python - 重复数据删除 Python - "Records do not line up with data model"

Question

我坚持设置 python 和 dedupe.io 中的库 dedupe 以对 postgres 数据库中的一组条目进行重复数据删除。错误是 - “记录与数据模型不一致”，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()

错误消息是“记录与数据模型不一致。字段‘id’在数据模型中但不在记录中”。如您所见，我定义了 5 个要“学习”的字段。我正在使用的查询准确地返回这 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，这像是去重库的有效输入。

我尝试过的

• 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阅读和使用，情况并非如此(代码甚至会说它)
• 我尝试调试“deduper”对象，其中的定义字段等等进去，我可以看到字段定义
• 查看其他示例，如 csv 或 mysql，它们的功能与我几乎相同。

请指出错误的方向。

Answer 1

看起来问题可能在于您的 temp_d 是元组字典，而不是字典字典的预期输入。我刚开始使用这个包并找到了一个例子 here这适用于我的目的，它提供了这个功能来设置字典，尽管是从 csv 而不是你的数据拉取。

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