重复数据删除蟒蛇 - "Records do not line up with data model"

Question

我一直在设置python和来自dedupe.io的库dedupe，以消除postgres数据库中的一组条目的重复。错误是-">记录与数据模型不一致"，这应该很容易解决，但我不明白为什么会收到此消息。

我现在有什么(集中代码和删除其他功能)

# ## 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()

错误消息为"记录与数据模型不一致。字段'id'在data_model中，但不在记录中"。正如你所看到的，我定义了5个需要"学习"的领域。我使用的查询正好返回这5列及其数据。的输出

print(temp_d)

是

{0: ('00Q1o00000OjmQmEAJ', '[phone]', None, '01561', None), 1: ('00Q1o00000JhgSUEAZ', None, '[phone]', '27729', '[e-mail]')}

在我看来，这是重复数据消除库的有效输入。

我尝试了什么

• 我检查了他是否已经写了一个文件作为训练集以某种方式读取并使用，但事实并非如此(代码甚至会说它)
• 我尝试调试"重复数据消除器"对象，其中字段和诸如此类的输入，我可以看到字段的定义
• 看看其他的例子，比如csv或mysql，它们和我做的差不多

请指出我错的方向。

Answer 1

看起来问题可能是您的temp_d是元组字典，而不是字典字典的预期输入。我刚开始使用这个软件包，并在这里找到了一个适用于我的示例，它提供了设置字典的功能，尽管是从csv而不是从你的数据中提取。

data_d = {}
with open(filename) as f:
reader = csv.DictReader(f)
for row in reader:
clean_row = [(k, preProcess(v)) for (k, v) in row.items()]
row_id = int(row['Id'])
data_d[row_id] = dict(clean_row)
return data_d