def get_umls_data():
umls_data = pd.read_csv(umls_df_data_path)
print(f"Got UMLS data at length {len(umls_data)}")
acronyms_umls_df = pd.read_csv(acronyms_dir + os.sep + 'acronyms_terms.csv')
umls_data = pd.concat([umls_data, acronyms_umls_df])
cuiless_umls_df = pd.read_csv(cuiless_dir + os.sep + 'cuiless_terms.csv')
umls_data = pd.concat([umls_data, cuiless_umls_df])
umls_data.reset_index(inplace=True)
heb_umls_list = list(umls_data['HEB'].values)
eng_umls_list = list(umls_data[STRING_COLUMN].values)
heb_db = DictDatabase(CharacterNgramFeatureExtractor(2))
eng_db = DictDatabase(CharacterNgramFeatureExtractor(2))
for heb_w in heb_umls_list:
heb_db.add(heb_w)
for eng_w in eng_umls_list:
lower_eng_w = eng_w.lower()
eng_db.add(lower_eng_w)
return heb_db, eng_db, umls_data
def test_features(self):
self.assertEqual(CharacterNgramFeatureExtractor().features('abcde'),
['$a_1', 'ab_1', 'bc_1', 'cd_1', 'de_1', 'e$_1'])
self.assertEqual(
CharacterNgramFeatureExtractor(3).features('abcde'),
['$$a_1', '$ab_1', 'abc_1', 'bcd_1', 'cde_1', 'de$_1', 'e$$_1'])
self.assertEqual(
CharacterNgramFeatureExtractor().features(u'あいうえお'),
['$あ_1', 'あい_1', 'いう_1', 'うえ_1', 'えお_1', 'お$_1']) # Japanese
def __init__(self, n_chars, n_words, special_words):
self.n_chars = n_chars
self.special_words = special_words
self.__char_feature_extractor = CharacterNgramFeatureExtractor(n_chars)
if type(n_words) != list:
self.__word_feature_extractors = [WordNgramFeatureExtractor(n_words)]
else:
self.__word_feature_extractors = [
WordNgramFeatureExtractor(n)
for n in n_words
]
def output_similar_strings_of_each_line(path, measure):
strings = []
with open(path, "r") as lines:
for line in lines:
strings.append(line.rstrip("\r\n"))
db = DictDatabase(CharacterNgramFeatureExtractor(2))
for string in strings:
db.add(string)
db.save("companies.db")
dbl = DictDatabase.load("companies.db")
searcher = Searcher(dbl, measure)
profiler.start()
for string in strings:
result = searcher.search(string, 0.8)
# result = [str(np.round(x[0], 5)) + ' ' + x[1] for x in searcher.ranked_search(string, 0.8)]
# print("\t".join([string, ",".join(result)]))
profiler.stop()
profiler.print()
profiler.open_in_browser()
def ssdb_build(strs,
dbname,
ngram_length=DEFAULT_NGRAM_LENGTH,
include_marks=DEFAULT_INCLUDE_MARKS):
"""Given a list of strings, a DB name, and simstring options, builds a
simstring DB for the strings."""
__import_simstring()
dbfn = __ssdb_path(dbname)
try:
# only library defaults (n=3, no marks) supported just now (TODO)
assert include_marks == False, "Error: begin/end marks not supported"
if SIMSTRING_BINARY:
assert ngram_length == 3, "Error: unsupported n-gram length"
db = simstring.writer(dbfn)
for s in strs:
db.insert(s)
db.close()
else:
fx = CharacterNgramFeatureExtractor(DEFAULT_NGRAM_LENGTH)
db = SQLite3Database(fx)
db.use(dbfn)
for s in strs:
db.add(s)
except BaseException:
print >> sys.stderr, "Error building simstring DB"
raise
return dbfn
def create_umls_ss_db(umls_kb, char_ngram_len=3, n_max_tokens=5):
logging.info('Loading scispacy ...')
import spacy
self.sci_nlp = spacy.load('en_core_sci_md',
disable=['tagger', 'parser', 'ner'])
simstring_db = DictDatabase(CharacterNgramFeatureExtractor(char_ngram_len))
# preprocessing aliases and labels
logging.info('Preprocessing aliases ...')
alias_mapping = defaultdict(set)
aliases = []
for cui in umls_kb.get_all_cuis():
cui_aliases = set(
[a.lower() for a in umls_kb.get_aliases(cui, include_name=True)])
for alias in cui_aliases:
alias_chars = set(alias)
if len(alias_chars.intersection(fb_punctuation)) > 0:
continue
elif alias in en_stopwords:
continue
elif alias.isnumeric():
continue
alias_doc = self.sci_nlp(
alias) # use same tokenizer as when splitting medmentions
if len(alias_doc
) > n_max_tokens: # gets too big without restrictions
continue
alias_mapping[alias].add(cui)
aliases.append(alias)
logging.info('Adding to DB ...')
for alias_idx, alias in enumerate(aliases):
simstring_db.add(alias)
if alias_idx % 1000000 == 0:
logging.info('At %d/%d ...' % (alias_idx, len(aliases)))
# setting paths
db_path = '%s.aliases.%dgram.%dtoks.db' % (umls_kb.umls_version,
char_ngram_len, n_max_tokens)
map_path = '%s.aliases.%dtoks.map' % (umls_kb.umls_version, n_max_tokens)
logging.info('Storing DB ...')
with open(db_path, 'wb') as f:
pickle.dump(simstring_db, f)
logging.info('Storing Alias Mapping ...')
with open(map_path, 'wb') as f:
alias_mapping = dict(alias_mapping)
pickle.dump(alias_mapping, f)
def __init__(self, word2vec):
self.w2v = word2vec
self.embedding_dim = self.w2v.vector_size
self.vocab = set(self.w2v.vocab.keys())
self.db = DictDatabase(CharacterNgramFeatureExtractor(2))
for vocab_word in self.vocab:
self.db.add(vocab_word)
def setUp(self) -> None:
db = DictDatabase(CharacterNgramFeatureExtractor(2))
db.add("foo")
db.add("bar")
db.add("fooo")
db.add("food")
db.add("fool")
db.add("follow")
self.searcher = Searcher(db, JaccardMeasure())
class GESSyntacticFeatureExtractor(BaseFeatureExtractor):
def __init__(self, n_chars, n_words, special_words):
self.n_chars = n_chars
self.special_words = special_words
self.__char_feature_extractor = CharacterNgramFeatureExtractor(n_chars)
if type(n_words) != list:
self.__word_feature_extractors = [WordNgramFeatureExtractor(n_words)]
else:
self.__word_feature_extractors = [
WordNgramFeatureExtractor(n)
for n in n_words
]
def features(self, string):
# lower y unicode
normalized_string = string.lower()
normalized_string = unidecode(normalized_string)
# saca símbolos de puntuación
normalized_string = re.sub(r'[,;()/+ -]+',' ',normalized_string)
# elimina la información que no es necesaria (correspondiente a la edad)
# 'en personas de'
normalized_string = re.sub(r'en +personas +(de|desde)?',' ', normalized_string)
# 'mayores de XX anos y mas'
normalized_string = re.sub(r'(mayor(es)?( *de)?)? *\d+ +anos?( +y +mas)?', ' ', normalized_string)
# 'desde XX anos y menores de XX anos' 'menores de XX anos'
normalized_string = re.sub(r'(desde +\d+ +anos? +y)? *menores +de +\d+ +anos?', ' ', normalized_string)
# 'de XX a XX anos'
normalized_string = re.sub(r'de +\d+ a +\d+ anos?', ' ', normalized_string)
# saca los ' y '
normalized_string = re.sub(' y ', ' ', normalized_string)
# saca los espacios de mas
normalized_string = re.sub(' +', ' ', normalized_string)
normalized_string = normalized_string.strip()
# obtiene las características de caracteres
char_features = self.__char_feature_extractor.features(normalized_string)
# obtiene las características de palabras
word_features = []
for extractor in self.__word_feature_extractors:
word_features += extractor.features(normalized_string)
# obtiene características de palabras especiales
special_features = []
for word in self.special_words:
re_word = f'(^{word})|( +{word} +)|({word}$)'
if re.search(re_word, normalized_string):
special_features.append(word)
return char_features + word_features + special_features
def simstring_database(umls, nchar_val):
db = DictDatabase(CharacterNgramFeatureExtractor(nchar_val))
term_to_cui = dict()
for value in umls:
try:
data = value.split('\t')
cui = data[0]
term = data[1].lower()
db.add(term)
term_to_cui[term] = cui
except:
continue
pickle.dump(db, open('db.pickle', 'wb'))
pickle.dump(term_to_cui, open('term_to_cui.pickle', 'wb'))
def output_similar_strings_of_each_line(path):
db = DictDatabase(CharacterNgramFeatureExtractor(2))
with open(path, 'r') as lines:
for line in lines:
strings = line.rstrip('\r\n')
db.add(strings)
searcher = Searcher(db, CosineMeasure())
with open(path, 'r') as lines:
for line in lines:
strings = line.rstrip('\r\n')
result = [
str(round(x[0], 5)) + ' ' + x[1]
for x in searcher.ranked_search(strings, 0.8)
]
print("\t".join([strings, ",".join(result)]))
def ssdb_open(dbname):
"""Given a DB name, opens it as a simstring DB and returns the handle.
The caller is responsible for invoking close() on the handle.
"""
__import_simstring()
try:
if SIMSTRING_BINARY:
return simstring.reader(__ssdb_path(dbname))
else:
fx = CharacterNgramFeatureExtractor(DEFAULT_NGRAM_LENGTH)
db = SQLite3Database(fx)
return db.use(__ssdb_path(dbname))
except IOError:
Messager.error('Failed to open simstring DB %s' % dbname)
raise ssdbNotFoundError(dbname)
def construct_ontology(ontology_data):
'''
Create an n-char simstring database and
term-to-code mapping to enable rapid ontology
querying
'''
database = DictDatabase(CharacterNgramFeatureExtractor(2))
term_to_cui = {}
for entry in ontology_data:
entry_values = entry.split('\t')
if len(entry_values) == 2:
term = clean_selected_term(entry_values[1])
term_to_cui[term] = entry_values[0].strip()
for term in term_to_cui.keys():
term = clean_selected_term(term)
database.add(term)
return database, term_to_cui
def output_similar_strings_of_each_line(path, Database):
number_of_lines = len(open(path).readlines())
with Benchmarker(width=20) as bench:
db = Database(CharacterNgramFeatureExtractor(2))
@bench("initialize database({0} lines)".format(number_of_lines))
def _(bm):
with open(path, 'r') as lines:
for line in lines:
strings = line.rstrip('\r\n')
db.add(strings)
@bench("search text({0} times)".format(min(number_of_lines, SEARCH_COUNT_LIMIT)))
def _(bm):
searcher = Searcher(db, CosineMeasure())
with open(path, 'r') as lines:
for i, line in enumerate(lines):
if i >= SEARCH_COUNT_LIMIT:
break
strings = line.rstrip('\r\n')
result = searcher.search(strings, 0.8)
def __init__(self,
from_dir: str = None,
json_path: str = "mapper.json",
umls_words: Iterable[str] = None):
# self.db = DictDatabase(WordNgramFeatureExtractor(2))
self.db = DictDatabase(CharacterNgramFeatureExtractor(2))
if from_dir:
json_path = os.path.join(from_dir, json_path)
if os.path.exists(json_path):
print(f"initialize {self.__class__.__name__}... Load json")
self.umls_dict, self.umls_reverse_dict = self.load_from_json(
json_path)
self.add_words_to_db(self.umls_dict.keys())
else:
print(f"initialize {self.__class__.__name__}... Load dir")
self.umls_dict, self.umls_reverse_dict = self.load_umls_dict(
from_dir)
self.add_words_to_db(self.umls_dict.keys())
self.save_as_json(path=json_path)
else:
self.add_words_to_db(umls_words)
def setUp(self):
self.db = MongoDatabase(CharacterNgramFeatureExtractor(2), database='simstring-test')
self.db.reset_collection()
for string in self.strings:
self.db.add(string)
def test_features(self):
self.assertEqual(CharacterNgramFeatureExtractor().features('abcde'), [' a', 'ab', 'bc', 'cd', 'de', 'e '])
self.assertEqual(CharacterNgramFeatureExtractor(3).features('abcde'), [' ab', 'abc', 'bcd', 'cde', 'de '])
self.assertEqual(CharacterNgramFeatureExtractor(4).features('abcde'), [' abc', 'abcd', 'bcde', 'cde '])
self.assertEqual(CharacterNgramFeatureExtractor(5).features('abcde'), [' abcd', 'abcde', 'bcde '])
self.assertEqual(CharacterNgramFeatureExtractor().features(u'あいうえお'), [' あ', 'あい', 'いう', 'うえ', 'えお', 'お ']) # Japanese
def setUp(self):
db = DictDatabase(CharacterNgramFeatureExtractor(2))
for string in self.strings:
db.add(string)
self.searcher = Searcher(db, CosineMeasure())
def setUp(self):
self.db = DictDatabase(CharacterNgramFeatureExtractor(2))
for string in self.strings:
self.db.add(string)
#############################
# Perform database cleaning #
#############################
# Read in branded foods CSV and clean it
df = pd.read_csv('branded_food.csv')
all_ingredients_final = get_cleaned_ingredients_list(df)
# Get a count for all the ingredients to be used by Peter Norvig Implementation
ingredients_count = Counter(all_ingredients_final)
##############################################
# Peter Norvig SimString Implementation Code #
##############################################
# Populate database with all ingredients
db = DictDatabase(CharacterNgramFeatureExtractor(2))
for ingredient in all_ingredients_final:
db.add(ingredient)
# Create searcher object to be used by candidates function
searcher = Searcher(db, CosineMeasure())
# Functions
def probability(word, N=sum(ingredients_count.values())):
"""
Returns the probability of the word appearing in the text
Usually, correctly spelled words will have a higher count and therefore probability than their mispellings
"""
return ingredients_count[word] / N
def make_change_image_dict(drink_names):
import re
import json
import difflib
from simstring.feature_extractor.character_ngram import CharacterNgramFeatureExtractor
from simstring.measure.cosine import CosineMeasure
from simstring.database.dict import DictDatabase
from simstring.searcher import Searcher
ff = open('jsons/theCocktailDB_allData_20181010.json', 'r', encoding="utf-8_sig")
json_data2 = json.load(ff)
ff.close()
# 互いに類似度を比較する文字列のリスト
STR_db = [re.sub(r'[!-/:-@[-`{-~]', " ", d["en"]) for d in drink_names]
TCD_db ={re.sub(r'[!-/:-@[-`{-~]', " ", d["drinks"][0]["strDrink"]): d["drinks"][0]["strDrinkThumb"] for d in json_data2}
TCD_name_db = list(TCD_db.keys())
count = 0
length = len(STR_db)
result_dict = {}
change_image_dict = {}
db = DictDatabase(CharacterNgramFeatureExtractor(2))
for str1 in STR_db:
db.add(str1)
for str2 in TCD_name_db:
result_dict[str2] = {}
searcher = Searcher(db, CosineMeasure())
i = 1.0
# 類似度を計算、0.0~1.0 で結果が返る
flag = False
for str1 in STR_db:
s = difflib.SequenceMatcher(None, str2, str1).ratio()
if s > 0.75:
flag = True
if (str1 in result_dict[str2]):
d = result_dict[str2][str1]
#平均更新
d = [(d[0]*d[1]+s)/(d[1]+1), d[1]+1]
result_dict[str2][str1] = d
else:
result_dict[str2].setdefault(str1, [s ,1])
temp = []
while i >= 0.65:
result = searcher.search(str2, i)
if (len(result)):
flag = True
for str1 in result:
if (str1 in temp): continue
temp += [str1]
if (str1 in result_dict[str2]):
d = result_dict[str2][str1]
#平均更新
d = [(d[0]*d[1]+i)/(d[1]+1), d[1]+1]
result_dict[str2][str1] = d
else:
result_dict[str2].setdefault(str1, [i ,1])
i -= 0.001
if (flag):
count += 1
with open("./search_log.txt", "w+", encoding="utf-8_sig") as f:
real_count = 0
for str2 in TCD_name_db:
print("\n", file=f)
print("\n")
print(">> "+str2, file=f)
print(">> "+str2)
M = 0.0
name = ""
for key, value_list in result_dict[str2].items():
if (M < value_list[0]):
name = key
M = value_list[0]
print(" "+name+": "+str(M), file=f)
if (M != 0):
if (M >= 0.76):
print(" "+name+": "+str(M))
print("ok", file=f)
print("ok")
change_image_dict[name] = TCD_db[str2]
real_count += 1
else:
print(" "+name+": "+str(M))
print("out", file=f)
print("out")
print("\nmatch is {count}/{length} but real_match is {real_count}/{length}".format(count=count, real_count=real_count, length=length), file=f)
print("\nmatch is {count}/{length} but real_match is {real_count}/{length}".format(count=count, real_count=real_count, length=length))
exit()
return change_image_dict
def main(argv):
arg = argparser().parse_args(argv[1:])
# only simstring library default supported at the moment (TODO)
if SIMSTRING_BINARY:
assert DEFAULT_NGRAM_LENGTH == 3, "Error: unsupported n-gram length"
assert DEFAULT_INCLUDE_MARKS == False, "Error: begin/end marks not supported"
infn = arg.file
if arg.database is None:
# default database file name
bn = splitext(basename(infn))[0]
sqldbfn = sqldb_filename(bn)
ssdbfn = ssdb_filename(bn)
else:
sqldbfn = arg.database + '.' + SQL_DB_FILENAME_EXTENSION
ssdbfn = arg.database + '.' + SS_DB_FILENAME_EXTENSION
if arg.verbose:
print("Storing SQL DB as %s and" % sqldbfn, file=sys.stderr)
print(" simstring DB as %s" % ssdbfn, file=sys.stderr)
start_time = datetime.now()
import_count, duplicate_count, error_count, simstring_count = 0, 0, 0, 0
with codecs.open(infn, 'rU', encoding=arg.encoding) as inf:
# create SQL DB
try:
connection = sqlite.connect(sqldbfn)
except sqlite.OperationalError as e:
print("Error connecting to DB %s:" % sqldbfn, e, file=sys.stderr)
return 1
cursor = connection.cursor()
# create SQL tables
if arg.verbose:
print("Creating tables ...", end=' ', file=sys.stderr)
for command in CREATE_TABLE_COMMANDS:
try:
cursor.execute(command)
except sqlite.OperationalError as e:
print("Error creating %s:" % sqldbfn,
e,
"(DB exists?)",
file=sys.stderr)
return 1
# import data
if arg.verbose:
print("done.", file=sys.stderr)
print("Importing data ...", end=' ', file=sys.stderr)
next_eid = 1
label_id = {}
next_lid = 1
next_pid = dict([(t, 1) for t in TYPE_VALUES])
for i, l in enumerate(inf):
l = l.rstrip('\n')
# parse line into ID and TYPE:LABEL:STRING triples
try:
id_, rest = l.split('\t', 1)
except ValueError:
if error_count < MAX_ERROR_LINES:
print(
"Error: skipping line %d: expected tab-separated fields, got '%s'"
% (i + 1, l),
file=sys.stderr)
elif error_count == MAX_ERROR_LINES:
print(
"(Too many errors; suppressing further error messages)",
file=sys.stderr)
error_count += 1
continue
# parse TYPE:LABEL:STRING triples
try:
triples = []
for triple in rest.split('\t'):
type_, label, string = triple.split(':', 2)
if type_ not in TYPE_VALUES:
print("Unknown TYPE %s" % type_, file=sys.stderr)
triples.append((type_, label, string))
except ValueError:
if error_count < MAX_ERROR_LINES:
print(
"Error: skipping line %d: expected tab-separated TYPE:LABEL:STRING triples, got '%s'"
% (i + 1, rest),
file=sys.stderr)
elif error_count == MAX_ERROR_LINES:
print(
"(Too many errors; suppressing further error messages)",
file=sys.stderr)
error_count += 1
continue
# insert entity
eid = next_eid
next_eid += 1
try:
cursor.execute("INSERT into entities VALUES (?, ?)",
(eid, id_))
except sqlite.IntegrityError as e:
if error_count < MAX_ERROR_LINES:
print("Error inserting %s (skipping): %s" % (id_, e),
file=sys.stderr)
elif error_count == MAX_ERROR_LINES:
print(
"(Too many errors; suppressing further error messages)",
file=sys.stderr)
error_count += 1
continue
# insert new labels (if any)
labels = set([l for t, l, s in triples])
new_labels = [l for l in labels if l not in label_id]
for label in new_labels:
lid = next_lid
next_lid += 1
cursor.execute("INSERT into labels VALUES (?, ?)",
(lid, label))
label_id[label] = lid
# insert associated strings
for type_, label, string in triples:
table = TABLE_FOR_TYPE[type_]
pid = next_pid[type_]
next_pid[type_] += 1
lid = label_id[label] # TODO
if TABLE_HAS_NORMVALUE[table]:
normstring = string_norm_form(string)
cursor.execute(
"INSERT into %s VALUES (?, ?, ?, ?, ?)" % table,
(pid, eid, lid, string, normstring))
else:
cursor.execute(
"INSERT into %s VALUES (?, ?, ?, ?)" % table,
(pid, eid, lid, string))
import_count += 1
if arg.verbose and (i + 1) % 10000 == 0:
print('.', end=' ', file=sys.stderr)
if arg.verbose:
print("done.", file=sys.stderr)
# create SQL indices
if arg.verbose:
print("Creating indices ...", end=' ', file=sys.stderr)
for command in CREATE_INDEX_COMMANDS:
try:
cursor.execute(command)
except sqlite.OperationalError as e:
print("Error creating index", e, file=sys.stderr)
return 1
if arg.verbose:
print("done.", file=sys.stderr)
# wrap up SQL table creation
connection.commit()
# create simstring DB
if arg.verbose:
print("Creating simstring DB ...", end=' ', file=sys.stderr)
try:
if SIMSTRING_BINARY:
ssdb = simstring.writer(ssdbfn)
for row in cursor.execute(SELECT_SIMSTRING_STRINGS_COMMAND):
s = row[0]
ssdb.insert(s)
simstring_count += 1
ssdb.close()
else:
sys.path.append(
join(dirname(abspath(__file__)), '..', 'server', 'src'))
from simstring_pure_sqlite3 import SQLite3Database
from simstring.feature_extractor.character_ngram import CharacterNgramFeatureExtractor
from simstring.searcher import Searcher
fx = CharacterNgramFeatureExtractor(DEFAULT_NGRAM_LENGTH)
ssdb = SQLite3Database(fx)
ssdb.use(connection)
for row in cursor.execute(SELECT_SIMSTRING_STRINGS_COMMAND):
s = row[0]
ssdb.add(s)
simstring_count += 1
except BaseException:
print("Error building simstring DB", file=sys.stderr)
raise
if arg.verbose:
print("done.", file=sys.stderr)
cursor.close()
# done
delta = datetime.now() - start_time
if arg.verbose:
print(file=sys.stderr)
print("Done in:",
str(delta.seconds) + "." + str(delta.microseconds / 10000),
"seconds",
file=sys.stderr)
print(
"Done, imported %d entries (%d strings), skipped %d duplicate keys, skipped %d invalid lines"
% (import_count, simstring_count, duplicate_count, error_count))
return 0