def init_linker(data_fields, training_data_file, training_settings_file, doc_data, per_data, sample_size, training_size):
if training_settings_file:
try:
with open(training_settings_file, 'rb') as f:
linker = StaticRecordLink(f)
log.info('Read settings from {}'.format(training_settings_file))
return linker
except FileNotFoundError:
pass
linker = RecordLink(data_fields)
if training_data_file:
try:
with open(training_data_file) as f:
linker.readTraining(f)
log.info('Read training data from {}'.format(training_data_file))
except FileNotFoundError:
pass
log.info('Generating training data')
linker.sample(doc_data, per_data, sample_size=sample_size)
linker.markPairs(trainingDataLink(doc_data, per_data, common_key='person', training_size=training_size))
linker.train()
if training_data_file:
log.info('Writing training data to {}'.format(training_data_file))
with open(training_data_file, 'w+') as fp:
linker.writeTraining(fp)
if training_settings_file:
log.info('Writing settings data to {}'.format(training_settings_file))
with open(training_settings_file, 'wb+') as fp:
linker.writeSettings(fp)
return linker
except ValueError :
clean_row['price'] = 0
data_d[filename + str(i)] = dict(clean_row)
return data_d
print 'importing data ...'
data_1 = readData('AbtBuy_Abt.csv')
data_2 = readData('AbtBuy_Buy.csv')
# These data have already been linked by hand, and linked records
# share the value of the field 'unique_id'. We are exploiting this work
# to create training data. This function also assume that if all pairs of
# records that DO NOT share a 'unique_id' value are distinct.
training_pairs = dedupe.trainingDataLink(data_1, data_2, 'unique_id', 5000)
# ## Training
if os.path.exists(settings_file):
print 'reading from', settings_file
linker = dedupe.StaticRecordLink(settings_file)
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 'Zip' field.
fields = {
'title': {'type': 'String'},
print('found duplicate')
print(len(found_dupes))
print('precision')
print(1 - len(false_positives) / float(len(found_dupes)))
print('recall')
print(len(true_positives) / float(len(true_dupes)))
settings_file = 'canonical_data_matching_learned_settings'
data_1, header = canonicalImport('tests/datasets/restaurant-1.csv')
data_2, _ = canonicalImport('tests/datasets/restaurant-2.csv')
training_pairs = dedupe.trainingDataLink(data_1, data_2, 'unique_id', 5000)
duplicates_s = set(frozenset(pair) for pair in training_pairs['match'])
t0 = time.time()
print('number of known duplicate pairs', len(duplicates_s))
if os.path.exists(settings_file):
with open(settings_file, 'rb') as f:
deduper = dedupe.StaticRecordLink(f)
else:
fields = [{
'field': 'name',
'type': 'String'
}, {
StructField("manufacturer", StringType(), True),
StructField("family", StringType(), True),
StructField("model", StringType(), True),
]
products = sqlContext.read.json(PRODUCTS_PATH, StructType(product_fields))\
.fillna({'family': ''}) # replace nulls in family fields
products_df, products_dict = canonical_format(products, Product)
listings_df, listings_dict = canonical_format(listings, Listing)
products_training_dict = json.load(open(PRODUCTS_TRAINING_PATH))
listings_training_dict = json.load(open(LISTINGS_TRAINING_PATH))
# train model
gazetteer.sample(products_dict, listings_dict, 10000)
training_pairs = trainingDataLink(products_training_dict, listings_training_dict, 'labelled_id', 10)
gazetteer.markPairs(training_pairs)
gazetteer.train()
# add products to the index of records to match against
if not gazetteer.blocked_records:
gazetteer.index(products_dict)
alpha = gazetteer.threshold(listings_dict, recall_weight=.5)
# identify records that all refer to the same entity
print('clustering...')
clustered_dupes = gazetteer.match(listings_dict, threshold=alpha)
debug(clustered_dupes)
