def find_all(self):
cursor = self.find()
software = cursor.distinct('software_name')
os = cursor.distinct('os_name')
company = cursor.distinct('company_name')
cursor.close()
return flatten(flatten(software,os),company)
def compare(matrix, possible_matrix_list):
cost = []
for possible_matrix in possible_matrix_list:
matrix_cost = 0
possible_matrix = flatten(possible_matrix)
final_matrix = flatten(matrix)
for i, j in zip(final_matrix, possible_matrix):
if i == b:
pass
elif i != j:
matrix_cost += 1
cost.append(matrix_cost)
return cost
def compare(matrix,possible_matrix):
cost=[]
for possible in possible_matrix:
mat_cost=0
possible=flatten(possible)
matrix=flatten(matrix)
for i,j in zip(matrix,possible):
if i==b:
pass
elif(i!=j):
mat_cost+=1
cost.append(mat_cost)
return cost
def __iter__(self):
n=len(self.uniblocks)
indreorder=torch.randperm(n).tolist()
batch=[]
for idx in indreorder:
indnew=list(range(idx*self.blocksize,(idx+1)*self.blocksize))
batch.append(indnew)
if len(batch)==self.nblock: ##number of block in each minibatch
yield flatten(batch)
batch=[]
if len(batch) > 0:
yield flatten(batch)
def extract_zip_code(texts):
'''
Concat the address without zip code and the zip code from zip_code_submethod method.
Args:
texts (string): addresses with and without seprated by delimeter.
Returns:
concat_address (string) : address concat with delimeter
concat_postal_code (string) : concat list of zipcode
'''
delimeter = '|'
address_list, postal_code_list = list(), list()
if str(texts) != 'nan' and texts != 'N/A' and texts != None:
if delimeter in texts:
for text in texts.split(delimeter):
address_list_val, postal_code_val = zip_code_submethod(
Constants.zip_code_regex, text)
address_list.append(address_list_val)
postal_code_list.append(postal_code_val)
else:
postal_code = re.findall(Constants.zip_code_regex, texts)
address_list_val, postal_code_val = zip_code_submethod(
Constants.zip_code_regex, texts)
address_list.append(address_list_val)
postal_code_list.append(postal_code_val)
concat_address = delimeter.join(address_list)
concat_postal_code = flatten(postal_code_list)
return concat_address, concat_postal_code
def process_natural_text(body):
toks_ = [[
l.strip() for l in re.findall(CODE_TOKENISATION_REGEX, line.strip())
if len(l.strip()) > 0
] for line in body.split('\n')]
toks_ = flatten(toks_)
return [(w, t, 0) for w, t in pos_tag(toks_, tagset='universal')]
def getSortedCountMap(col, rx):
backupColName = 'backup_' + col
train[backupColName] = train[col].apply(lambda x: toJson(x, rx)
if type(x) is str else [])
sortedList = sorted(flatten(list(train[backupColName].values)))
countMap = [(i, len(list(c))) for i, c in groupby(sortedList)]
return sorted(countMap, key=lambda x: x[1], reverse=1)
def preprocess(self,text):
check = re.search(r'([a-z])\1+',text)
if check:
if len(check.group())>2:
text = re.sub(r'([a-z])\1+', lambda m: m.group(1), text, flags=re.IGNORECASE) #remove các ký tự kéo dài như hayyy,ngonnnn...
text = text.strip() #loại dấu cách đầu câu
text = text.lower() #chuyển tất cả thành chữ thường
text = re.sub('< a class.+</a>',' ',text)
for k, v in self.replace_list.items(): #replace các từ có trong replace_list
text = text.replace(k, v)
text = re.sub(r'_',' ',text)
text = ' '.join(i for i in flatten(tokenize(text).split(" "))) #gán từ ghép
for i in self.Pos_list: #thêm feature positive
if re.search(' '+i+' ',text):
text = re.sub(i,i+' positive ',text)
for i in self.Neg_list: #thêm feature negative
if re.search(' '+i+' ',text):
text = re.sub(i,i+' negative ',text)
return text
def get_traindata(input_files):
if (len(input_files) == 0):
raise Exception('empty values')
list_filedata = []
list_filepaths = []
#print(input_files)
for eachfile in input_files:
for filename in eachfile:
filename = filename
filepaths = glob.glob(str(filename))
#print(filepaths)
list_filepaths.append(filepaths)
filepaths_list = nltk.flatten(list_filepaths)
filepaths_list = filepaths_list[:200]
print('Text files in Train data', len(filepaths_list))
#print(filepaths_list)
for filepath in filepaths_list:
fileopen = open(filepath)
file_data = fileopen.read()
file_data = file_data.replace("<br />", " ")
#file_data = re.sub(r'[^a-zA-Z0-9]'," ",file_data)
list_filedata.append(file_data)
fileopen.close()
#print(list_filedata[0:10])
return list_filedata
def Update_Output(inputfiles,files_data,outputpath):
# print((outputpath))
filenames =[]
files = nltk.flatten(inputfiles)
for i in range(len(files)):
input_files = glob.glob(files[i])
# print(input_files)
for j in range(len(input_files)):
# print(type(input_files[j]))
if '.txt' in input_files[j]:
input_files[j] = input_files[j].replace(".txt", ".redacted.txt")
if '.md' in input_files[j]:
input_files[j] = input_files[j].replace(".md", ".redacted.txt")
if '\\' in input_files[j]:
input_files[j]= input_files[j].split("\\")
input_files[j] = input_files[j][1]
# print(input_files[j])
filenames.append(input_files[j])
for i in range(len(files_data)):
for j in range(len(filenames)):
if i==j:
file_data =files_data[i]
# print((filenames[i]))
path1 = (os.getcwd())
# print(outputpath+filenames[j])
path2 = (outputpath+'/'+filenames[j])
final_file = open(os.path.join(path1,path2), "w" ,encoding="utf-8")
# print(os.path.join(path1,path2))
final_file.write(file_data)
final_file.close()
return len(filenames)
def get_partial_template(formId, count, key, value):
s = ''
for i in value:
# print(f"{key} - {i}")
if not i:
pass
elif len(i) == 1: # single <p> paragraph
count += 1
s += f"\n\n<p>{get_message_with_affinity(formId, count)}</p>"
elif isinstance(i[0], str) and i[0].endswith(':'): # a <ul> list
if len(i) == 2:
result, count = get_list_template(i, formId, count, False)
s += f"\n\n<p>{result}</p>"
else:
result, count = get_list_template(i[:2], formId, count, False)
s += f"\n\n<p>{result}</p>"
count, ts = get_partial_template(formId, count, key, [i[2:]])
s += f"{ts}"
elif i == flatten(i) and index_of_urls(
i): # paragraph <p> with <a> links
count, ts = get_link_template(i, formId, count + 1,
index_of_urls(i))
if len(index_of_urls(i)) == 1:
s += f"\n\n<p>@genericLink(params, \"{formId}\", {ts})</p>"
else:
s += f"\n\n{ts}"
else:
print(
"ERROR : An unhandled variation was encountered when generating GUIDE PAGE TEMPLATE. This is most likely due to a missing html tag in guide page messages in dfs-frontend."
)
return count, s
def get_hypernyms(synset):
hypernyms = set()
for hyponym in synset.hypernyms():
hypernyms |= set(get_hypernyms(hyponym))
result_syns = hypernyms | set(synset.hypernyms())
result = set(flatten([list(x.lemmas()) if isinstance(x, Synset) else x for x in result_syns]))
return result
def get_all_tokens(c):
res = c.copy()
children = nltk.flatten([token.children for token in c])
if len(children) > 0:
res += UDPipeKeywordsExtractor.get_all_tokens(children)
return res
def get_antonyms(synset):
antonyms = set()
new_antonyms = set()
for lemma in synset.lemmas():
new_antonyms |= set(lemma.antonyms())
antonyms |= new_antonyms
for antonym in new_antonyms:
antonyms |= set(flatten([list(x.lemmas()) for x in antonym.synset().similar_tos()]))
return antonyms
def get_all_tokens(x):
res = x.copy()
children = nltk.flatten([token.children for token in x])
if len(children) > 0:
res += UDPipeModel.get_all_tokens(children)
return res
def input(files):
Read_data = []
x = []
for j in files:
for file in j:
data = glob.glob(str(file))
x.append(data)
y = nltk.flatten(x)
for i in y:
Read_data.append(open(i).read())
return(Read_data)
def evaluate(self, X):
"""
Evaluate the classifier.
:param X: data to test on
:return: evaluation results
"""
features, labels = separate_labels_from_features(X)
# get predictions for data
y = self.predict(features)
n_sent_correct = 0
num_sent = len(y)
for i in range(len(labels)):
if labels[i] == y[i]:
n_sent_correct += 1
labels = nltk.flatten(labels)
y = nltk.flatten(y)
print("F1 score:")
print(
sklearn.metrics.precision_recall_fscore_support(labels,
y,
average='micro'))
print()
print("Accuracy:")
print(sklearn.metrics.accuracy_score(labels, y))
print()
print("Sentence level accuracy:")
print(n_sent_correct / num_sent)
print()
print("F1 score per class:")
print(sklearn.metrics.precision_recall_fscore_support(labels, y))
print()
print("Confusion matrix:")
cfm = sklearn.metrics.confusion_matrix(labels, y)
plot_confusion_matrix(cfm, np.unique(labels))
def tokenizeSentence(input):
sentences = [
re.sub(pattern=r'[\!"#$%&\*+,-./:;<=>?@^_`()|~=]', repl=' ',
string=x).strip().split(' ') for x in input.split('\n')
if not x.endswith('writes:')
]
sentences = [x for x in sentences if x != ['']]
flat_list = flatten(sentences)
flat_list = [x for x in flat_list if x != '']
stopwords_german = set(stopwords.words('german'))
filtered_tokens = [w for w in flat_list if not w in stopwords_german]
return filtered_tokens
def extract(self, keywords, threshold=3, sampler=Sampler.last.value):
"""
Extracts the relevant phrases to use for comparison with an incoming query for computing veracity
keywords (list) - Query of keywords
threshold (int) - Controls number of sentences to return (-1 for all sentences)
sampler(Sampler) - Returns first-n, last-n, random-n or best-n number of sentences (heuristic)
return (list) - List of phrases and sentences to use for veracity computation
"""
sentences = TextBlob(
self.body).sentences # Extract sentences from article body
relevant = {}
for sentence in sentences:
sentence = str(sentence).decode('utf-8').lower().strip()
tokens = word_tokenize(sentence)
tokens = [t.lower() for t in tokens if t not in punctuation]
common = len([value for value in keywords if value in tokens])
if common == 0: continue
if common not in relevant.keys(): relevant[common] = []
relevant[common].append(' '.join(tokens))
if threshold == -1: return flatten(relevant.values())
if sampler == Sampler.last.value:
return flatten(relevant.values())[-threshold:]
elif sampler == Sampler.first.value:
return flatten(relevant.values())[:threshold]
elif sampler == Sampler.random.value:
shuffle(flatten(relevant.values()))
return relevant[-threshold:]
elif sampler == Sampler.best.value:
result = []
for key in sorted(relevant.keys(),
reverse=True): # Sort by keyword hits
result.extend(relevant[key])
if len(result) >= threshold: break
return result[:threshold]
def get_pertainyms(synset):
pertainyms = set()
new_pertainyms = set()
for lemma in synset.lemmas():
new_pertainyms |= set(lemma.pertainyms())
pertainyms |= new_pertainyms
for pertainym in new_pertainyms:
pertainyms |= set(
flatten([
list(x.pertainyms())
for x in pertainym.synset().similar_tos()
]))
return pertainyms
def generate_guide_template(formId, userType, stats):
folder = templateUrl + '/app/uk/gov/hmrc/dfstemplaterenderer/templates/guidePageTemplates' + f"/{formId}"
if not os.path.exists(folder):
os.mkdir(folder)
pInfo(f"Folder {formId} created")
if not os.path.exists(folder + f"/{userType}"):
os.mkdir(folder + f"/{userType}")
pInfo(f"Folder {userType} created")
if os.path.isfile(folder + f"/{userType}/{formId}.scala.html"):
pWarn('Guide page template already exists')
else:
f = open(folder + f"/{userType}/{formId}.scala.html", 'w')
f.writelines(get_copyright())
f.writelines([
"\n\n@import play.twirl.api.Html",
"\n@import uk.gov.hmrc.dfstemplaterenderer.templates.guidePageTemplates.helpers.genericHelpers.html._",
"\n@import uk.gov.hmrc.dfstemplaterenderer.utils._",
"\n@import uk.gov.hmrc.dfstemplaterenderer.models.LinkTemplate",
"\n\n@(params: Map[String, Any])",
f"\n\n@baseGenericGuidePageHeader(params, \"{formId}\")"
])
print(f"\n\nstats:{stats}")
count = 0
for key, value in stats.items():
print(f"k = {key}, v = {value}")
if key == 'list':
f.write(f"\n\n@noLinkList(params, \"{formId}\", Seq(")
for i in range(1, len(flatten(value))):
if i > 1:
f.write(", ")
count += 1
f.write(f"\"guide.{count:02d}\"")
f.write("))")
elif key == 'extraInfo' or key == 'beforeStart':
if key == 'beforeStart':
f.write(
f"\n\n@baseGenericGuidePageBody(params, \"{formId}\")")
count, text = get_partial_template(formId, count, key,
value[1:])
f.write(text)
else:
pError(
"The detected set of messages are not of types: header, list, extraInfo or beforeStart."
)
if userType == 'Individual':
f.write(
"\n\n<p>@MessagesUtils.getCommonMessages(\"page.guide.youCanTrack\", {params(\"langLocaleCode\")}.toString) <a href=\"@Links.ptaLink\">@MessagesUtils.getCommonMessages(\"abandon.pta.link.msg\", {params(\"langLocaleCode\")}.toString)</a> </p>"
)
f.close()
def get_derivationally_related_forms(synset):
derivationally_related_forms = set()
new_derivationally_related_forms = set()
for lemma in synset.lemmas():
new_derivationally_related_forms |= set(
lemma.derivationally_related_forms())
derivationally_related_forms |= derivationally_related_forms
for derivationally_related_form in derivationally_related_forms:
derivationally_related_forms |= set(
flatten([
list(x.derivationally_related_forms()) for x in
derivationally_related_forms.synset().similar_tos()
]))
return derivationally_related_forms
def create_multi_batch(titles, bodies, padding_id, pad_left,
generated_questions):
questions_count = [len(gq) + 1 for gq in generated_questions]
titles = flatten(zip(titles, generated_questions))
bodies = flatten([[b] * questions_count[i]
for (i, b) in enumerate(bodies)])
assert len(titles) == len(bodies)
assert sum(questions_count) == len(titles)
max_title_len = max(1, max(len(x) for x in titles))
max_body_len = max(1, max(len(x) for x in bodies))
if pad_left:
titles = np.column_stack([
np.pad(x, (max_title_len - len(x), 0),
'constant',
constant_values=padding_id) for x in titles
])
bodies = np.column_stack([
np.pad(x, (max_body_len - len(x), 0),
'constant',
constant_values=padding_id) for x in bodies
])
else:
titles = np.column_stack([
np.pad(x, (0, max_title_len - len(x)),
'constant',
constant_values=padding_id) for x in titles
])
bodies = np.column_stack([
np.pad(x, (0, max_body_len - len(x)),
'constant',
constant_values=padding_id) for x in bodies
])
return titles, bodies, questions_count
def wordnet_lookup_xnyms (index_to_tokens, fun):
xnym_dict = OrderedDict()
lemma_vocab = set (porter.stem(word) for word in index_to_tokens.values())
for token in lemma_vocab:
xnyms_syns = set()
for syn in wordnet.synsets(token):
xnyms_syns |= fun(syn)
lemmas = set(flatten([list(x.lemmas()) if isinstance(x, Synset) else x for x in xnyms_syns]))
strings = [split_multi_word(x.name()) for x in lemmas]
xnym_dict[(token,)] = strings
return xnym_dict
def pair_spans(self, spans: List[Dict[str,
Any]]) -> List[List[Dict[str, Any]]]:
indices = sorted(
list(
set(
flatten([
list(range(d['start'], d['end'])) for d in spans
if d['able']
]))))
rs = ranges(indices)
paired_spans = [[
d for d in spans
if d['start'] >= r_start and d['end'] <= r_end and d['able']
] for r_start, r_end in rs]
return paired_spans
def output(files, data, name):
allfiles = []
for i in files:
for file in i:
allfiles.append(glob.glob(file))
flattenf = nltk.flatten(allfiles)
newfilepath = os.path.join(os.getcwd(), name)
for j in range(len(flattenf)):
getpath = os.path.splitext(flattenf[j])[0]
getpath = os.path.basename(getpath) + '.redacted'
if not os.path.exists(newfilepath):
os.makedirs(newfilepath)
with open(os.path.join(newfilepath, getpath), 'w') as temp:
temp.write(data[j])
elif os.path.exists(newfilepath):
with open(os.path.join(newfilepath, getpath), 'w') as temp:
temp.write(data[j])
def main():
papers_df = readData(["pdf_json", "pmc_json"], cfg["data_path"])
papers_df[
"abstract_fullText"] = papers_df["abstract"] + papers_df["full_text"]
papers_df.drop(columns=['title', 'abstract', 'full_text'], inplace=True)
text_preprocessor = Preprocessor()
papers_df['abstract_fullText_Cleaned'] = papers_df[
'abstract_fullText'].map(lambda text: text_preprocessor.
clean_and_tokenize(text,
stop=False,
lowercase=True,
removeUrls=False,
remove_html=False,
lemmatize=False,
remove_numbers=False,
tokenize=False))
terms = papers_df['abstract_fullText_Cleaned'].map(
lambda text: text_preprocessor.clean_and_tokenize(text,
stop=True,
lowercase=False,
removeUrls=True,
remove_html=True,
lemmatize=False,
remove_numbers=True,
tokenize=True))
arrayTerm = terms.ravel().tolist()
flattenedTerms = flatten(arrayTerm)
## Create Vocabulary
vocabulary = set(flattenedTerms)
vocabulary = list(vocabulary)
# Intializating the tfIdf model
tfidf = TfidfVectorizer(vocabulary=vocabulary,
dtype=np.float32,
min_df=0,
max_df=0.8,
use_idf=True,
smooth_idf=True,
sublinear_tf=True)
# Fit the TfIdf model
tfidf.fit(papers_df.abstract_fullText_Cleaned)
dictIDF = dict(zip(tfidf.vocabulary_, tfidf.idf_))
save_obj(dictIDF, "dictIDF", 'txt')
print("Dictionary successfully created")
def Reading_input(inputfiles):
# print(inputfiles)
files_data = []
files = nltk.flatten(inputfiles)
for i in range(len(files)):
# print(files[i])
input_files = glob.glob(files[i])
# print(input_files1)
for j in range(len(input_files)):
# print(input_files1[j])
data = open(input_files[j]).read()
# print(data)
files_data.append(data)
# print(type(files_data))
# print(len(files_data))
return files_data
def data_input(input_files):
if (len(input_files) == 0):
raise Exception('empty values')
list_filedata = []
list_filepaths = []
for eachfile in input_files:
for filename in eachfile:
filename = filename
filepaths = glob.glob(str(filename))
list_filepaths.append(filepaths)
filepaths_list = nltk.flatten(list_filepaths)
for filepath in filepaths_list:
fileopen = open(filepath)
file_data = fileopen.read()
list_filedata.append(file_data)
fileopen.close()
return list_filedata
def run(self, min_wd_cnt = 5, stem = True, spelling_correct = True, folds = 10):
""" We don't want to remove stop words
"""
sentences, tagged_sentences = self.load_tagged_sentences()
processed_sentences = self.process_sentences(sentences, spelling_correct, stem, min_wd_cnt)
sentence_features = np.asarray( map(self.features_for_sentence, processed_sentences))
cross_validation_ixs = cross_validation(range(len(sentences)), folds)
codes = sorted(set(flatten(tagged_sentences)))
for code in codes:
code_tags = self.tags_for_code(code, tagged_sentences)
pass
pass
def file_output(files, data, filename):
list_files = []
for i in files:
for file in i:
list_files.append(glob.glob(file))
flattenfiles = nltk.flatten(list_files)
newfilepath = os.path.join(os.getcwd(), filename)
j = 0
while j < len(flattenfiles):
getpath = os.path.splitext(flattenfiles[j])[0]
getpath = os.path.basename(getpath) + '.redacted'
if not os.path.exists(newfilepath):
os.makedirs(newfilepath)
with open(os.path.join(newfilepath, getpath), 'w') as outputfile:
outputfile.write(data[j])
elif os.path.exists(newfilepath):
with open(os.path.join(newfilepath, getpath), 'w') as outputfile:
outputfile.write(data[j])
j = j + 1
def chunks(file):
f = open(file)
raw = f.read()
tokens = nltk.word_tokenize(raw)
tagged_tokens = nltk.pos_tag(tokens)
# The patter for this grammar is repeated twice in order to only find noun phrases with two or more words
grammar = "NP: {<JJ.*>*<NN.*>+ <JJ.*>*<NN.*>+}"
# Other possible grammars:
# grammar = "NP: {<DT>?<JJ.*>*<NN.*>+}"
# grammar = r"""
# NP: {<DT|PP\$>?<JJ>*<NN>} # chunk determiner/possessive, adjectives and nouns
# {<NNP>+} # chunk sequences of proper nouns
# """
# grammar = r"""
# NP: {<DT><NN.*><.*>*<NN>}
# }<VB.*>{
# """
cp = nltk.RegexpParser(grammar)
chunks_tree = cp.parse(tagged_tokens)
# The result from the chunk parser is a tree. Here I'm finding all the Noun Phrases subtrees,
# flattening them into lists, and converting those lists to tuples. This way we end up with the
# same data structure that we get from pos_tag()
np_subtrees = list(chunks_tree.subtrees(filter=lambda x: x.node=='NP'))
flatten_np_subtrees = [tuple(nltk.flatten(t)) for t in np_subtrees]
result = []
for item in flatten_np_subtrees:
noun_phrase = ''
for n in range(len(item)):
if n % 2 == 0:
noun_phrase += item[n]
noun_phrase += ' '
result.append((noun_phrase.rstrip(), 'NP'))
return result
def chunks(file):
f = open(file)
raw = f.read()
tokens = nltk.word_tokenize(raw)
tagged_tokens = nltk.pos_tag(tokens)
# The patter for this grammar is repeated twice in order to only find noun phrases with two or more words
grammar = "NP: {<JJ.*>*<NN.*>+ <JJ.*>*<NN.*>+}"
# Other possible grammars:
# grammar = "NP: {<DT>?<JJ.*>*<NN.*>+}"
# grammar = r"""
# NP: {<DT|PP\$>?<JJ>*<NN>} # chunk determiner/possessive, adjectives and nouns
# {<NNP>+} # chunk sequences of proper nouns
# """
# grammar = r"""
# NP: {<DT><NN.*><.*>*<NN>}
# }<VB.*>{
# """
cp = nltk.RegexpParser(grammar)
chunks_tree = cp.parse(tagged_tokens)
# The result from the chunk parser is a tree. Here I'm finding all the Noun Phrases subtrees,
# flattening them into lists, and converting those lists to tuples. This way we end up with the
# same data structure that we get from pos_tag()
np_subtrees = list(chunks_tree.subtrees(filter=lambda x: x.node == 'NP'))
flatten_np_subtrees = [tuple(nltk.flatten(t)) for t in np_subtrees]
result = []
for item in flatten_np_subtrees:
noun_phrase = ''
for n in range(len(item)):
if n % 2 == 0:
noun_phrase += item[n]
noun_phrase += ' '
result.append((noun_phrase.rstrip(), 'NP'))
return result
def top_ten(self):
from operator import itemgetter
d = self._group(key='software_name')
d=flatten(d,self._group('os_name'))
proper = []
extras = []
s = set()
for map in d:
proper.append(dict(name=map['os_name'] if 'os_name' in map else map['software_name'], count=map['count']))
for map in proper[:]:
if isinstance(map['name'],list):
extras.append(map)
proper.remove(map)
else: s.add(map['name'])
for map in extras:
self._aggregate(key='name',
map=map,
set=s,
list=proper)
return sorted(proper, key=itemgetter('count'), reverse=True)[:10]