def test_phonetic_tokenize_name_python2():
"""Test checking if custom phonetic algorithms from fuzzy packages work."""
import fuzzy
soundex = fuzzy.Soundex(5)
assert phonetic_tokenize_name("Dupont, René", "nysiis") == (
((fuzzy.nysiis(u"Dupont"),), (fuzzy.nysiis(u"René"),)))
assert phonetic_tokenize_name("Dupont, René", "soundex") == (
# no direct support for unicode in soundex, thus "Rene"
((soundex(u"Dupont"),), (soundex(u"Rene"),)))
def calc_sim(candidate, poem, sim_range):
inrange = [] #lines within sim_range
if len(poem) <= sim_range:
inrange = poem
else:
inrange = poem[-sim_range:]
score = 0
for line in poem:
for tok in line:
for can_tok in candidate:
score += pylev.distance( fuzzy.nysiis(tok), fuzzy.nysiis(can_tok))
return score
def phx(test):
print '--> phx'
print test
# 1
soundex = fuzzy.Soundex(5)
print soundex(test)
# 2
dmeta = fuzzy.DMetaphone()
print dmeta(test)
# 3
print fuzzy.nysiis(test)
def filter(self, tokens, replace): """ Filters the tokens. *tokens* (``list``) the tokens (``str``). *replace* (``bool``) is whether tokens created by this filter should replace the encountered tokens (``True``), or if they should be combined (i.e., appended after) the encountered tokens. Default is ``False``. Returns the filtered (``list``) tokens (``str``). """ truncate = self.truncate found = set(t[0] for t in tokens) if not replace else set() final = [] for token in tokens: if not replace: # Since we are not supposed to replace tokens, append each token # before it is consumed. final.append(token) # Run token through nysiis. result = fuzzy.nysiis(token[0])[:truncate] if result and result not in found: found.add(result) final.append((result, token[1], token[2], token[3])) return final
def phonetic_similarity(poem):
# Phonetic representation of each line
phonetic_lines = []
for line in poem:
phon_line = []
for word in line:
phon_line.append(fuzzy.nysiis(word))
phonetic_lines.append(phon_line)
# Calculate phonetic value
phon_value = 0.1
for i in range(0, len(phonetic_lines)-1):
last_word = str(phonetic_lines[i][-1])
next_last_word = str(phonetic_lines[i+1][-1])
# Throw out identical sounding words (TODO: Check their original words, not phonetic representations)
if last_word != next_last_word and len(last_word) > 0 and len(next_last_word) > 0:
lev_dist = pylev.distance(last_word, next_last_word)
# Divide distance by word length since eg morning - mourning is a way better similarity than hat - had
lev_dist = lev_dist/float((len(last_word) + len(next_last_word)))
phon_value += lev_dist
#print 'Potential rhyme pair: '+last_word +' '+next_last_word+' with dist: '+str(lev_dist)
else:
phon_value += 1 # very unsimilar for two words that are too short
# Normalize by poem length (in case variable length poems are allowed)
phon_value /= len(phonetic_lines)
return phon_value
def score_nysiis(words):
"""Score words using the nysiis algorithm.
:param words (list): the list of words.
:rtype scores (list): the scored words
"""
return ['{}: {}'.format(w.lower(), fuzzy.nysiis(w)) for w in words]
def Realm2Slug(region, realm):
global realm2slug
if not region in realm2slug:
realm2slug[region] = LookupRegionSlugs(region)
if not realm in realm2slug[region]:
# Lets see if we can find an alias for it
realm = fuzzy.nysiis(realm)
return realm2slug[region].get(realm)
def initialize():
global wordlist
words = []
with open('/usr/share/dict/words') as f:
words = f.readlines()
for w in words:
if not w[0].isupper():
wordlist.append( (w, fuzzy.nysiis(w)) )
def find_similar(word):
word_sound = fuzzy.nysiis(word)
best = None
best_dist = 99999
for w in wordlist:
if pylev.distance(word_sound, w[1]) < best_dist and word != w[0][:-1]:
best_dist = pylev.distance(word_sound, w[1])
best = (w[0], best_dist)
return best
def name_nysiis(word):
if word:
try:
result = fuzzy.nysiis(word)
if result:
word = result
except ValueError:
pass
return word
def end_rhymes_with(self, line):
"""
Returns a boolean to indicate whether two lines form an end rhyme.
"""
if not self.has_same_ending_as(line):
if (self.ending[0].pronunciation != None) and (line.ending[0].pronunciation != None):
for e1 in self.ending:
for e2 in line.ending:
m1 = re.search('(?:%(VOWELS)s)(?:%(STRESSED)s)(?!.*(?:%(VOWELS)s)(?:%(STRESSED)s)).*$' % SYMBOLS_REGEX_OR,e1.pronunciation)
if m1 is not None:
r1 = m1.group()
else:
r1 = None
m2 = re.search('(?:%(VOWELS)s)(?:%(STRESSED)s)(?!.*(?:%(VOWELS)s)(?:%(STRESSED)s)).*$' % SYMBOLS_REGEX_OR,e2.pronunciation)
if m2 is not None:
r2 = m2.group()
else:
r2 = None
#If the two candidate endings rhyme, then the lines rhyme
if r1 == r2:
return True
else:
#Rhyme Fallback... If at least one word was not in the dictionary, we have to use the fallback
#TODO: Improve the accuracy of this. Currently, we just get the largest substring starting with a vowel.
# We should iterate over the substrings and check for matches instead so that we capture more mult-syllablic rhymes.
word1_nysiis = fuzzy.nysiis(self.ending[0].word)
word2_nysiis = fuzzy.nysiis(line.ending[0].word)
if len(word1_nysiis) <= len(word2_nysiis):
m = re.search('[aeiou].*',word1_nysiis)
if m is not None:
ending = m.group()
if word2_nysiis.endswith(ending):
return True
else:
m = re.search('[aeiou]',word2_nysiis)
if m is not None:
ending = m.group()
if word1_nysiis.endswith(ending):
return True
return False
def keyword_matching(arguments, entities):
words = arguments['keywords']
phonics = set([])
overlap = []
for w in words:
phonics.add(fuzzy.nysiis(w))
for i in xrange(0, len(entities)):
entity_name = nltk.word_tokenize(entities[i].name)
entity_phonics = set([])
for word in entity_name:
entity_phonics.add(fuzzy.nysiis(word))
common = len(phonics & entity_phonics) / len(entity_phonics)
if common == 1:
arguments['idx'] = i
return
overlap.append(common)
arguments['idx'] = overlap.index(max(overlap))
def generateNysiisHash(self, dictionary, table=None):
nysiisHash = {} if table is None else table
for name, gender in dictionary.iteritems():
name = self._sanitizeName(name)
if len(name) > 1:
nysiishash = fuzzy.nysiis(name)
self._appendToDict(nysiishash, gender, nysiisHash)
return nysiisHash
def determineFromMetaphone(self, firstName):
hashTable = {}
self.generateMetaphoneHash(self.firstDict, hashTable)
self.generateMetaphoneHash(self.secondDict, hashTable)
firstName = self._sanitizeName(firstName)
nameHash = fuzzy.nysiis(firstName)
if nameHash in hashTable:
results = hashTable[nameHash]
gender = max(results, key=results.get)
if results[gender] > 0:
return gender
return self.options['unknown']
def soundify(word):
table = []
table.append( ('', 0) )
word = fuzzy.nysiis(word)
for n in range(1,len(word)):
opt_n_score = 99999
opt_n_obj = None
for m in range(0, n):
opt_m = table[m]
remaining = find_similar(word[m:n])
if opt_m[1] + remaining[1] < opt_n_score:
opt_n_score = opt_m[1] + remaining[1]
opt_n_obj = (opt_m[0]+' '+remaining[0], opt_m[1] + remaining[1])
print n
table.append(opt_n_obj)
return table[-1]
def LookupRegionSlugs(region):
global database
if not database:
ConnectDatabase(True)
c = database.cursor()
c.execute("""SELECT `name`, `slug` FROM `realmStatus` WHERE `region` = %s;""" ,
(region,))
d = c.fetchone()
h = {}
while d is not None:
h[d[0]] = d[1]
h[fuzzy.nysiis(d[0])] = d[1]
d = c.fetchone()
c.close()
return h
def phonetic_tokenize_name(name, phonetic_algorithm="double_metaphone"):
"""Create Double Metaphone tokens from the string.
Parameters
----------
:param name: string
Name of the author. Usually it should be in the format:
surnames, first names.
:param phonetic algorithm: string
Which phonetic algorithm will be used. Options:
- "double_metaphone"
- "nysiis" (only for Python 2)
- "soundex" (only for Python 2)
Returns
-------
:return: tuple
The first element is a tuple with the tokens for surnames, the second
is a tuple with the tokens for first names. The tuple always contains
exactly two elements. Only the first results of the double metaphone
algorithm are included in tuples.
"""
if sys.version[0] == '2':
import fuzzy
dm = fuzzy.DMetaphone()
soundex = fuzzy.Soundex(5)
phonetic_algorithms = {
"double_metaphone": lambda y: dm(y)[0] or '',
"nysiis": lambda y: fuzzy.nysiis(y),
"soundex": lambda y: soundex(y)
}
else:
from ..ext.metaphone import dm
phonetic_algorithms = {
"double_metaphone": lambda y: dm(y)[0]
}
tokens = tokenize_name(name)
# Use double metaphone
tokens = tuple(map(lambda x: tuple(map(lambda y: phonetic_algorithms[
phonetic_algorithm](y), x)),
tokens))
return tokens
def aarnel_validation(data):
"""
COEFICIENTE DE AARNEL EN VERSION GENERALIZADA PARA DOS STRINGS..
Coeficiente de Aarnel es una mezcla de la distancia Levenshtein (LV) y
Teoria de Metaphone (ME).
Primero se calcula la distancia de los 2 string con LV, paralelamente se
extraen las combinaciones foneticas con ME de los strings por individual y
como resultado se obtienen 2 nuevos strings a los cuales tambien se le
aplicara LV para compara su proximidad fonetica, posteriormente se les saca
el promedio para obtener la media del cruce de proximidad literaria y
proximidad fonetica.
Parameters
----------
address_1 : str
String a comparar.
address_2 : str
String a comparar.
Returns
-------
True:
En caso del porcentaje obtenido sea mayor o igual al especificado por
los administradores.
False:
En caso del porcentaje obtenido sea menor al especificado por los
administradores
:Authors:
- Aarón Dominguez
- Nel Perez
Last Modification : 13.11.2017
"""
pruebanadres4 = 0
address_1 = data.get('address_1')
address_2 = data.get('address_2')
address_comparation = data.get('comparation_values')
if address_1 is None or address_2 is None:
return False
try:
digit = re.match('.+([0-9])[^0-9]*$', address_1)
digit = digit.start(1)
address_1 = address_1[:digit + 1]
address_1 = address_1.upper()
except:
pass
try:
digit = re.match('.+([0-9])[^0-9]*$', address_2)
digit = digit.start(1)
address_2 = address_2[:digit + 1]
address_2 = address_2.upper()
except:
pass
cleaner = address_comparation
if not cleaner:
return False
valid = truediv(address_comparation.get('percent_strength'), 100)
cleaner_especials = address_comparation.get('special')
cleaner_especials = cleaner_especials.split('|')
for item in cleaner_especials:
address_1 = address_1.replace(item, "")
address_2 = address_2.replace(item, "")
cleaner_words = address_comparation.get('word').upper()
cleaner_words = cleaner_words.split('|')
for item in cleaner_words:
address_1 = address_1.replace(item + ' ', "")
address_2 = address_2.replace(item + ' ', "")
address_1 = address_1.replace(' ', "")
address_2 = address_2.replace(' ', "")
address_1 = address_1.replace(' ', "")
address_2 = address_2.replace(' ', "")
ratio_lev = Levenshtein.ratio(address_1, address_2)
try:
sound_first_dir = fuzzy.nysiis(address_1.upper())
sound_second_dir = fuzzy.nysiis(address_2.upper())
ratio_met = Levenshtein.ratio(sound_first_dir, sound_second_dir)
media = (ratio_lev + ratio_met) / 2
except:
media = 0
if media >= valid:
return True
else:
return False
#!/usr/bin/env python
# coding: utf-8
# ## 1. Sound it out!
# <p>Grey and Gray. Colour and Color. Words like these have been the cause of many heated arguments between Brits and Americans. Accents (and jokes) aside, there are many words that are pronounced the same way but have different spellings. While it is easy for us to realize their equivalence, basic programming commands will fail to equate such two strings. </p>
# <p>More extreme than word spellings are names because people have more flexibility in choosing to spell a name in a certain way. To some extent, tradition sometimes governs the way a name is spelled, which limits the number of variations of any given English name. But if we consider global names and their associated English spellings, you can only imagine how many ways they can be spelled out. </p>
# <p>One way to tackle this challenge is to write a program that checks if two strings sound the same, instead of checking for equivalence in spellings. We'll do that here using fuzzy name matching.</p>
# In[104]:
# Importing the fuzzy package
import fuzzy
# Exploring the output of fuzzy.nysiis
fuzzy.nysiis('tufool')
# Testing equivalence of similar sounding words
fuzzy.nysiis('tomorrow') == fuzzy.nysiis('tommorow')
# In[105]:
get_ipython().run_cell_magic(
'nose', '',
"import sys\n\ndef test_fuzzy_is_loaded():\n assert 'fuzzy' in sys.modules, \\\n 'The fuzzy module should be loaded'"
)
# ## 2. Authoring the authors
# <p>The New York Times puts out a weekly list of best-selling books from different genres, and which has been published since the 1930’s. We’ll focus on Children’s Picture Books, and analyze the gender distribution of authors to see if there have been changes over time. We'll begin by reading in the data on the best selling authors from 2008 to 2017.</p>
# In[106]:
def generate_nysiis(word):
## https://pypi.python.org/pypi/Fuzzy
if isinstance(word,unicode):
word=word.encode('UTF-8')
return [fuzzy.nysiis(word)]
def makeFuzzy(self, pattern, variants):
variants.add(fuzzy.nysiis(pattern).lower())
return self
# Importing the fuzzy package
import fuzzy
# Exploring the output of fuzzy.nysiis
a=fuzzy.nysiis('color')
# Testing equivalence of similar sounding words
fuzzy.nysiis('color')==fuzzy.nysiis(a)
# Reading in datasets/nytkids_yearly.csv, which is semicolon delimited.
author_df=pd.read_csv('datasets/nytkids_yearly.csv',';')
# Looping through author_df['Author'] to extract the authors first names
first_name = []
for name in author_df['Author']:
first_name.append(name.split()[0])
# Adding first_name as a column to author_df
author_df['first_name'] = first_name
# Checking out the first few rows of author_df
author_df.head()
import numpy
# Looping through author's first names to create the nysiis (fuzzy) equivalent
nysiis_name = []
for name in author_df['first_name']:
nysiis_name.append(fuzzy.nysiis(name))
# Adding nysiis_name as a column to author_df
author_df['nysiis_name']=nysiis_name
def score_nysiis(words):
scores = []
for word in words:
scored = '%s: %s' % (word.lower(), fuzzy.nysiis(word))
scores.append(scored)
return scores