def get_celldifference(self):
conn = sqlite3.connect('ipython.db')
c = conn.cursor()
lines = c.execute('SELECT script, cell, line_content FROM ipython ')
lines = list(lines)
lines_copy = copy.copy(lines)
cell_differences = {}
repo_cell = ""
for l in range(0, len(lines)):
line = lines[l]
line_diff = []
for c in range(l+1, len(lines_copy)):
line_copy = lines_copy[c]
if line[0] == line_copy[0] and l != c and line[1] != line_copy[1]:
difference = distance.levenshtein(line[2], line_copy[2])
same = self.cell_analysis(len(line[2]), len(line_copy[2]), difference)
line_diff.append(same)
repo_cell = str(line[0]) + ": " + str(line[1])
if repo_cell in cell_differences:
cell_differences[repo_cell].extend(line_diff)
else:
cell_differences[repo_cell] = line_diff
return cell_differences
def kskip_ngram_cuoccurrence(answer, example, ng, kskip, question_index):
distance_over_ngrams = 0
# nltk skipgrams function doesn't include unigrams, so they are calculated separately:
summ_distance = 0
for word_ex in example:
dist = 0
for word_an in answer:
dist_tmp = distance.levenshtein(word_ex, word_an)
if dist_tmp > dist:
dist = dist_tmp
summ_distance += dist
try:
distance_over_ngrams += summ_distance / len(example)
except:
print("zero")
print(question_index)
distance_over_ngrams += 0
for n in range(2, ng + 1):
answer_ngramed = list(skipgrams(answer, n, kskip))
example_ngramed = list(skipgrams(example, n, kskip))
summ_distance = 0
for ngram_of_example in example_ngramed:
dist = 0
for ngram_of_answer in answer_ngramed:
dist_tmp = distance.levenshtein(ngram_of_example, ngram_of_answer)
if dist_tmp > dist:
dist = dist_tmp
summ_distance += dist
try:
distance_over_ngrams += summ_distance / len(example_ngramed)
except:
distance_over_ngrams += 0
return distance_over_ngrams
def run_cal():
db = MySQLdb.connect(host="localhost", port=3308, user="******",
passwd="你的密码", db="MovieSizer", charset="utf8")
cursor = db.cursor()
sql = "select * from movies_movieinfo"
cursor.execute(sql)
res = cursor.fetchall()
id = 1
print('\n\n cal_similar_gry.py\n\n')
# 控制矩阵规模
for i in range(0, int(len(res)/100)):
for j in range(i+1, len(res)):
i_id = res[i][0]
j_id = res[j][0]
moviename_length = distance.levenshtein(res[i][1], res[j][1])
nation_length = distance.levenshtein(res[i][3], res[j][3])
directors_length = distance.levenshtein(res[i][4], res[j][4])
leadactors_length = distance.levenshtein(res[i][5], res[j][5])
editors_length = distance.levenshtein(res[i][6], res[j][6])
length = moviename_length + nation_length + \
directors_length + leadactors_length + editors_length
similar = 1/length
sql = "INSERT INTO movies_moviesimilar VALUES (%d, %d, %d, %f)" % (
id, i_id, j_id, similar)
cursor.execute(sql)
db.commit()
id = id + 1
print('current : ', i)
db.close()
print('DONE !')
def testLD(self):
str1 = "hello my name is jose"
str2 = "hello m yname is jose"
str3 = "hlelo my name is jose"
self.assertEquals(distance.levenshtein(str1, str2), 2)
self.assertEquals(distance.levenshtein(str1, str3), 2)
self.assertEquals(distance.levenshtein(str1, str2), \
distance.levenshtein(str2, str1))
def valid_text(text):
text = clean_text(text)
# print(text)
if text == "":
return False, ''
p = re.compile('((\d{3})-(\d{3})-(\d{2}))')
if p.search(text) is not None:
found = p.search(text).group()
print(f"Matched regex 1: {found}")
return True, found
p = re.compile('(LPN\d{8})')
if p.match(text) is not None:
# print(f"Matched regex 2: {p.match(text).group()}")
return True, p.match(text).group()
else:
if len(text) > 3:
min_distance_levenshtein = 2
index = text.find('L')
if index >= 0:
#print('trazas ',distance.levenshtein('LPN',text[max(index-2,0):index]))
if distance.levenshtein(
'LPN', text[index:min(index + 2, len(text)
)]) <= min_distance_levenshtein:
text_aux = text[index + 3:-1]
text_aux = 'LPN' + text_aux
#print("NUEVO LPN L ", text_aux)
if p.match(text_aux) is not None:
# print(f"Matched regex 2: {p.match(text_aux).group()}")
return True, p.match(text_aux).group()
index = text.find('P')
if index >= 0:
#print('trazas ',distance.levenshtein('LPN',text[max(index-2,0):index]))
if distance.levenshtein(
'LPN',
text[max(index - 1, 0):min(index + 1, len(text))]
) <= min_distance_levenshtein:
text_aux = text[index + 2:-1]
text_aux = 'LPN' + text_aux
#print("NUEVO LPN P ", text_aux)
if p.match(text_aux) is not None:
# print(f"Matched regex 2: {p.match(text_aux).group()}")
return True, p.match(text_aux).group()
index = text.find('N')
if index >= 0:
#print('trazas ',distance.levenshtein('LPN',text[max(index-2,0):index]))
if distance.levenshtein(
'LPN',
text[max(index -
2, 0):index]) <= min_distance_levenshtein:
text_aux = text[index + 1:-1]
text_aux = 'LPN' + text_aux
#print("NUEVO LPN N ", text_aux)
if p.match(text_aux) is not None:
# print(f"Matched regex 2: {p.match(text_aux).group()}")
return True, p.match(text_aux).group()
return False, ''
def predict_oov_v2(self, n):
if len(self.masked_index) != 0:
print('Masked sentence ' + ' '.join(self.tokenized_text) +
' and the masked index is ' + str(self.masked_index))
indexed_tokens = self.tokenizer.convert_tokens_to_ids(
self.tokenized_text)
segments_ids = [0] * len(self.tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
#print (indexed_tokens)
with torch.no_grad():
outputs = self.model(tokens_tensor,
token_type_ids=segments_tensors)
predictions_1 = outputs[0]
#predicted_index = torch.argmax(predictions_1[0, masked_index]).item()
bestsent = self.tokenized_text
# looping the masked index
for mei in self.masked_index:
sorted_index = torch.argsort(predictions_1[0, mei],
descending=True).tolist()
sorted_probability = [
predictions_1[0, mei][i] for i in sorted_index
]
returnstr = []
for loop in range(n):
predicted_token = self.tokenizer.convert_ids_to_tokens(
[sorted_index[loop]])[0]
returnstr.append(predicted_token)
#returnstr.append(' '.join(self.tokenized_text).replace('[MASK]', predicted_token))
#print(predicted_token)
bestd = 100
bestw = ''
for v in returnstr:
#print(v+' ' +str(distance.levenshtein(v, self.original_sentence[mei])))
if distance.levenshtein(
v.lower(),
self.original_sentence[mei].lower()) < bestd:
bestd = distance.levenshtein(
v.lower(), self.original_sentence[mei].lower())
bestw = v
bestsent[mei] = bestw
print('Recovered sentence ' + ' '.join(bestsent))
return ' '.join(bestsent), self.get_score(bestsent)
else:
return ' '.join(self.tokenized_text), self.get_score(
self.tokenized_text)
def compare_mt(usertrans, referencetrans, machinetrans):
"""
Compare if user translation better or worst
than machine translation
"""
evaluation = {'better': ['Congratulations, you did better than the machine translation!', \
'Be proud, you were better than the machine translation!', \
'You are the best, even better than the machine translation!'], \
'same': ['You did as good as the machine translation!', \
'This is a tie between you and the machine translation!', \
'The machine translation was as good as you!'], \
'worst': ["The machine translation beat you, let's try to do better!", \
"What a shame, you were defeated by the machine translation.", \
"Next time, you will beat the machine translation, but not this time!"]}
# encode sentences to UTF-8
ut = usertrans.encode('utf8')
tt = referencetrans.encode('utf8')
mt = machinetrans.encode('utf8')
# remove punctuation
replace_punctuation = string.maketrans(string.punctuation, ' '*len(string.punctuation))
ut = ut.translate(replace_punctuation).lower()
tt = tt.translate(replace_punctuation).lower()
mt = mt.translate(replace_punctuation).lower()
# levensthein characters ratio
lev_let_ut = lev.ratio(tt, ut)
lev_let_mt = lev.ratio(tt, mt)
# levensthein tokens ratio
ut = ut.split()
tt = tt.split()
mt = mt.split()
lensum_user = len(ut)+len(tt)
lensum_machine = len(mt)+len(tt)
lev_tok_ut = (lensum_user - distance.levenshtein(tt, ut)) / lensum_user
lev_tok_mt = (lensum_machine - distance.levenshtein(mt, ut)) / lensum_machine
# get best levensthien ratio
ratio_ut = max(lev_let_ut, lev_tok_ut)
ratio_mt = max(lev_let_mt, lev_tok_mt)
# evaluate if user better, worst or similar than machine
if ratio_ut > ratio_mt:
return random.choice(evaluation['better'])
elif ratio_ut < ratio_mt:
return random.choice(evaluation['worst'])
else:
return random.choice(evaluation['same'])
def compare_sent(self, sent_true: str, sent_pred: str):
'''
:param sent_true:
:param sent_pred:
:return: int value
'''
return distance.levenshtein(
sent_true, sent_pred), distance.levenshtein(sent_true,
sent_pred,
normalized=True)
def get_closest_word(ww, wset):
bestd = 100
bestw = ''
for v in wset:
#print(v+' ' +str(distance.levenshtein(v, self.original_sentence[mei])))
if distance.levenshtein(v.lower(), ww.lower()) < bestd:
bestd = distance.levenshtein(v.lower(), ww.lower())
bestw = v
return bestw
def compare_lex(item1, item2):
global search_word
if search_word == item1["point headline"]:
return 1
if search_word == item2["point headline"]:
return -1
else:
if levenshtein(item1["point headline"], search_word) < levenshtein(
item2["point headline"], search_word):
return 1
else:
return -1
def getIDs(name, players, ids):
best = 100000
bestIndex = -1
for player in players:
if distance.levenshtein(name, player) < best:
best = distance.levenshtein(name, player)
bestIndex = players.index(player)
if best > 5:
print(name)
print(ids[bestIndex])
return ids[bestIndex]
def test_levenshteinBase(self):
a = ''
b = ''
self.assertAlmostEqual(distance.levenshtein(a, b), 0, places=3)
a = 'abc'
b = 'abc'
self.assertAlmostEqual(distance.levenshtein(a, b), 0, places=3)
a = 'abcd'
b = 'abc'
self.assertAlmostEqual(distance.levenshtein(a, b), 0.25, places=3)
a = 'abc'
b = 'abd'
self.assertAlmostEqual(distance.levenshtein(a, b), 0.33333, places=3)
a = 'ab'
b = 'abc'
self.assertAlmostEqual(distance.levenshtein(a, b), 0.33333, places=3)
a = ''
b = 'abc'
self.assertAlmostEqual(distance.levenshtein(a, b), 1, places=3)
a = 'abc'
b = ''
self.assertAlmostEqual(distance.levenshtein(a, b), 1, places=3)
a = 'abc'
b = 'xyz'
self.assertAlmostEqual(distance.levenshtein(a, b), 1, places=3)
def calculateSimilarity_WithDistancePackage(createdSentence): #createdSentence is list of words
levinDist = {}
jaccardDist = {}
bestValues = {}
for i in range(len(X_train)):
currSentence = X_train[i]
sentence_str = [index_to_word[x] for x in currSentence[1:-1]] # sentence_str is list of words
#Levinstein Distance
dist = distance.levenshtein(createdSentence, sentence_str)
dist2 = distance.jaccard(createdSentence,sentence_str)
#print(dist)
if (dist>0):
#print ("Distance Levinshtein: %f" % (dist))
levinDist[i]=dist
jaccardDist[i]=dist2
#print ("Jaccard Distance: %f" % (dist2))
#take best value
levinMin = min(levinDist.itervalues())
jaccardMin = min(jaccardDist.itervalues())
print ("Best Distance Levinshtein: %f" % (levinMin))
print ("Best Distance Jaccard: %f" % (jaccardMin))
bestValues["Jaccard"]=jaccardMin
bestValues["Levin"]=levinMin
return bestValues
def main(args):
parameters = process_args(args)
target_formulas_file = parameters.target_file
predicted_formulas_file = parameters.predicted_file
target_formulas = open(target_formulas_file).readlines()
predicted_formulas = open(predicted_formulas_file).readlines()
i = 0
total_len = 0
total_edit_distance = 0
if len(target_formulas) != len(predicted_formulas):
print("number of formulas doesn't match")
return
n = len(target_formulas)
for tf, pf in zip(target_formulas, predicted_formulas):
i += 1
if i % 2000 == 0:
print("{}/{}".format(i, n))
tf_ = tf.strip()
pf_ = pf.strip()
true_token = tf_.split(' ')
predicted_tokens = pf_.split(' ')
l = max(len(true_token), len(predicted_tokens))
edit_distance = distance.levenshtein(true_token, predicted_tokens)
total_len += l
total_edit_distance += edit_distance
print("{}/{}".format(n, n))
print('Edit Distance Accuracy: %f' %
(1. - float(total_edit_distance) / total_len))
def cluster_similar_responses(output_path):
max_count = get_max_socket_message_count(output_path)
listing = glob.glob(output_path + '*-%s.log' % max_count)
messages = [file(filename).read() for filename in listing]
messages = [extract_description_from_message(m) for m in messages]
messages = np.asarray(messages)
print()
print('Clustering %s responses...(this might take a while)' %
len(messages))
print()
lev_similarity = -1 * np.array(
[[distance.levenshtein(m1, m2) for m1 in messages] for m2 in messages])
affprop = sklearn.cluster.AffinityPropagation(affinity='precomputed',
damping=0.5)
affprop.fit(lev_similarity)
print('Generated clusters:')
print()
for cluster_id in np.unique(affprop.labels_):
exemplar = messages[affprop.cluster_centers_indices_[cluster_id]]
cluster = np.unique(
messages[np.nonzero(affprop.labels_ == cluster_id)])
cluster_str = ', '.join(cluster)
print('-' * 80)
print(' - *%s:* %s' % (exemplar, cluster_str))
print('-' * 80)
print()
def dist(word1, word2): # определяем схожесть 2-х треков import distance #print distance.hamming(word1, word2, normalized=True) # не можем использовать, так как это расстояние рассчитывается только для строк одинаковой длины return distance.levenshtein(word1, word2, normalized=True) #минимальное количество операций вставки одного символа, удаления одного символа и замены одного символа на другой, необходимых для превращения одной строки в другую
def similarity(self, word1, word2):
# Score function may vary and for sure can be improved
# I used distance module here
ldist = distance.levenshtein(word1, word2)
hdist = 0 #distance.hamming(word1, word2) requires equal length e.g. padding
score = ldist * 1 # + hdist * 0
return score
def initUI(self):
inputGroups = self.inputText.split()
solutionGroups = self.solutionText.split()
numLetters = 0.0
numErrors = 0.0
for idx, _ in enumerate(solutionGroups):
if idx >= len(inputGroups):
inputGroups.append("")
numLetters += len(solutionGroups[idx])
numErrors += levenshtein(solutionGroups[idx], inputGroups[idx])
percentage = numErrors / numLetters * 100.0
solutionLabel = QLabel(self.createEvaluationRichText(solutionGroups, inputGroups))
errorLabel = QLabel("Error count (Levenshtein): %02.2f%%" % percentage)
layout = QVBoxLayout()
layout.addWidget(solutionLabel)
layout.addWidget(errorLabel)
if percentage < 10.0:
successLabel = QLabel("Error rate lower than 10%! <br/> Proceed to next lesson!")
layout.addWidget(successLabel)
self.setLayout(layout)
self.setWindowTitle('Evaluation')
def similarity(self, word1, word2): # Score function may vary and for sure can be improved # I used distance module here ldist = distance.levenshtein(word1, word2) hdist = 0 #distance.hamming(word1, word2) requires equal length e.g. padding score = ldist * 1# + hdist * 0 return score
def checkNames(df):
df = df[df['mins'] >= 15]
df['hours'] = 1. / 60. * df['mins']
res = df.groupby('name')['hours'].agg(np.sum)
nPart = len(res)
print('Helenka')
names = res.index.tolist()
import sklearn.cluster
import distance
#words = "YOUR WORDS HERE".split(" ") #Replace this line
words = names[0:100]
words = np.asarray(words) #So that indexing with a list will work
lev_similarity = -1 * np.array(
[[distance.levenshtein(w1, w2) for w1 in words] for w2 in words])
print(lev_similarity)
vals = []
for idx, x in np.ndenumerate(lev_similarity):
vals.append([x, idx])
#print(idx, x)
vals = sorted(vals, key=lambda s: s[0])
for v in vals:
print(v[0], words[v[1][0]], ' , ', words[v[1][1]])
def ubica(bot, update, user_data):
conn = sqlite3.connect('aemet.db')
c = conn.cursor()
sitio = update.message.text.title()
c.execute('SELECT * FROM municipio WHERE nombre=?', (sitio, ))
base = c.fetchone()
if base is None:
update.message.reply_text(
'Parece que no está bien escrito, déjame ver...')
c.execute('SELECT * FROM municipio')
flag = 0
i = 0
while i < 9208:
base = c.fetchone()
if distance.levenshtein(unidecode(sitio.lower()),
unidecode(base[0].lower()),
normalized=True) < 0.2:
flag = 1
break
i += 1
if flag == 0:
update.message.reply_text(
'Ubicación no encontrada. Por favor, indica el municipio que quieres buscar:'
)
return LUGAR
cod[update.message.chat.id] = base[1]
update.message.reply_text("Se ha elegido el municipio %s, en %s" %
(base[0], base[2]),
reply_markup=markup)
place[update.message.chat.id] = base[1]
return CHOOSING
def inlevenshtein(seq1, seqs, max_dist=0.1):
for seq2 in seqs:
dist1 = distance.levenshtein(seq1, seq2, max_dist=2)
if dist1 !=-1:
dist2 = distance.nlevenshtein(seq1, seq2, )
if dist2 <= max_dist:
yield dist2, seq2
def distance_calculating(train_tuple, test_tuple):
"""
Calculate the Levenshtein distance between two items and return a
dictionary with the index of closest matches as a list as its value and
the n_test item as the key.
This function returns a dictionary of the form:
n_test: [distance, [n_train...n_train]]
"""
data = dict()
for n_test, test_item in enumerate(test_tuple):
# set default value for the data dict.
data.setdefault(n_test,[-1,[]])
# remove the class label
test_item = test_item[:-1]
for n_train, train_item in enumerate(train_tuple):
# remove the class label
train_item = train_item[:-1]
min_distance = distance.levenshtein(test_item, train_item)
# if there is an exact match
if min_distance == 0:
data[n_test] = [min_distance, [n_train]]
break
elif min_distance == data[n_test][0]:
data[n_test][1].append(n_train)
elif min_distance < data[n_test][0] or data[n_test][0] == -1:
data[n_test] = [min_distance, [n_train]]
return data
def knn(test, orig, k):
index = []
dist = []
ct = 1
y = test[0:8]
oc = test[8]
for i in orig:
x = i[0:8]
dist.append(distance.levenshtein(test, x))
index.append(i[8])
dist, index = pairsort(dist, index)
cp = {}
for i in dist:
if ct <= k:
if index[ct - 1] in cp.keys():
cp[index[ct - 1]] += 1
else:
cp[index[ct - 1]] = 1
else:
break
ct += 1
predicted = max(cp.items(), key=operator.itemgetter(1))[0]
return predicted, oc
def calcDist(Mots, AllWordsList, nbSyll):
nb9 = nbSyll / 9
# on calcule leur distance de Levenshtein min avec la base
WordDist = []
for i, mot in enumerate(Mots): # pseudo-mots
minD = 1000000
minWord = ''
nbD = 0
for word in AllWordsList: # base
d = distance.levenshtein(mot, word)
if d < minD:
minD = d
minWord = word
nbD = 1
elif d == minD:
nbD += 1
WordDist.append([mot, minWord, minD, nbD])
distList = [x[2] for x in WordDist]
# nombre le plus commun
[dist, nb] = Counter(distList).most_common(1)[0]
if nb < 12:
return -1
L1 = distList[0:nb9]
L2 = distList[nb9:2 * nb9]
L3 = distList[2 * nb9:3 * nb9]
if L1.count(dist) < 4 or L2.count(dist) < 4 or L3.count(dist) < 4:
return -1
L1Res = [x for x in WordDist[0:nb9] if x[2] == dist]
L2Res = [x for x in WordDist[nb9:2 * nb9] if x[2] == dist]
L3Res = [x for x in WordDist[2 * nb9:3 * nb9] if x[2] == dist]
return L1Res + L2Res + L3Res
def calculateSimilarity_WithDistancePackage(
createdSentence): #createdSentence is list of words
levinDist = {}
jaccardDist = {}
bestValues = {}
for i in range(len(X_train)):
currSentence = X_train[i]
sentence_str = [index_to_word[x] for x in currSentence[1:-1]
] # sentence_str is list of words
#Levinstein Distance
dist = distance.levenshtein(createdSentence, sentence_str)
dist2 = distance.jaccard(createdSentence, sentence_str)
#print(dist)
if (dist > 0):
#print ("Distance Levinshtein: %f" % (dist))
levinDist[i] = dist
jaccardDist[i] = dist2
#print ("Jaccard Distance: %f" % (dist2))
#take best value
levinMin = min(levinDist.itervalues())
jaccardMin = min(jaccardDist.itervalues())
print("Best Distance Levinshtein: %f" % (levinMin))
print("Best Distance Jaccard: %f" % (jaccardMin))
bestValues["Jaccard"] = jaccardMin
bestValues["Levin"] = levinMin
return bestValues
def get_conceptual_independence(data):
comm = []
count = 0
comm_data = data["clusters"]
for i in comm_data:
temp = []
for j in i["nodes"]:
if find_node_type(data, j):
name = find_node(data, j)
name = name.split(".")[-1]
temp.append(name)
comm.append(temp)
community_score = []
for i in comm:
score = 0
words = np.asarray(i)
for j in words:
for k in words:
# print (distance.levenshtein(j, k)/max(len(j), len(k)))
score += 1 - distance.levenshtein(j, k) / max(
len(j), len(k))
community_score.append(score / len(i)**2)
total_score = 0
for i in community_score:
total_score += i
total_score = total_score / len(community_score)
return total_score
def get_repodifference(self):
conn = sqlite3.connect('ipython.db')
c = conn.cursor()
lines = c.execute(
'SELECT repository, script, line_content FROM ipython ')
lines = list(lines)
lines_copy = copy.copy(lines)
repo_differences = {}
for l in range(0, len(lines)):
line = lines[l]
line_diff = []
for c in range(l + 1, len(lines_copy)):
line_copy = lines_copy[c]
if line[0] != line_copy[0] and l != c and line[1] != line_copy[
1]:
difference = distance.levenshtein(line[2], line_copy[2])
same = self.cell_analysis(len(line[2]), len(line_copy[2]),
difference)
line_diff.append(same)
repo_cell = str(line[0]) + ": " + str(line[1])
if repo_cell in repo_differences:
repo_differences[repo_cell].extend(line_diff)
else:
repo_differences[repo_cell] = line_diff
return repo_differences
def get_title(author, talk_names):
for name in talk_names:
if name.startswith(author):
matches = re.search(r'(.*) "(.*)"', name)
if matches:
return matches.group(2)
else:
return name
# Use levenshtein's distance to get the closes title
min_title = ''
min_distance = float('inf')
for subauth, subtitle in filter(lambda x: x[0],
map(get_author, talk_names)):
cur_dis = distance.levenshtein(subauth, author)
if cur_dis < min_distance:
min_distance = cur_dis
min_title = subtitle
if min_title:
return min_title
else:
return 'not_found. Is it no educativa?'
def assign_third(first, second, tags):
first_out = []
second_out = []
third_out = []
B = len(first)
attempts = min(B-1,ATTEMPTS)
for i in range(B):
ti = first[i]
si = second[i]
hi, tidi, sidi = tags[i]
flag = attempts
checked = []
while (flag):
j = random.randrange(B)
if j in checked:
continue
checked.append(j)
flag -= 1
hj = tags[j][0]
if distance.levenshtein(hi, hj) > MIN_LEV_DIST:
if (random.getrandbits(1)):
tj = first[j]
newdi = tags[j][1]
else:
tj = second[j]
newdi = tags[j][2]
if (newdi != tidi ) & (newdi != sidi):
first_out.append(ti)
second_out.append(si)
third_out.append(tj)
flag = 0
return (first_out, second_out, third_out)
def RatingLevenshtein(revista):
#Get path file
dirname = os.path.dirname(__file__)
loc = os.path.join(dirname, r'JCR2018.xlsx')
#Initialize reader
workbook = xlrd.open_workbook(loc)
sheet = workbook.sheet_by_index(0)
tuplas = []
start_time = time()
for i in range(sheet.nrows):
valor = (sheet.cell_value(i, 1),
levenshtein(revista, sheet.cell_value(i, 1)))
tuplas.append(valor)
final_time = time()
execution_time = round(final_time - start_time, 2)
tuplas.sort(key=lambda revista: revista[1])
top_5 = tuplas[:10]
result = (top_5, execution_time)
return result
def score(self):
count = []
j_coeff = []
j_coeff_feat = []
l_dist = []
# pred_feat_list = []
# pred_feat_accuracy = []
bleu_score = []
for i in range(self.df.shape[0]):
curr_feat = np.array([self.df.iloc[i, 0:4]])
path, label, decision_feature = self.predict(curr_feat)
print('actual vs predicted: ', self.df.iloc[i, 4], ' vs ',
' '.join(path), 'labels: ', self.df.iloc[i, 5], label[0])
count.append(self.df.iloc[i, 5] == label[0])
actual_path = self.df.iloc[i, 4].split()
actual_path_tok = [self.char_indices[char] for char in actual_path]
pred_path_tok = [self.char_indices[char] for char in path]
# print('actual_path--', actual_path)
# print('path--', path)
bleu_score.append(sentence_bleu([actual_path], path))
j_coeff.append(self.get_j_coeff(actual_path_tok, pred_path_tok))
j_coeff_feat.append(
self.get_j_coeff(self.df.iloc[i, 6], decision_feature))
l_dist.append(
distance.levenshtein(self.df.iloc[i, 4].replace(' ', ''),
''.join(path)))
print('Actual vs predicted features: ', self.df.iloc[i, 6], 'vs',
decision_feature, '\n')
print('\nLabel accuracy - ', np.mean(count))
print('Path metric (Jaccard) - ', np.mean(j_coeff))
print('Path metric (Levenshtein) - ', np.mean(l_dist))
print('Decision feature metric (Jaccard) - ', np.mean(j_coeff_feat))
print('Bleu score of paths - ', np.mean(bleu_score))
def edit_distance(references, hypotheses):
d_leven, len_tot = 0, 0
for ref, hypo in zip(references, hypotheses):
d_leven += distance.levenshtein(ref, hypo)
len_tot += float(max(len(ref), len(hypo)))
return 1. - d_leven / len_tot
def update1(k1,k2): i=0 while i<len(k1): t1=k1[i]['T'] j=0 flag=0 while j<len(k2): k=0 t2=k2[j]['T'] #for word1 in t1: # dist=0 # for word2 in t2: # dist+=distance.lebenshtein(word1,word2) dist=int(distance.levenshtein(t1,t2)) #print dist if dist<=18: flag=1 break #print dist j=j+1 #minsum=su #k=j i=i+1
def align(sen1, sen2):
"""finds the best mapping of words from one sentence to the other"""
#find lengths
sen1 = list(map(preprocess_word, sen1.split()))
sen2 = list(map(preprocess_word, sen2.split()))
lengthDif = len(sen1) - len(sen2)
if lengthDif > 0:
shorter = sen2
longer = sen1
else:
shorter = sen1
longer = sen2
lengthDif = abs(lengthDif)
shorter += ["emptyWord"] * lengthDif
#create matrix
matrix = np.zeros((len(longer), len(longer)))
for i in range(len(longer)):
for j in range(len(longer) - lengthDif):
matrix[i,j] = distance.levenshtein(longer[i], shorter[j])
print(matrix)
#compare with munkres
m = Munkres()
indexes = m.compute(matrix)
print("mapping is:",[(shorter[i], longer[j]) for (i,j) in indexes])
def comp_string(self): # self.comp_string()
allOriginalWords = []
allNLD = []
# rec = j[count][wordPos].split() #j is entry, count is dict key, wordPos is sentence called
for i in self.original:
split = i.split() # this splits one whole sentence to words
allOriginalWords.append(split)
for i in allOriginalWords:
groupNLD = []
allNLD.append(groupNLD)
for i in self.recognized:
count = 0
#print("Self.recognized", self.recognized)
for j in range(25):
#print(j)
sentence = i[j].split()
#print(allOriginalWords[j])
#print(sentence)
ld = d.levenshtein(allOriginalWords[j], sentence)
nld = 0
nld = ld /( max(len(allOriginalWords[j]), len(sentence)))
allNLD[j].append(nld)
# print(nld)
count += 1
for i in allNLD:
s.distances.append(i)
#print(i)
print("finished distance NLD")
def analyse_titles(author, liste_titles, dict_titles2ark, zipname, outputfile):
"""
Si un auteur a plusieurs titres associés :
on compare chaque titre avec chacun des titres suivants dans la liste
en ne tenant pas compte des variantes sur les chiffres
"""
i = 0
for title in liste_titles[i:]:
title_chars = clean_titles_int(title)
for foll_title in liste_titles[i + 1:]:
foll_title_chars = clean_titles_int(foll_title)
dist = distance.levenshtein(title, foll_title)
if ((dist == 1 or dist == 2) and title_chars != foll_title_chars):
nbark_title1 = len(dict_titles2ark[title])
nbark_title2 = len(dict_titles2ark[foll_title])
ark_title = " ".join(list(dict_titles2ark[title]))
ark_foll_title = " ".join(list(dict_titles2ark[foll_title]))
if nbark_title1 > nbark_title2:
line = [
str(author), ark_title, title, ark_foll_title,
foll_title,
str(dist), zipname
]
line2report(line, outputfile)
else:
line = [
str(author), ark_foll_title, foll_title, ark_title,
title,
str(dist), zipname
]
line2report(line, outputfile)
i += 1
def assign_third(first, second, tags):
first_out = []
second_out = []
third_out = []
B = len(first)
attempts = min(B - 1, ATTEMPTS)
for i in range(B):
ti = first[i]
si = second[i]
hi, tidi, sidi = tags[i]
flag = attempts
checked = []
while (flag):
j = random.randrange(B)
if j in checked:
continue
checked.append(j)
flag -= 1
hj = tags[j][0]
if distance.levenshtein(hi, hj) > MIN_LEV_DIST:
if (random.getrandbits(1)):
tj = first[j]
newdi = tags[j][1]
else:
tj = second[j]
newdi = tags[j][2]
if (newdi != tidi) & (newdi != sidi):
first_out.append(ti)
second_out.append(si)
third_out.append(tj)
flag = 0
return (first_out, second_out, third_out)
def __init__(self, mapping=None, weights=None):
self._distanceMetrics = {
'euclidean': lambda a, b: distance.euclidean([a], [b]),
'manhattan': distance.manhattanScalar,
'levenshtein': lambda a, b: distance.levenshtein([a], [b]),
'needleman_wunsch':
lambda a, b: distance.needleman_wunsch([a], [b]),
'jaccard': distance.jaccard,
'dice': distance.dice
}
self._mapping = mapping
if (self._mapping == None):
self._mapping = [None] * NUM_FEATURES
self._mapping[STARS] = self._distanceMetrics['manhattan']
self._mapping[TOTAL_REVIEW_COUNT] = self._distanceMetrics[
'manhattan']
self._mapping[AVAILABLE_REVIEW_COUNT] = self._distanceMetrics[
'manhattan']
self._mapping[MEAN_REVIEW_LEN] = self._distanceMetrics['manhattan']
self._mapping[MEAN_WORD_LEN] = self._distanceMetrics['manhattan']
self._mapping[NUM_WORDS] = self._distanceMetrics['manhattan']
self._mapping[MEAN_WORD_COUNT] = self._distanceMetrics['manhattan']
self._mapping[TOTAL_HOURS] = self._distanceMetrics['manhattan']
self._mapping[ATTRIBUTES] = self._distanceMetrics['jaccard']
self._mapping[CATEGORIES] = self._distanceMetrics['jaccard']
self._mapping[TOP_WORDS] = self._distanceMetrics['jaccard']
self._mapping[KEY_WORDS] = self._distanceMetrics['jaccard']
self._mapping[OPEN_HOURS] = self._distanceMetrics['jaccard']
self._weights = weights
if (self._weights == None):
self._weights = [1] * NUM_FEATURES
def ajax():
if not request.json:
abort(400)
else:
ingredient1 = request.json.get("ingredient1")
ingredient2 = request.json.get("ingredient2")
edit_distance = distance.levenshtein(ingredient1, ingredient2)
return jsonify({"distance": edit_distance})
def leven(self):
all_dis = []
small_list = Strings_comp(self).clear_data()
for i in range(0, len(small_list)-1):
cursor = small_list[i]
for j in range(i+1, len(small_list)-1):
dis = distance.levenshtein(cursor, small_list[j])
all_dis.append(dis)
return all_dis
def successful(entry, word_list=word_list):
new_word_list = []
for address, subtree in sorted(entry.lexical, key=lambda (addr,subt): addr):
new_word = subtree.symbol
if new_word not in word_list:
return False
new_word_list.append(new_word)
return levenshtein(new_word_list, word_list) == 0
def test_similarity(sentence):
if sentence in similarity_memo:
return similarity_memo[sentence]
similarity = distance.levenshtein(target_sentence, sentence)
similarity_memo[sentence] = similarity
return similarity
def meaning_answer_qual(self, entered):
qual = 0
for meaning in word.meaning:
meaning = normalize(meaning)
# arbitrary
ok_dist = round(0.4*len(meaning))
dist = distance.levenshtein(entered, meaning)
qual = max(qual, 2 if dist == 0 else 1 if dist <= ok_dist else 0)
return qual
def levenhstein_cluster(words):
words = np.asarray(words) #So that indexing with a list will work
lev_similarity = -1*np.array([[distance.levenshtein(w1,w2) for w1 in words] for w2 in words])
affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed", damping=0.5)
affprop.fit(lev_similarity)
for cluster_id in np.unique(affprop.labels_):
exemplar = words[affprop.cluster_centers_indices_[cluster_id]]
cluster = np.unique(words[np.nonzero(affprop.labels_==cluster_id)])
cluster_str = ", ".join(cluster)
print(" - *%s:* %s" % (exemplar, cluster_str))
def wikiMatches(word, maxDistance=5):
import distance
import re
word = word.lower()
response = keywordSearch(word, maxHits = 20)
results = response["results"]
for result in results:
cleanLabel = re.sub(r'\(.*\)$', '', result["label"]).lower().strip()
result["labelDistance"] = distance.levenshtein(word, cleanLabel)
return [result for result in results if result["labelDistance"] <= maxDistance]
def get_close_matches(self,test_string,n=5,cutoff=0.5):
candidate_keys = difflib.get_close_matches(test_string.lower(),self.long_titles.keys(),n=n,cutoff=cutoff)
candidates = []
scores = []
for candidate_key in candidate_keys:
candidate = self.long_titles[candidate_key]
candidates.append(candidate)
scores.append(1.0-distance.levenshtein(test_string,candidate))
return zip(candidates,scores)
def edit_distance():
if not request.json:
raise InvalidUsage('Requires JSON')
data = request.get_json()
edit_distance = levenshtein(data['word1'], data['word2'])
response = {
'word1': data['word1'],
'word2': data['word2'],
'edit_distance': edit_distance
}
return jsonify(**response)
def search(self, term, k, results=None):
if results is None:
results = []
d = distance.levenshtein(self.term, term)
counter = 1
if d <= k:
results.append(self.term)
for i in range(max(0, d - k), d + k + 1):
child = self.children.get(i)
if child:
counter += child.search(term, k, results)
return counter
def build_json(df):
node_list = []
edge_list = []
name_list = list(df.name)
for name in name_list:
node_list.append({'name': name})
for i in range(0,len(name_list)):
for j in range(i,len(name_list)):
d = distance.levenshtein(name_list[i],name_list[j])
if d < 3 and j!=i:
edge_list.append({'source':i,'target':j,'value':d+1})
n = {'nodes': node_list, 'edges': edge_list}
return n
def calculateDistance(word_array,vocabulary):
distance_score_min_levenshstein = {}
distance_score_sum_levenshstein = {}
for voc in vocabulary:
temp_score_lev = 9999
mean_score = 0
for word in word_array:
score = distance.levenshtein(word,voc);
if temp_score_lev > score:
temp_score_lev = score
distance_score_min_levenshstein[voc] = temp_score_lev
return distance_score_min_levenshstein
def extract(self, query, document, params=None):
'''
Extracts features for a given pair of query and document
Args:
query(str): The query string
document(str)
params: parameters for the feature extractor
Returns
float -- feature value
'''
return distance.levenshtein(self.tokenize(query),
self.tokenize(document),
normalized=True)
def align(sen1, sen2, string=True): """finds the best mapping of words from one sentence to the other string = a boolean represents if sentences are given as strings or as list of ucca terminal nodes returns list of word tuples and the corresponding list of indexes tuples""" if string: sen1 = list(map(preprocess_word, sen1.split())) sen2 = list(map(preprocess_word, sen2.split())) else: sen1 = [preprocess_word(terminal.text) for terminal in sen1] sen2 = [preprocess_word(terminal.text) for terminal in sen2] #find lengths length_dif = len(sen1) - len(sen2) if length_dif > 0: shorter = sen2 longer = sen1 switched = False else: shorter = sen1 longer = sen2 switched = True length_dif = abs(length_dif) shorter += ["emptyWord"] * length_dif #create matrix matrix = np.zeros((len(longer), len(longer))) for i in range(len(longer)): for j in range(len(longer) - length_dif): matrix[i,j] = distance.levenshtein(longer[i], shorter[j]) + float(abs(i-j))/len(longer) #compare with munkres m = Munkres() indexes = m.compute(matrix) #remove indexing for emptywords and create string mapping refactored_indexes = [] mapping = [] start = 0 if string else 1 for i, j in indexes: if j >= len(longer) - length_dif: j = -1 - start if switched: refactored_indexes.append((j + start, i + start)) mapping.append((shorter[j], longer[i])) else: refactored_indexes.append((i + start, j + start)) mapping.append((longer[i], shorter[j])) return mapping, refactored_indexes
def getSuggestedWords(word, wordFrequencies):
suggestedWords = []
print word
for item in wordFrequencies:
possibleWord = item[0]
#quick check for possibility based on length
if abs(len(word) - len(possibleWord)) > 2:
continue
#compute levenshtein distance
editDistance = distance.levenshtein(word, possibleWord)
if editDistance <= 2:
suggestedWords.append(possibleWord)
return suggestedWords
def findWordVariants(tset, wordVariants):
dist = 0
for ind in range(len(wordVariants)):
#its a set
entry = wordVariants[ind]
dist = 0
for cand in tset:
if cand not in entry:
for toCheck in entry:
dist += levenshtein(cand, toCheck)
dist /= (len(tset) * len(entry) * 1.0)
if dist < 1.3:
#print dist, len(tset), len(entry)
return ind
else:
return ind
return -1
def testCloseMatch(self):
for k1 in self.keys.iterkeys():
timer_start("close_match")
d = self.tree.close_match(k1, 4, None, DictAction())
timer_end("close_match")
for k2 in self.keys.iterkeys():
distance = levenshtein(k1, k2)
if distance <= 4:
self.assert_(k2 in d, "Match manquant pour %s : %s (distance = %i)" % (k1, k2, distance))
self.assert_(
k2 in d and d[k2][0] == distance,
"Mauvaise distance pour %s et %s : %i != %s" % (k1, k2, distance, d.get(k2)[0]),
)
else:
self.assert_(
k2 not in d,
"Mauvaise distance pour %s et %s : %i > 4 mais trouvé %s" % (k1, k2, distance, d.get(k2)),
)
def getMinSequence(string, slist):
mind = None
mdist = None
i = 0
for tup in slist:
tdist = 0
for entry in tup:
tdist += levenshtein(string, entry)
dist = tdist / (len(tup) * 1.0)
if dist < mdist or mdist == None:
mdist = dist
mind = i
i += 1
if mdist < 3:
return mind
else:
return None
def compare_ref(usertrans, targettrans):
"""
Takes the target translation and the user translation as inputs.
Based on their edit distance returns an evaluation.
@ targettrans: target translation (ideal translation of a text)
@ usertrans: translation provided by the user
"""
evaluation = {'very good': ['Superb translation!', 'Great work!', 'Perfect score!', 'High five!'], \
'good': ['Good translation!', 'Nice work!', 'Almost perfect!'], \
'fair': ['Not bad!', 'Almost there!'], \
'average': ['You can do better!', 'Shall we practice a little more?']
}
# encode to UTF-8
tt = targettrans.encode('utf-8')
ut = usertrans.encode('utf-8')
# remove punctuation
replace_punctuation = string.maketrans(string.punctuation, ' '*len(string.punctuation))
tt = tt.translate(replace_punctuation).lower()
ut = ut.translate(replace_punctuation).lower()
lev_let = lev.ratio(tt, ut)
tt = tt.split()
ut = ut.split()
lensum = len(tt)+len(ut)
lev_tok = (lensum - distance.levenshtein(tt, ut)) / lensum
ratio = max(lev_tok, lev_let)
if ratio >= 0.9:
return random.choice(evaluation['very good'])
elif ratio >= 0.75:
return random.choice(evaluation['good'])
elif ratio >= 0.6:
return random.choice(evaluation['fair'])
else:
return random.choice(evaluation['average'])
def lexem_best_match(token, lexems):
if len(lexems) == 0:
return token
# print "TOKEN: ", token
# print lexems
best_match = None
best_count = len(token)
for match in lexems:
if match[1] != 'P' and match[1] != 'V':
lexem = match[0].lower()
count_diff = distance.levenshtein(token, lexem)
if count_diff < best_count:
best_count = count_diff
best_match = lexem
# print "BEST_MATCH: ", best_match, best_count
if best_match:
return strip_diacritics_1(best_match.lower())
return None
def main_batches():
g = Graph(is_training=False)
# Load data
nums, X, ys = load_test_data()
pnyn2idx, idx2pnyn, hanzi2idx, idx2hanzi = load_vocab()
with g.graph.as_default():
sv = tf.train.Supervisor()
with sv.managed_session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# Restore parameters
sv.saver.restore(sess, tf.train.latest_checkpoint(hp.logdir)); print("Restored!")
# Get model
mname = open(hp.logdir + '/checkpoint', 'r').read().split('"')[1] # model name
with codecs.open('eval/{}_{}.csv'.format(mname, "qwerty" if hp.isqwerty else "nine"), 'w', 'utf-8') as fout:
fout.write("NUM,EXPECTED,{}_{},# characters,edit distance\n".format(mname, "qwerty" if hp.isqwerty else "nine"))
total_edit_distance, num_chars = 0, 0
for step in range(len(X)//hp.batch_size):
num = nums[step*hp.batch_size:(step+1)*hp.batch_size] #number batch
x = X[step*hp.batch_size:(step+1)*hp.batch_size] # input batch
y = ys[step*hp.batch_size:(step+1)*hp.batch_size] # batch of ground truth strings
preds = sess.run(g.preds, {g.x: x})
for n, xx, pred, expected in zip(num, x, preds, y): # sentence-wise
#got = "".join(idx2hanzi[str(idx)] for idx in pred)[:np.count_nonzero(xx)].replace("_", "")
got = "".join(idx2hanzi[idx] for idx in pred)[:np.count_nonzero(xx)].replace("_", "")
edit_distance = distance.levenshtein(expected, got)
total_edit_distance += edit_distance
num_chars += len(expected)
fout.write(u"{},{},{},{},{}\n".format(n, expected, got, len(expected), edit_distance))
fout.write(u"Total CER: {}/{}={},,,,\n".format(total_edit_distance,
num_chars,
round(float(total_edit_distance)/num_chars, 2)))