def get_test_feature(text_file, standardize_file, output_file):
param = json.loads(open(standardize_file, 'r').read())
text = load_text(text_file)
labels = [0] * len(text)
feat = [ ( {'name':'label', 'type':'{0, 1}'}, labels ) ]
'''
Call functions to extract features and add to data.
'''
feat += avg_sen_len(text)
feat += w2v_sim(text)
feat += ngram(text, './models/3.binlm', 'tri')
feat += ngram(text, './models/4.binlm', 'quad')
# text = load_raw_text(text_file)
# pos_feat = extract_pos_feat(text)
# with open('temp_pos', 'w') as f:
# for line in pos_feat:
# f.write(line)
# pos_labels = load_text('temp_pos')
# feat += ngram(pos_labels, './models/pos3.binlm', 'pos-tri')
# feat += ngram(pos_labels, './models/pos4.binlm', 'pos-quad')
'''
Output the libsvm file.
'''
arff_dump('temp_feat.arff', feat, param=param)
data, label = load_arff('temp_feat.arff')
to_libsvm(data, label, output_file)
def get_test_feature(text_file, standardize_file, output_file):
param = json.loads(open(standardize_file, 'r').read())
text = load_text(text_file)
labels = [0] * len(text)
feat = [({'name': 'label', 'type': '{0, 1}'}, labels)]
'''
Call functions to extract features and add to data.
'''
feat += avg_sen_len(text)
feat += w2v_sim(text)
feat += ngram(text, './models/3.binlm', 'tri')
feat += ngram(text, './models/4.binlm', 'quad')
# text = load_raw_text(text_file)
# pos_feat = extract_pos_feat(text)
# with open('temp_pos', 'w') as f:
# for line in pos_feat:
# f.write(line)
# pos_labels = load_text('temp_pos')
# feat += ngram(pos_labels, './models/pos3.binlm', 'pos-tri')
# feat += ngram(pos_labels, './models/pos4.binlm', 'pos-quad')
'''
Output the libsvm file.
'''
arff_dump('temp_feat.arff', feat, param=param)
data, label = load_arff('temp_feat.arff')
to_libsvm(data, label, output_file)
def run(train,nmax,reps,out): #Get probabilities with arbitrary precision fh = open(train) ngram.set_fractions(True) probs_ap=ngram.probabilities(ngram.good_turing(ngram.ngram(nmax,filters.unk(filters.shakespeare(fh))))) #Get probabilities with logs fh = open(train) ngram.set_fractions(False) probs_log=ngram.probabilities(ngram.good_turing(ngram.ngram(nmax,filters.unk(filters.shakespeare(fh))))) #Make sentences sentence_generation(train,out,nmax,reps,probs_ap,probs_log)
def ngram_plot(values, top = 10):
"""
Plot an ngram
:param values: Is either a string or an array containing strings
:param top: Is the number of results you want returned (optional, defaults to 10)
:return A matplotlib of the actual n gram
e.g.
ngram_plot(A1:10)
"""
dict = ngram(values)
max_freq = dict[0][1] #the max_frequency is the value of the first word
words = []
freq = []
for key, val in dict: #add words to matplotlib at random coordinates with font size relative to max frequency, rotate every other word
words.append(key)
y_pos = np.arange(len(words))
freq.append(val)
plt.bar(y_pos, freq, align='center')
plt.xticks(y_pos, words)
plt.ylabel('Frequency')
plt.title('Common Ngrams')
top -= 1
if top == 0:
break
return plt
def add_model(self, model_name):
self.model_results[model_name] = {}
#self.model_results[model_name]["batch count"] = 0
self.model_results[model_name]["count"] = 0
self.model_results[model_name]["BLEU_count"] = 0
self.model_results[model_name]["BLEU_count_arr"] = []
self.model_results[model_name]["BLEU"] = 0
self.model_results[model_name]["BLEU_arr"] = []
self.model_results[model_name]["context_len_arr"] = []
self.model_results[model_name]["context BLEU"] = 0
self.model_results[model_name]["gen_list_temp"] = []
self.model_results[model_name]["self BLEU count"] = 0
self.model_results[model_name]["self BLEU"] = 0
self.model_results[model_name]["self BLEU arr"] = []
self.model_results[model_name]["self BLEU count arr"] = []
self.model_results[model_name]["perplexity"] = 0
self.model_results[model_name]["ngram"] = ngram()
self.model_results[model_name]["ngram count"] = 0
self.model_results[model_name]["ngram count arr"] = []
self.model_results[model_name]["unigram"] = 0
self.model_results[model_name]["bigram"] = 0
self.model_results[model_name]["unigram arr"] = []
self.model_results[model_name]["bigram arr"] = []
self.model_results[model_name]["time_sum"] = 0
self.model_results[model_name]["time_count"] = 0
def checkPlagiarism(file):
isPlagiarized = False
grams = fileOpen(file)
ngrams = ngram(grams, 9)
ngrams = [' '.join(i) for i in ngrams]
for i in range(len(ngrams)):
driver = webdriver.Firefox()
toSearch = ngrams[i].encode('utf-8')
driver.get("http://google.com")
search = driver.find_element_by_name('q')
search.send_keys(ngrams[i])
search.send_keys(Keys.RETURN)
googleResult = googleSearch(toSearch)
search = driver.find_element_by_name('q')
search.send_keys(ngrams[i])
search.send_keys(Keys.RETURN)
for result in googleResult:
similarity = getSimilarity(toSearch,
strip_tags(result.description))
if similarity >= 70:
print("This file was plagiarized!")
isPlagiarized = True
driver.quit()
return isPlagiarized
driver.quit()
print("This file is original with no evidence of plagiarism.")
return isPlagiarized
def add_model(self, model_name):
self.model_results[model_name] = {}
self.model_results[model_name]["batch count"] = 0
self.model_results[model_name]["count"] = 0
self.model_results[model_name]["BLEU_count"] = 0
self.model_results[model_name]["BLEU_count_arr"] = []
self.model_results[model_name]["BLEU"] = 0
self.model_results[model_name]["BLEU_arr"] = []
self.model_results[model_name]["context_len_arr"] = []
self.model_results[model_name]["context BLEU"] = 0
self.model_results[model_name]["gen_list_temp"] = []
self.model_results[model_name]["self BLEU count"] = 0
self.model_results[model_name]["self BLEU"] = 0
self.model_results[model_name]["token hit"] = 0
self.model_results[model_name]["word type hit"] = 0
self.model_results[model_name]["topic hit"] = 0
self.model_results[model_name]["exact token hit"] = 0
self.model_results[model_name]["exact word type hit"] = 0
self.model_results[model_name]["exact topic hit"] = 0
self.model_results[model_name]["perplexity"] = 0
self.model_results[model_name]["ngram"] = ngram()
self.model_results[model_name]["unigram"] = 0
self.model_results[model_name]["bigram"] = 0
self.model_results[model_name]["time_sum"] = 0
self.model_results[model_name]["time_count"] = 0
def renew_ngram(self):
for model_name, model in self.model_results.items():
model["batch count"] += 1
unigram, bigram = model["ngram"].diversity_n()
#print(model["ngram"].diversity_n())
model["unigram"] += unigram
model["bigram"] += bigram
model["ngram"] = ngram()
def main():
import ngram
sentence = 'I am an NLPer'
word_2gram = ngram.word_ngram(sentence, 2)
char_2gram = ngram.ngram(sentence, 2)
print(word_2gram)
print(char_2gram)
def renew_ngram(self, head_idx):
for model_name, model in self.model_results.items():
model["ngram count"] += 1
model["ngram count arr"][head_idx] += 1
unigram, bigram = model["ngram"].diversity_n()
#print(model["ngram"].diversity_n())
model["unigram"] += unigram
model["bigram"] += bigram
model["unigram arr"][head_idx] += unigram
model["bigram arr"][head_idx] += bigram
model["ngram"] = ngram()
def getwords(doc):
menus = [
"备孕","怀孕","产后",
"发烧","胎教","母胎",
"疾病","营养","护理",
"疾病","孕妇","孕妈",
"生育",
]
tg = ngram(menus,min_sim=0.0)
words = tg.getSimilarStrings(doc.encode("utf8")).keys()
return dict([(w,1) for w in words])
def main():
import ngram
sentecne1 = 'paraparaparadise'
sentecne2 = 'paragraph'
X = set(ngram.ngram(sentecne1, 2))
Y = set(ngram.ngram(sentecne2, 2))
XYunion = X | Y
XYintersection = X & Y
XYdiference = X - Y
print(X)
print(Y)
print(XYunion)
print(XYintersection)
print(XYdiference)
if 'se' in X and 'se' in Y:
print("'se' is in X and Y")
else:
print("'se' is not in X and Y")
def makengramtext(n, fname):
fngram = fname[:-4] + '_{}-gram.txt'.format(n)
inittext(fngram)
with open(fname, mode='r', encoding='utf-8') as f:
for line in tqdm.tqdm(f):
line = line.rstrip()
line = re.sub(' ', 'R', line)
ngram_list = ngram.ngram(line, n)
with open(fngram, mode='a') as fn:
fn.write(' '.join(ngram_list) + '\n')
def compareAuthors(authors, compareDict, tg_dict):
# this dict contains how many texts (that we are comparing against the corpus) each author has written
authorMade = {}
# The dict that contains which authors have had their text attributed to whom
resultDict = {}
for value in compareDict:
textToCompare = value["text"]
realAuthor = value["user_id"]
# Compare value
compareTo = [textToCompare]
com = ngram.ngram(compareTo)
(gram, workList) = com.total_ngram(compareTo)
dataDist = {}
# We do the actual testing
for author in authors:
sum = 0.0
tg = tg_dict[author]
for word in workList:
sum += tg.propability(word, 0)
dataDist[author] = sum
value = -1
for an in dataDist.keys():
newValue = dataDist[an]
if newValue > value:
value = newValue
author = an
print "Real author:", realAuthor
print "Most likely author:", author
if not authorMade.has_key(author):
authorMade[author] = [realAuthor]
else:
authorMade[author].append(realAuthor)
# We take score of the attributions
if not resultDict.has_key(realAuthor) :
resultDict[realAuthor] = {author: 1}
elif not resultDict[realAuthor].has_key(author):
resultDict[realAuthor][author] = 1
else:
resultDict[realAuthor][author] += 1
return (authorMade, resultDict)
def makeNgram(filename):
worker = JSON.workOnJSON()
dict = worker.read_JSON_file(filename)
authorDict = {}
authorWrittenDict = {}
tg_dict = {}
authorNameDirec = {}
num = 0
for entry in dict:
author = entry["user_id"]
id = entry["post_id"]
value = {"user_id": author, "text": entry["text"]}
if authorDict.has_key(author):
authorDict[author].append(value)
authorWrittenDict[author].append(id)
else:
authorDict[author] = [value]
authorWrittenDict[author] = [id]
newAuthorDict = {}
authorTexts = {}
for authorName in authorDict.keys():
author = authorDict[authorName]
newAuthorDictTemp = {}
listOfEntries = [entry["text"] for entry in author]
newAuthorDict[authorName] = listOfEntries
text = ''.join(listOfEntries)
tg = ngram.ngram(listOfEntries)
tg.corp = text
tg.newRemember()
tg_dict[authorName] = tg
return (newAuthorDict, tg_dict)
def __init__(self, corpus, n):
# corpus, 训练标注器的语料， 格式为 [[('Hello', 'NNP'), ('world', 'NN'), ('!', '.')], [...], ...]
# n - 语言模型 n-gram 中的 n
# 定义词性标注任务
# 1. transition 为 n-gram 模型
# 2. emission 为 P( pos |Word )
# 3. initial distribution 为 P('START') = 1.0
# 预处理词库，给每句话加上开始和结束符号
brown_tags_words = []
for sent in corpus:
brown_tags_words.append(('START', 'START'))
brown_tags_words.extend([(tag[:2], word) for word, tag in sent])
brown_tags_words.append(('END', 'END'))
# 从语料集获得 emission - 统计条件概率
cfd_tagwords = ConditionalFreqDist(brown_tags_words)
# P(W = word, condition = pos)
cpd_tagwords = ConditionalProbDist(cfd_tagwords, MLEProbDist)
emission = {
tag:
{word: cpd_tagwords[tag].prob(word)
for word in cfd_tagwords[tag]}
for tag in cpd_tagwords
}
# 从语料集获得 transition - 调用 n-gram 模型
tags = [[tag for _, tag in sent] for sent in corpus]
transition = Transition(ngram(tags, n))
# 定义 initial distribution - 以 START 为句首， 概率为 1
initial_distribution = {('START', ): 1.0}
# 定义 词性标注器
HMM.__init__(self, initial_distribution, transition, emission, n)
def nlist(arr, n=2):
nlist = []
for text in arr:
nlist.extend(ngram(text, n))
return nlist
async def mimic(ctx, word, num_words=0):
"""Mimics Owen given a starting word."""
sentence = ngram.ngram(counts, word, num_words)
await ctx.send(sentence)
def __init__(self,fileName): self.sList = readXml(fileName) self.n = ngram.ngram() self.usedDist = 1;
def differenceSet(a,b):
product = productSet(a,b)
result = []
for w in a:
if w not in product:
result.append(w)
return result
if __name__ == '__main__':
from ngram import ngram
sentence1 = "paraparaparadise"
sentence2 = "paragraph"
X = ngram(2, sentence=sentence1, mode='char')
Y = ngram(2, sentence=sentence2, mode='char')
union = unionSet(X, Y)
product = productSet(X, Y)
difference1 = differenceSet(X, Y)
difference2 = differenceSet(Y, X)
print("unionSet is {0}".format(union))
print("productSet is {0}".format(product))
print("differenceSet X-Y is {0}".format(difference1))
print("differenceSet Y-X is {0}".format(difference2))
print("'se' is in X : {0}".format(str('se' in X)))
print("'se' is in Y : {0}".format(str('se' in Y)))
import ngram
import re
import string
from preprocess import *
from stemming.porter2 import stem
import nltk
import numpy as np
if __name__ == "__main__":
model = ngram.ngram(3)
# Train the model on text file preprocessed
with open("preprocessed/train_set.txt", 'r') as train_set:
model.train(train_set.read())
with open("Holmes.machine_format.questions.txt", 'r') as questions_machine, \
open("preprocessed/questions.txt", 'r') as questions_set, \
open("Holmes.machine_format.answers.txt", 'r') as ans_machine, \
open("preprocessed/answers.txt", 'r') as answers_set:
# prepare questions for preprocessing
questions = questions_machine.read().split('\n')
# format answers for easier comparison
answers = ans_machine.read().split('\n')
questions_set = questions_set.read().split('\n')
answers_set = answers_set.read().split('\n')
i = 0
sentences = []
words = ""
rights = 0
def getModels():
tru_unigram = ngram.ngram('true.train', 1, ngram.Smooth.GOOD_TURING, True)
fal_unigram = ngram.ngram('false.train', 1, ngram.Smooth.GOOD_TURING, True)
tru_uni_rl = ngram.ngram('true.train', 1, ngram.Smooth.GOOD_TURING, True, ngram.Direction.RL)
fal_uni_rl = ngram.ngram('false.train', 1, ngram.Smooth.GOOD_TURING, True, ngram.Direction.RL)
tru_bigram = ngram.ngram('true.train', 2, ngram.Smooth.GOOD_TURING, True)
fal_bigram = ngram.ngram('false.train', 2, ngram.Smooth.GOOD_TURING, True)
tru_bi_rl = ngram.ngram('true.train', 2, ngram.Smooth.GOOD_TURING, True, ngram.Direction.RL)
fal_bi_rl = ngram.ngram('false.train', 2, ngram.Smooth.GOOD_TURING, True, ngram.Direction.RL)
#trial code
tru_trigram = ngram.ngram('true.train', 3, ngram.Smooth.GOOD_TURING, True)
fal_trigram = ngram.ngram('false.train', 3, ngram.Smooth.GOOD_TURING, True)
tru_tri_rl = ngram.ngram('true.train', 3, ngram.Smooth.GOOD_TURING, True, ngram.Direction.RL)
fal_tri_rl = ngram.ngram('false.train', 3, ngram.Smooth.GOOD_TURING, True, ngram.Direction.RL)
tru_quadgram = ngram.ngram('true.train', 4, ngram.Smooth.GOOD_TURING, True)
fal_quadgram = ngram.ngram('false.train', 4, ngram.Smooth.GOOD_TURING, True)
return [tru_unigram, fal_unigram, tru_bigram, fal_bigram, tru_trigram, fal_trigram, tru_quadgram, fal_quadgram
, tru_uni_rl, fal_uni_rl, tru_bi_rl, fal_bi_rl, tru_tri_rl, fal_tri_rl]
def train(self, tokens):
bigram = ngram(tokens)
for s1, s2 in bigram:
self.chains.setdefault(s1, [])
self.chains[s1].append(s2)
import ngram str1 = "paraparaparadise" str2 = "paragraph" tool = ngram.ngram() list1 = tool.literaln(str1, 2) list2 = tool.literaln(str2, 2)
def train(self, tokens):
bigram = ngram(tokens)
for s1, s2 in bigram:
self.chains.setdefault(s1, [])
self.chains[s1].append(s2)
# Call rate Labeling
import ngram
import pandas as pd
ng = ngram.ngram()
fl = ng.search(r'C:\Users\student\nlp_project\bok_project\ngram\ngram_data')
for doc_num in range(len(fl)):
datas = ng.select_file(doc_num)
if doc_num == 0:
ngram_df = datas[['date', 'ngram']]
else:
ngram_df = pd.concat([ngram_df, datas[['date', 'ngram']]])
ngram_df.reset_index()[['date', 'ngram']].to_json('total_ngram.json')
def __ngram(x):
if skipgram:
return skip_bigram(x, max_step=skipgram)
if n > 1:
return ngram(x, n=n)
return x
import ngram str1 = "paraparaparadise" str2 = "paragraph" tool = ngram.ngram() list1 = tool.literaln(str1,2) list2 = tool.literaln(str2,2)
def nlist(arr, n=2):
nlist = []
for text in arr:
nlist.extend(ngram(text, n))
return nlist
def __init__(self):
# Conversion of some turkish language specific characters
self.charList = {"\x80":"c",
"\x8e":"a",
"\x99":"o",
"\xa3":"u",
"\x81":"",
"\x89":"",
"\x93":"",
"\x94":"",
"\xa1":"i",
"\xad":"",
"\xb0":"",
"\xb1":"",
"\xb3":"",
"\xba":"",
"\xe3":"",
"\xe4":"a",
chr(0xe2):"o", # 0xe2 karakterini silemiyorum!!! :S
"é":"e",
"ğ":"g",
"ü":"u",
"ş":"s",
"ı":"i",
"ö":"o",
"ç":"c",
"Ğ":"G",
"Ü":"U",
"Ş":"S",
"İ":"I",
"Ö":"O",
"Ç":"C",
"!":"",
",":"",
".":"",
";":"",
":":"",
")":"",
"(":"",
"'":"",
'"':"",
"1":"",
"2":"",
"3":"",
"4":"",
"5":"",
"6":"",
"7":"",
"8":"",
"9":"",
"0":"",
"-":"",
"_":"",
"?":"",
"%":"",
"$":"",
"&":"",
"/":"",
"\\":""} #This set is not complete!!
self.n = ngram.ngram()
self.feedList = []
def __init__(self):
# Conversion of some turkish language specific characters
self.charList = {
"\x80": "c",
"\x8e": "a",
"\x99": "o",
"\xa3": "u",
"\x81": "",
"\x89": "",
"\x93": "",
"\x94": "",
"\xa1": "i",
"\xad": "",
"\xb0": "",
"\xb1": "",
"\xb3": "",
"\xba": "",
"\xe3": "",
"\xe4": "a",
chr(0xe2): "o", # 0xe2 karakterini silemiyorum!!! :S
"é": "e",
"ğ": "g",
"ü": "u",
"ş": "s",
"ı": "i",
"ö": "o",
"ç": "c",
"Ğ": "G",
"Ü": "U",
"Ş": "S",
"İ": "I",
"Ö": "O",
"Ç": "C",
"!": "",
",": "",
".": "",
";": "",
":": "",
")": "",
"(": "",
"'": "",
'"': "",
"1": "",
"2": "",
"3": "",
"4": "",
"5": "",
"6": "",
"7": "",
"8": "",
"9": "",
"0": "",
"-": "",
"_": "",
"?": "",
"%": "",
"$": "",
"&": "",
"/": "",
"\\": ""
} #This set is not complete!!
self.n = ngram.ngram()
self.feedList = []
param = None
if len(sys.argv) > 4:
standardize_file = sys.argv[4]
param = json.loads(open(standardize_file, 'r').read())
text = load_text(text_file)
labels = [int(line.strip()) for line in open(label_file, 'r')]
data = [ ( {'name':'label', 'type':'{0, 1}'}, labels ) ]
assert len(labels) == len(text)
'''
Call functions to extract features and add to data.
'''
data += avg_sen_len(text)
data += w2v_sim(text)
data += ngram(text, './models/3.binlm', 'tri')
data += ngram(text, './models/4.binlm', 'quad')
#print pos_file
#pos_labels = load_text(pos_file)
#data += ngram(pos_labels, './models/pos3.binlm', 'pos-tri')
#data += ngram(pos_labels, './models/pos4.binlm', 'pos-quad')
'''
Output the arff file.
'''
param = arff_dump(output_file, data, param=param)
if len(sys.argv) <= 4:
f = open('param.json', 'w')
f.write(json.dumps(param, indent = 2))
import ngram
import re
import string
from preprocess import *
from stemming.porter2 import stem
import nltk
import numpy as np
if __name__ == "__main__":
model = ngram.ngram(3)
# Train the model on text file preprocessed
with open("preprocessed/train_set.txt", 'r') as train_set:
model.train(train_set.read())
with open("Holmes.machine_format.questions.txt", 'r') as questions_machine, \
open("preprocessed/questions.txt", 'r') as questions_set, \
open("Holmes.machine_format.answers.txt", 'r') as ans_machine, \
open("preprocessed/answers.txt", 'r') as answers_set:
# prepare questions for preprocessing
questions = questions_machine.read().split('\n')
# format answers for easier comparison
answers = ans_machine.read().split('\n')
questions_set = questions_set.read().split('\n')
answers_set = answers_set.read().split('\n')
i = 0
sentences = []
words = ""
rights = 0
pos_file = './data/' + pos_file
param = None
if len(sys.argv) > 4:
standardize_file = sys.argv[4]
param = json.loads(open(standardize_file, 'r').read())
text = load_text(text_file)
labels = [int(line.strip()) for line in open(label_file, 'r')]
data = [({'name': 'label', 'type': '{0, 1}'}, labels)]
assert len(labels) == len(text)
'''
Call functions to extract features and add to data.
'''
data += avg_sen_len(text)
data += w2v_sim(text)
data += ngram(text, './models/3.binlm', 'tri')
data += ngram(text, './models/4.binlm', 'quad')
#print pos_file
#pos_labels = load_text(pos_file)
#data += ngram(pos_labels, './models/pos3.binlm', 'pos-tri')
#data += ngram(pos_labels, './models/pos4.binlm', 'pos-quad')
'''
Output the arff file.
'''
param = arff_dump(output_file, data, param=param)
if len(sys.argv) <= 4:
f = open('param.json', 'w')
f.write(json.dumps(param, indent=2))
f.close()
import ngram
import list_subtraction
a="paraparaparadise"
b="paragraph"
x=sorted(set(ngram.ngram(a,2)))
y=sorted(set(ngram.ngram(b,2)))
print("x:",x)
print("y:",y)
w=x+y
""" 関数ngramの内容
def ngram(text,n):
#n字数ごとずらす
x=[]
for y in range(len(text)):
if y==len(text)-n+1:
break;
x.append(text[y:y+n])
return x
"""
f=sorted(set(w))
#set型は重複しない要素（同じ値ではない要素、ユニークな要素）のコレクション
#しかし元の順序とは異なる
s=sorted(set(list_subtraction.list_subtraction(w,f)))
c=sorted(set(list_subtraction.list_subtraction(x,s)))
def test_ngram_twogram(self):
import ngram
cnts = ngram.ngram(n=2, k=1, document='test.txt')
self.assertTrue(cnts.has_key("a a"), 'ngram does not contain appropriate keys' + str(cnts))
self.assertEqual(cnts["a a"], 2, 'ngram count is incorrect' + str(cnts))
self.assertEqual(len(cnts), 1, 'ngram should only contain top k counts' + str(cnts))