def get_matches(df, col, input, option):
if option == 'Starts with':
cond = df[col].astype(str).str.lower().str.startswith(input.lower())
dfn = df[cond].copy()
dfn.sort_values(by=col, inplace=True, ignore_index=True)
elif option == 'Contains':
cond = df[col].astype(str).str.lower().str.contains(input.lower())
dfn = df[cond].copy()
dfn.sort_values(by=col, inplace=True, ignore_index=True)
elif option == 'Most similar':
dfn = match_strings(df[col].astype(str).drop_duplicates(),
pd.Series(input),
min_similarity=0.4)
cols = dfn.columns
dfn.sort_values(by='similarity',
ascending=False,
inplace=True,
ignore_index=True)
dfn = pd.merge(dfn, df, left_on='left_side', right_on=col)
dfn.drop(cols, axis=1, inplace=True)
return dfn
def calculate_fuzzymatches_for_min_similarity(
left: Union[pd.DataFrame, pd.Series],
right: Union[pd.DataFrame, pd.Series],
on: str = None,
left_on: str = None,
right_on: str = None,
min_similarity: float = None,
) -> pd.DataFrame:
if isinstance(left, pd.Series) and isinstance(right, pd.Series):
left_clean = left.drop_duplicates().pipe(clean_fuzzy_column)
right_clean = right.drop_duplicates().pipe(clean_fuzzy_column)
elif on is not None:
left_clean = left[on].drop_duplicates().pipe(clean_fuzzy_column)
right_clean = right[on].drop_duplicates().pipe(clean_fuzzy_column)
elif (left_on is not None) and (right_on is not None):
left_clean = left[left_on].drop_duplicates().pipe(clean_fuzzy_column)
right_clean = right[right_on].drop_duplicates().pipe(clean_fuzzy_column)
else:
raise ValueError("Unexpected condition...")
match = match_strings(left_clean, right_clean, min_similarity=min_similarity)
return match
def dupes_from_as(cfg):
column_to_match = 'name_concat'
dataset = pd.read_csv(cfg['input_csv'])
matches = match_strings(dataset[column_to_match])
# excludes matches with the same index, which are by definition the same.
match_subset = matches[matches['left_index'] != matches['right_index']]
match_subset.to_csv(cfg['output_csv'], index=False)
joined_subset = regroup_data(dataset, match_subset)
joined_subset.to_csv(cfg['output_csv_2'], index=False)
dropped_dupes = filter_mirrors(joined_subset)
dropped_dupes.to_csv(cfg['output_csv_3'], index=False)
dropped_subfields = remove_subfields(dropped_dupes)
dropped_subfields.to_csv(cfg['output_csv_4'], index=False)
def _get_lookup_matches(self, requested_words):
# Create a small set of artifical company names
search = pd.Series(pd.Series(requested_words).unique())
lookups = pd.Series(self.lookup["Word"].unique())
# Create all matches:
matches = match_strings(lookups,
search,
ngram_size=3,
min_similarity=float(
os.environ.get("LOOKUP_THRES_PRETAGGING")))
return matches
def dupes_from_wikidata(cfg):
wikidata_sublist = prep_wikidata_sublist(cfg)
for key, value in wikidata_sublist.items():
master_match = pd.DataFrame([key],
columns=['agent_uri', 'name_concat'])
master_match.set_index('agent_uri', inplace=True)
dupe_match = pd.DataFrame(
value,
columns=[
'index', 'num_matches', 'agent_uri', 'aay_url', 'name_concat',
'sort_name', 'dates', 'resources', 'archival_objects',
'accessions', 'authority_id', 'source', 'create_time',
'wikidata_uri', 'wikidata_name', 'wikidata_begin',
'wikidata_end'
])
dupe_match.set_index('index', inplace=True)
matches = match_strings(master_match['name_concat'],
dupe_match['wikidata_name'])
matches.to_csv(cfg.get('output_csv'),
mode='a',
header=False,
index=False)
words_detected = []
for m_id, start, end in shape_matches:
entity = doc[start:end]
words_detected.append(clean_text(entity.text))
entities_detected.append((entity.text, entity.start_char, entity.end_char,
nlp.vocab.strings[m_id], ""))
print(entity.text)
req_words = []
for t in doc:
if len(t.text
) > 2 and t.text != "break" and not t.is_stop and not clean_text(
t.text) in words_detected:
print(t.text)
req_words.append(t.text.lower())
# Create a small set of artifical company names
duplicates = pd.Series(pd.Series(req_words).unique())
string_words = pd.Series(result["Word"].unique())
# Create all matches:
matches = match_strings(string_words,
duplicates,
ngram_size=3,
min_similarity=0.4)
print(matches)
return False
strings = [x for x in strings if '[no title captured]' not in x]
articles = [x for x in strings if isarticle(x)]
books = [x for x in strings if not isarticle(x)]
print("%s articles, %s books to group" % (len(articles), len(books)))
# grouping books
# this cell may take quite a while to run.
# on Intel i7-9700F this runs in about a minute on 185k names.
books_grouped = string_grouper.match_strings(
pd.Series(books),
number_of_processes=8,
min_similarity=0.7
)
from collections import defaultdict
books_grouped[(books_grouped.similarity<1-1e-8)].sort_values("similarity")
# for books, we require that the authors are no more than 1 edit from each other
# even after limiting the comparisons necessary, this takes about 20s on Intel i7-9700F
ft = defaultdict(set)
for i,r in books_grouped.iterrows():
ls = r.left_side
rs = r.right_side
'/Users/haoranliu/match/Trademark/Clean_name/Clean/cleaned/ciq_id.json'
) as f:
ciq_id = json.load(f)
tma_assignor = pd.DataFrame()
tma_assignor['name'] = tma_assignor_name
tma_assignor['id'] = tma_assignor_id
ciq = pd.DataFrame()
ciq['name'] = ciq_name
ciq['id'] = ciq_id
# num = 0.9
# matches = match_strings(master = tma_assignor['name'], master_id = tma_assignor['id'], duplicates = ciq['name'], duplicates_id = ciq['id'], min_similarity = num)
# matches.to_stata(f'assignor_ciq{num}.dta', version = 117)
# num = 0.8
# matches = match_strings(master = tma_assignor['name'], master_id = tma_assignor['id'], duplicates = ciq['name'], duplicates_id = ciq['id'], min_similarity = num)
# matches.to_stata(f'assignor_ciq{num}.dta', version = 117)
num = 0.7
matches = match_strings(master=tma_assignor['name'],
master_id=tma_assignor['id'],
duplicates=ciq['name'],
duplicates_id=ciq['id'],
min_similarity=num)
matches.to_stata(f'assignor_ciq{num}.dta', version=117)
# num = 0.6
# matches = match_strings(master = tma_assignor['name'], master_id = tma_assignor['id'], duplicates = ciq['name'], duplicates_id = ciq['id'], min_similarity = num)
# matches.to_stata(f'assignor_ciq{num}.dta', version = 117)
#string_grouper.match_strings()
#(master, master_id, duplicates, duplicates_id, min_similarity)
def run(self):
from knowknow import pd, Counter, VariableNotFound
from collections import defaultdict
import string_grouper
import editdistance
# tracks the last group-id assigned
new_gid = 0
print(len(self.strings), 'strings total...')
def isarticle(x):
sp = x.split("|")
if len(sp) < 2:
return False
try:
int(sp[1])
return True
except ValueError:
return False
strings = [x for x in self.strings if '[no title captured]' not in x]
articles = [x for x in strings if isarticle(x)]
books = [x for x in strings if not isarticle(x)]
print('sample articles:', articles[:10])
print('sample books:', books[:10])
print("%s articles, %s books to group" % (len(articles), len(books)))
# grouping books
# this cell may take quite a while to run.
# on Intel i7-9700F this runs in about a minute on 185k names.
self.books_grouped = string_grouper.match_strings(
pd.Series(books), number_of_processes=8, min_similarity=0.7)
# for books, we require that the authors are no more than 1 edit from each other
# even after limiting the comparisons necessary, this takes about 20s on Intel i7-9700F
self.ft = defaultdict(set)
for i, r in self.books_grouped.iterrows():
ls = r.left_side
rs = r.right_side
if ls == rs:
continue
la = ls.split("|")[0]
ra = rs.split("|")[0]
if editdistance.eval(la, ra) > 1:
continue
self.ft[ls].add(rs)
self.ft[rs].add(ls)
print("%s books have some connection to others in a group" %
len(self.ft))
# assigns group-ids based on the relational structure derived thus far
# the code propagates ids through the network, assuming transitivity of equality
for i, k in enumerate(books):
if k in self.groups:
continue
self.traverse(k, new_gid)
new_gid += 1
print(len(set(self.groups.values())), 'groups total')
print(
Counter(gid for x, gid in self.groups.items()
if len(x.split("|")) == 2).most_common(10))
# grouping articles
# this cell may take quite a while to run.
# on Intel i7-9700F this runs in five minutes on 234k entries.
self.articles_grouped = string_grouper.match_strings(
pd.Series(articles),
number_of_processes=
8, # decrease this number to 1 or 2 for slower computers or laptops (the fan might start screaming)
min_similarity=
0.8 # the similarity cutoff is tighter for articles than for books
)
self.articles_grouped[(self.articles_grouped.similarity <
1 - 1e-8)].sort_values("similarity")
# for articles, we require that the entire citations is only 1 edit apart.
# even after limiting the comparisons necessary, this takes about 20s on Intel i7-9700F
# this cell produces the `ft` variable, which maps from each term to the set of terms equivalent. I.e., `ft[A] = {B1,B2,B3}`
self.ft = defaultdict(set)
for i, r in self.articles_grouped.iterrows():
ls = r.left_side
rs = r.right_side
if ls == rs:
continue
la = ls.split("|")[0]
ra = rs.split("|")[0]
if editdistance.eval(ls, rs) > 2:
continue
self.ft[ls].add(rs)
self.ft[rs].add(ls)
#print(ls,"|||",rs)
print("%s articles have some connection to others in a group" %
len(self.ft))
# assigns group-ids based on the relational structure derived thus far
# the code propagates ids through the network, assuming transitivity of equality
for i, k in enumerate(articles):
if k in self.groups:
continue
self.traverse(k, new_gid)
new_gid += 1
# this line will break execution if there aren't as many groups assigned as we have articles and books
assert (len(articles) + len(books) == len(self.groups))
print("%s books and %s articles total" % (len(books), len(articles)))
from collections import defaultdict
# saving the variable for later
self.dataset.save_variable("groups", self.groups)
self.dataset.save_variable("group_reps", self.get_reps())
def string_matcher(col1, col2, sim_thresh=0.95):
from string_grouper import match_strings, match_most_similar, group_similar_strings, StringGrouper
matches = match_strings(col1, col2, min_similarity=sim_thresh)
return matches
punti_search = func.test_search_functions(search_functions_to_test, possibilities, src.list_of_searches, src.categories, saveresults=True, savelog=True)
# %% codecell
# Plot dei grafici dei risultati
folder_log = os.path.join(folder,"log_ricerca")
func.plot_figure(folder_log, src.categories)
# %%
# Test per StringGrouper
import pandas as pd
import numpy as np
from string_grouper import match_strings, match_most_similar, group_similar_strings, StringGrouper
company_names = os.path.join(os.getcwd(),'sec_edgar_company_info.csv')
# We only look at the first 50k as an example
companies = pd.read_csv(company_names)[0:50000]
c = companies['Company Name']
# Create all matches:
matches = match_strings(companies['Company Name'])
# Look at only the non-exact matches:
matches[matches.left_side != matches.right_side].head()
# Create a small set of artifical company names
duplicates = pd.Series(['S MEDIA GROUP', '012 SMILE.COMMUNICATIONS', 'foo bar', 'B4UTRADE COM CORP'])
# Create all matches:
matches = match_strings(companies['Company Name'], duplicates)
matches
# Create a small set of artificial company names
new_companies = pd.Series(['S MEDIA GROUP', '012 SMILE.COMMUNICATIONS', 'foo bar', 'B4UTRADE COM CORP'])
# Create all matches:
matches = match_most_similar(companies['Company Name'], new_companies)
# Display the results:
pd.DataFrame({'new_companies': new_companies, 'duplicates': matches})
#%%