使用库中的重复数据删除记录链接并不断收到有关“公司名称”字段的错误在数据模型中但不在记录中。我已经两次和三次检查它是否在两者中都没有错别字。谁能指出我正确的方向？
这就是我现在所拥有的：
import os
import csv
import re
import logging
import optparse
import dedupe
from unidecode import unidecode
#Do a little bit of data cleaning with the help of Unidecode and Regex. Things like casing, extra spaces, quotes and new lines can be ignored.
def preProcess(column):
#
column = unidecode(column)
column = re.sub('\n', ' ', column)
column = re.sub('-', '', column)
column = re.sub('/', ' ', column)
column = re.sub("'", '', column)
column = re.sub(",", '', column)
column = re.sub(":", ' ', column)
column = re.sub(' +', ' ', column)
column = column.strip().strip('"').strip("'").lower().strip()
if not column:
column = None
return column
#Read in our data from a CSV file and create a dictionary of records, where the key is a unique record ID.
def readData(filename):
data_d = {}
with open(filename, encoding='UTF-8') as f:
reader = csv.DictReader(f)
for i, row in enumerate(reader):
clean_row = dict([(k, preProcess(v)) for (k, v) in row.items()])
data_d[filename + str(i)] = dict(clean_row)
return data_d
if __name__ == '__main__':
#Logging
#dedupe uses Python logging to show or suppress verbose output. Added for convenience. To enable verbose logging, run python examples/csv_example/csv_example.py -v
optp = optparse.OptionParser()
optp.add_option('-v', '--verbose', dest='verbose', action='count',
help='Increase verbosity (specify multiple times for more)'
)
(opts, args) = optp.parse_args()
log_level = logging.WARNING
if opts.verbose:
if opts.verbose == 1:
log_level = logging.INFO
elif opts.verbose >= 2:
log_level = logging.DEBUG
logging.getLogger().setLevel(log_level)
#Setup
output_file = 'data_matching_output.csv'
settings_file = 'data_matching_learned_settings'
training_file = 'data_matching_training.json'
left_file = 'imports/MSOR_sample.csv'
right_file = 'imports/import.csv'
print('importing data ...')
data_1 = readData(left_file)
data_2 = readData(right_file)
#
def descriptions():
for dataset in (data_1, data_2):
for record in dataset.values():
yield record['description']
#Training
if os.path.exists(settings_file):
print('reading from', settings_file)
with open(settings_file, 'rb') as sf:
linker = dedupe.StaticRecordLink(sf)
else:
#Define the fields the linker will pay attention to
#Notice how we are telling the linker to use a custom field comparator for the ‘price’ field.
fields = [
{'field': 'companyname', 'type': 'String'}]
#Create a new linker object and pass our data model to it.
linker = dedupe.RecordLink(fields)
#If we have training data saved from a previous run of linker, 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:
linker.prepare_training(data_1,
data_2,
training_file=tf,
sample_size=15000)
else:
linker.prepare_training(data_1, data_2, sample_size=15000)
#Active learning
#Dedupe will find the next pair of records it is least certain about and ask you to label them as matches or not. use ‘y’, ‘n’ and ‘u’ keys to flag duplicates press ‘f’ when you are finished
print('starting active labeling...')
dedupe.console_label(linker)
linker.train()
#When finished, save our training away to disk
with open(training_file, 'w') as tf:
linker.write_training(tf)
#Save our weights and predicates to disk. If the settings file exists, we will skip all the training and learning next time we run this file.
with open(settings_file, 'wb') as sf:
linker.write_settings(sf)
#Blocking
#Clustering
#Find the threshold that will maximize a weighted average of our precision and recall. When we set the recall weight to 2, we are saying we care twice as much about recall as we do precision.
#If we had more data, we would not pass in all the blocked data into this function but a representative sample.
print('clustering...')
linked_records = linker.join(data_1, data_2, 0.0)
print('# duplicate sets', len(linked_records))
#Writing Results
#Write our original data back out to a CSV with a new column called ‘Cluster ID’ which indicates which records refer to each other.
cluster_membership = {}
for cluster_id, (cluster, score) in enumerate(linked_records):
for record_id in cluster:
cluster_membership[record_id] = {'Cluster ID': cluster_id,
'Link Score': score}
with open(output_file, 'w') as f:
header_unwritten = True
for fileno, filename in enumerate((left_file, right_file)):
with open(filename) as f_input:
reader = csv.DictReader(f_input)
if header_unwritten:
fieldnames = (['Cluster ID', 'Link Score', 'source file'] +
reader.fieldnames)
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
header_unwritten = False
for row_id, row in enumerate(reader):
record_id = filename + str(row_id)
cluster_details = cluster_membership.get(record_id, {})
row['source file'] = fileno
row.update(cluster_details)
writer.writerow(row)
记录样本：
{'companyname': 'dji america', 'website': 'dji.com'}
不完全确定断开连接的位置。任何帮助表示赞赏。