我坚持从 dedupe.io 设置 python 和库 dedupe 以对 postgres 数据库中的一组条目进行重复数据删除。错误是 - “记录不符合数据模型”这应该很容易解决，但我只是不明白为什么会收到此消息。
我现在拥有的（重点代码并删除了其他功能）
# ## Setup
settings_file = 'lead_dedupe_settings'
training_file = 'lead_dedupe_training.json'
start_time = time.time()
...
def training():
# We'll be using variations on this following select statement to pull
# in campaign donor info.
#
# We did a fair amount of preprocessing of the fields in
""" Define Lead Query """
sql = "select id, phone, mobilephone, postalcode, email from dev_manuel.somedata"
# ## Training
if os.path.exists(settings_file):
print('reading from ', settings_file)
with open(settings_file, 'rb') as sf:
deduper = dedupe.StaticDedupe(sf, num_cores=4)
else:
# Define the fields dedupe will pay attention to
#
# The address, city, and zip fields are often missing, so we'll
# tell dedupe that, and we'll learn a model that take that into
# account
fields = [
{'field': 'id', 'type': 'ShortString'},
{'field': 'phone', 'type': 'String', 'has missing': True},
{'field': 'mobilephone', 'type': 'String', 'has missing': True},
{'field': 'postalcode', 'type': 'ShortString', 'has missing': True},
{'field': 'email', 'type': 'String', 'has missing': True}
]
# Create a new deduper object and pass our data model to it.
deduper = dedupe.Dedupe(fields, num_cores=4)
# connect to db and execute
conn = None
try:
# read the connection parameters
params = config()
# connect to the PostgreSQL server
conn = psycopg2.connect(**params)
print('Connecting to the PostgreSQL database...')
cur = conn.cursor()
# excute sql
cur.execute(sql)
temp_d = dict((i, row) for i, row in enumerate(cur))
print(temp_d)
deduper.sample(temp_d, 10000)
print('Done stage 1')
del temp_d
# close communication with the PostgreSQL database server
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
print('Closed Connection')
# If we have training data saved from a previous run of dedupe,
# look for it an load it in.
#
# __Note:__ if you want to train from
# scratch, delete the training_file
if os.path.exists(training_file):
print('reading labeled examples from ', training_file)
with open(training_file) as tf:
deduper.readTraining(tf)
# ## Active learning
print('starting active labeling...')
# Starts the training loop. Dedupe will find the next pair of records
# it is least certain about and ask you to label them as duplicates
# or not.
# debug
print(deduper)
# vars(deduper)
# use 'y', 'n' and 'u' keys to flag duplicates
# press 'f' when you are finished
dedupe.convenience.consoleLabel(deduper)
# When finished, save our labeled, training pairs to disk
with open(training_file, 'w') as tf:
deduper.writeTraining(tf)
# Notice our argument here
#
# `recall` is the proportion of true dupes pairs that the learned
# rules must cover. You may want to reduce this if your are making
# too many blocks and too many comparisons.
deduper.train(recall=0.90)
with open(settings_file, 'wb') as sf:
deduper.writeSettings(sf)
# We can now remove some of the memory hobbing objects we used
# for training
deduper.cleanupTraining()
错误消息是“记录不符合数据模型。字段 'id' 在 data_model 但不在记录中”。如您所见，我正在定义要“学习”的 5 个字段。我正在使用的查询准确地返回了这 5 列以及其中的数据。的输出
print(temp_d)
是
{0: ('00Q1o00000OjmQmEAJ', '+4955555555', None, '01561', None), 1: ('00Q1o00000JhgSUEAZ', None, '+4915555555', '27729', 'email@aemail.de')}
在我看来，这就像重复数据删除库的有效输入。
我试过的
- 我检查了他是否已经编写了一个文件作为训练集，该文件会以某种方式被读取和使用，事实并非如此（代码甚至会这样说）
- 我尝试调试字段定义等进入的“deduper”对象，我可以看到字段定义
- 查看其他示例，例如 csv 或 mysql，它们的功能与我几乎相同。
请指出我错的方向。