def fuzzy_restrictions(outfile,
metadata,
corrections,
data_path,
threshold=95):
with open(outfile, 'wb') as outfile:
writer = csv.writer(outfile,
delimiter=',',
quotechar='\'',
quoting=csv.QUOTE_MINIMAL)
lookup = yule_k.create_metadata_lookup(metadata)
correction_lookup = yule_k.create_correction_lookup(corrections)
seen_books = set()
for filename in yule_k.retrieve_tsvs(data_path):
htid = re.sub('\.tsv', '', os.path.split(filename)[1], count=1)
if lookup[htid]['title'] not in seen_books:
print filename
fuzzy_matches = [
title for title in seen_books if fuzz.token_set_ratio(
title, lookup[htid]['title']) >= threshold
]
if not fuzzy_matches:
seen_books.add(lookup[htid]['title'])
tsv_table = yule_k.read_tsv_file(filename,
correction_lookup)
k = yule_k.calculate_k([val for val in tsv_table.values()])
writer.writerow([
htid, lookup[htid]['title'], lookup[htid]['author'],
lookup[htid]['date'], k
])
def as_single_corpus(metadata, corrections, data_path):
corpus_table = defaultdict(int)
lookup = yule_k.create_metadata_lookup(metadata)
correction_lookup = yule_k.create_correction_lookup(corrections)
for filename in yule_k.retrieve_tsvs(data_path):
print filename
htid = re.sub('\.tsv', '', os.path.split(filename)[1], count=1)
for item, val in yule_k.read_tsv_file(filename, correction_lookup).items():
corpus_table[item] += val
print yule_k.calculate_k([val for val in corpus_table.values()])
def non_fuzzy(outfile, metadata, corrections, data_path):
with open(outfile, 'wb') as outfile:
writer = csv.writer(outfile, delimiter=',',quotechar='\'', quoting=csv.QUOTE_MINIMAL)
lookup = yule_k.create_metadata_lookup(metadata)
correction_lookup = yule_k.create_correction_lookup(corrections)
for filename in yule_k.retrieve_tsvs(data_path):
print filename
htid = re.sub('\.tsv', '', os.path.split(filename)[1], count=1)
tsv_table = yule_k.read_tsv_file(filename, correction_lookup)
k = yule_k.calculate_k([val for val in tsv_table.values()])
writer.writerow([htid, lookup[htid]['title'],lookup[htid]['author'],lookup[htid]['date'], k])
def as_single_corpus(metadata, corrections, data_path):
corpus_table = defaultdict(int)
lookup = yule_k.create_metadata_lookup(metadata)
correction_lookup = yule_k.create_correction_lookup(corrections)
for filename in yule_k.retrieve_tsvs(data_path):
print filename
htid = re.sub('\.tsv', '', os.path.split(filename)[1], count=1)
for item, val in yule_k.read_tsv_file(filename,
correction_lookup).items():
corpus_table[item] += val
print yule_k.calculate_k([val for val in corpus_table.values()])
def non_fuzzy(outfile, metadata, corrections, data_path):
with open(outfile, 'wb') as outfile:
writer = csv.writer(outfile,
delimiter=',',
quotechar='\'',
quoting=csv.QUOTE_MINIMAL)
lookup = yule_k.create_metadata_lookup(metadata)
correction_lookup = yule_k.create_correction_lookup(corrections)
for filename in yule_k.retrieve_tsvs(data_path):
print filename
htid = re.sub('\.tsv', '', os.path.split(filename)[1], count=1)
tsv_table = yule_k.read_tsv_file(filename, correction_lookup)
k = yule_k.calculate_k([val for val in tsv_table.values()])
writer.writerow([
htid, lookup[htid]['title'], lookup[htid]['author'],
lookup[htid]['date'], k
])
def fuzzy_restrictions(outfile, metadata, corrections, data_path, threshold=95):
with open(outfile, 'wb') as outfile:
writer = csv.writer(outfile, delimiter=',',quotechar='\'', quoting=csv.QUOTE_MINIMAL)
lookup = yule_k.create_metadata_lookup(metadata)
correction_lookup = yule_k.create_correction_lookup(corrections)
seen_books = set()
for filename in yule_k.retrieve_tsvs(data_path):
htid = re.sub('\.tsv', '', os.path.split(filename)[1], count=1)
if lookup[htid]['title'] not in seen_books:
print filename
fuzzy_matches = [title for title in seen_books if fuzz.token_set_ratio(title, lookup[htid]['title']) >= threshold]
if not fuzzy_matches:
seen_books.add(lookup[htid]['title'])
tsv_table = yule_k.read_tsv_file(filename, correction_lookup)
k = yule_k.calculate_k([val for val in tsv_table.values()])
writer.writerow([htid, lookup[htid]['title'],lookup[htid]['author'],lookup[htid]['date'], k])