cl.compute(candidate_links, dfA, dfB)
ecm = recordlinkage.ECMClassifier()
matches = ecm.fit_predict(features)
(请注意,此示例基于here的文档中的示例。)
matches对象实际上是一个Pandas MultiIndex。我们可以将其转换为元组列表,以便更好地了解它包含的信息。
# Look at the first 5 matches
list(matches)[:5]
[('rec-2371-org', 'rec-2371-dup-0'),
('rec-3024-org', 'rec-3024-dup-0'),
('rec-4652-org', 'rec-4652-dup-0'),
('rec-4795-org', 'rec-4795-dup-0'),
('rec-1016-org', 'rec-1016-dup-0')]
这些是dfA
和dfB
数据帧中的索引名称。我们可以查找它们,看看它们实际上是匹配的:
dfA.loc['rec-2371-org'], dfB.loc['rec-2371-dup-0']
given_name michaela
surname dunstone
street_number 37
address_1 deane street
address_2 rosedown
suburb woodcroft
postcode 2065
state vic
date_of_birth 19121018
soc_sec_id 3166178
Name: rec-2371-org, dtype: object
given_name michaela
surname dunstone
street_number 37
address_1 deane street
address_2 rosedlwn
suburb woodcroft
postcode 2065
state vic
date_of_birth 19121018
soc_sec_id 3166178
Name: rec-2371-dup-0, dtype: object
发布于 2018-10-21 00:36:30
我也找不到此方法的信息,但据我所知,ecm_learn
返回一个MultiIndex,它与indexer.index()
方法返回的pairs
的数据类型相同。
所以,这就是我使用它的方式(顺便说一句,我可能完全错了!)
import recordlinkage
indexer = recordlinkage.BlockIndex(on=['FirstName_CD','LastName_CD'])
pairs = indexer.index(data1, data2)
compare_cl = recordlinkage.Compare()
compare_cl.string('FirstName_CD', 'FirstName_CD', method='jarowinkler', threshold=0.50,label='given_name')
compare_cl.string('LastName_CD', 'LastName_CD', method='jarowinkler', threshold=0.50, label='surname')
compare_cl.exact('Date.Of.Birth_CD', 'Date.Of.Birth_CD', label='date_of_birth')
compare_cl.exact('Gender_CD', 'Gender_CD', label='gender')
compare_cl.exact('Profession_CD', 'Profession_CD', label='profession')
compare_cl.string('Address_CD', 'Address_CD', threshold=0.85, label='address_1')
features = compare_cl.compute(pairs,data1)
ecm = recordlinkage.ECMClassifier()
result_ecm=ecm.learn(features)
#reprocess the compute() call with the newly adjusted match information
features = compare_cl.compute(result_ecm,data1)
#now, do your stuff..
#...
如果其他人有进一步的信息,我将非常感谢您的反馈。
谢谢!
https://stackoverflow.com/questions/50508739
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