def _similarity(self, sentences):
"""
N-gram with similarity.
The NGram class extends the Python ‘set’ class with efficient fuzzy search for members by
means of an N-gram similarity measure.
Reference:
Vacláv Chvátal and David Sankoff.
Longest common subsequences of two random sequences, 1975.
Journal of Applied Probability,
Python module: ngram (https://pypi.org/project/ngram/)
"""
ngram = NGram(self.corpus.split(), key=lambda x: x.lower(), N=self.N)
predicts = []
if not isinstance(sentences, list):
sentences = [sentences]
for i in range(len(sentences)):
split = []
for x in sentences[i].split():
sugg = ngram.find(
x.lower()) if x not in string.punctuation else None
split.append(sugg if sugg else x)
predicts.append(" ".join(split))
return predicts
class PMI(object):
"""docstring for PMI"""
def __init__(self, lang, uri=None, ngram=False):
self.client = pymongo.MongoClient(uri)
self.uri = uri
self.lang = lang
self.db = self.client['nlp_{}'.format(self.lang)]
self.fs = gridfs.GridFS(self.db)
self.Collect = self.db['pmi']
self.cpus = math.ceil(mp.cpu_count() * 0.2)
self.frequency = {}
if ngram:
# use ngram for searching
self.pmiNgram = NGram((i['key']
for i in self.db['pmi'].find({}, {
'key': 1,
'_id': False
})))
def getWordFreqItems(self):
# use cache
if os.path.exists('frequency.pkl'):
self.frequency = pickle.load(open('frequency.pkl', 'rb'))
frequency_of_total_keyword = pickle.load(
open('frequency_of_total_keyword.pkl', 'rb'))
return frequency_of_total_keyword
# return all frequency of word in type of dict.
self.frequency = {}
frequency_of_total_keyword = 0
# iterate through gridFS
for keyword in self.fs.list():
cursor = self.fs.find({"filename": keyword})[0]
value = {
'PartOfSpeech': cursor.contentType,
'value':
json.loads(self.fs.get(cursor._id).read().decode('utf-8'))
}
for correlation_keyword, PartOfSpeech, corTermCount in value[
'value']:
frequency_of_total_keyword += corTermCount
# accumulate keyword's frequency.
self.frequency[keyword] = self.frequency.setdefault(
keyword, 0) + corTermCount
# iterate through all normal collection
for i in self.db['kcm'].find({}):
keyword = i['key']
for correlation_keyword, PartOfSpeech, corTermCount in i['value']:
frequency_of_total_keyword += corTermCount
# accumulate keyword's frequency.
self.frequency[keyword] = self.frequency.setdefault(
keyword, 0) + corTermCount
pickle.dump(self.frequency, open('frequency.pkl', 'wb'))
pickle.dump(frequency_of_total_keyword,
open('frequency_of_total_keyword.pkl', 'wb'))
return frequency_of_total_keyword
def build(self):
self.Collect.remove({})
# read all frequency from KCM and build all PMI of KCM in MongoDB.
# with format {key:'中興大學', freq:100, value:[(keyword, PMI-value), (keyword, PMI-value)...]}
frequency_of_total_keyword = self.getWordFreqItems()
print('frequency of total keyword:' + str(frequency_of_total_keyword))
@graceful_auto_reconnect
def process_job(job_list):
# Each process need independent Mongo Client
# or it may raise Deadlock in Mongo.
client = pymongo.MongoClient(self.uri)
db = client['nlp_{}'.format(self.lang)]
process_collect = db['pmi']
kcm_collect = db['kcm']
fs = gridfs.GridFS(db)
result = []
for keyword, keyword_freq in job_list:
pmiResult = []
collection_cursor = kcm_collect.find({
'key': keyword
}, {
'value': 1,
'_id': False
}).limit(1)
if collection_cursor.count() == 0:
gridfs_cursor = fs.find({"filename": keyword}).limit(1)[0]
cursor_result = json.loads(
fs.get(gridfs_cursor._id).read().decode('utf-8'))[:500]
else:
cursor_result = collection_cursor[0]['value']
for kcmKeyword, PartOfSpeech, kcmCount in cursor_result:
# algorithm:
# PMI = log2(p(x, y)/p(x)*p(y))
# p(x, y) = frequency of (x, y) / frequency of total keyword.
# p(x) = frequency of x / frequency of total keyword.
value = math.log2(
kcmCount * frequency_of_total_keyword /
(keyword_freq * self.frequency[kcmKeyword]))
# this equation is contributed by 陳聖軒.
# contact him with facebook: https://www.facebook.com/henrymayday
value *= math.log2(self.frequency[kcmKeyword])
pmiResult.append((kcmKeyword, value))
pmiResult = sorted(pmiResult, key=lambda x: -x[1])
result.append({
'key': keyword,
'freq': keyword_freq,
'value': pmiResult
})
# Insert Collections into MongoDB
if len(result) > 5000:
process_collect.insert(result)
result = []
amount = math.ceil(len(self.frequency) / self.cpus)
job_list = list(self.frequency.items())
job_list = [
job_list[i:i + amount]
for i in range(0, len(self.frequency), amount)
]
processes = [
mp.Process(target=process_job, kwargs={'job_list': job_list[i]})
for i in range(self.cpus)
]
for process in processes:
process.start()
for process in processes:
process.join()
self.Collect.create_index([("key", pymongo.HASHED)])
def get(self, keyword, amount):
cursor = self.Collect.find({
'key': keyword
}, {
'value': 1,
'_id': False
}).limit(1)
if cursor.count() != 0:
return {
'key': keyword,
'value': cursor[0]['value'][:amount],
'similarity': 1
}
else:
pmiNgramKeyword = self.pmiNgram.find(keyword)
if pmiNgramKeyword:
result = self.Collect.find({
'key': pmiNgramKeyword
}, {
'value': 1,
'_id': False
}).limit(1)[0]['value'][:amount]
return {
'key': pmiNgramKeyword,
'value': result,
'similarity':
self.pmiNgram.compare(pmiNgramKeyword, keyword)
}
return {}
import csv
from ngram import NGram
records = NGram()
with open('./data/houses.csv', 'r', encoding='windows-1251') as f:
for line in csv.reader(f, delimiter=';'):
records.add(' '.join(list(line)).lower())
while True:
print('Enter search text:')
search_text = input().lower()
print('find', records.find(search_text), 0.8)
class KCM(object):
"""docstring for KCM"""
'''args
lang help='language, english or chinese (eng/cht)', required=True)
io_dir help='input output directory, required=True)
max_file_count help='maximum number of input files, 0 for no limit, type=int, default=0)
thread_count help='number of thread used, type=int, default=1)
'''
def __init__(self,
lang,
io_dir,
max_file_count=0,
thread_count=1,
uri=None):
self.BASE_DIR = BASE_DIR
self.lang = lang
self.io_dir = os.path.join(io_dir, self.lang)
self.max_file_count = max_file_count
self.thread_count = thread_count
self.client = MongoClient(uri)
self.db = self.client['nlp']
self.Collect = self.db['kcm']
# ngram search
self.kcmNgram = NGram((i['key'] for i in self.Collect.find({}, {
'key': 1,
'_id': False
})))
logging.basicConfig(format='%(levelname)s : %(asctime)s : %(message)s',
filename='KCM_{}.log'.format(self.lang),
level=logging.INFO)
logging.info('Begin gen_kcm.py')
logging.info('input {self.max_file_count} files, '
'output to {self.io_dir}, '
'maximum file count {self.max_file_count}, '
'use {self.thread_count} threads'.format(**locals()))
def get_source_file_list(self):
"""Generate list of term data source files
Args:
args: input arguments, use self.lang, self.max_file_count
Returns:
list of source files
"""
file_list = [] # wiki files
for (dir_path, dir_names, file_names) in os.walk(self.io_dir):
for file_name in file_names:
if self.max_file_count and len(
file_list) >= self.max_file_count:
break
if file_name == '.DS_Store' or '.model' in file_name: # for OS X
continue
file_list.append(os.path.join(dir_path, file_name))
logging.info('appended file {}'.format(
os.path.join(dir_path, file_name)))
if not file_list:
logging.info('no file selected, end of script')
exit()
return file_list
@timing
def remove_symbols_tags(self, if_name):
"""Remove symbols and tags. Read input file, output to output file.
Args:
if_name: input file name
args: input arguments, use self.io_dir, self.lang
Returns:
output file name
"""
return rm_tags(if_name)
@timing
def paragraphs_to_sentences(self, inputData):
"""Generate sentences from paragraphs. Read input file, output to output file
Args:
inputData: input data from former process.
args: input arguments, use self.io_dir, self.lang
Returns:
output file name
"""
return paragraphs_to_sentences_cht(inputData)
@timing
def sentences_to_terms(self, if_name, inputData):
"""generate terms from sentences
Args:
if_name: input file name
args: input arguments, use self.io_dir, self.lang
Returns:
output file name
"""
prefix = if_name.split('/')[-1].split('_')[0]
of_name = '{self.io_dir}/{prefix}_terms_{self.lang}'.format(**locals())
PosTokenizer(self.BASE_DIR, inputData, of_name, 'r')
return of_name
@removeInputFile
@timing
def terms_to_term_pairs(self, if_name):
"""Generate term pairs from terms.
Args:
if_name: input file name
args: input arguments, use self.io_dir, self.lang
Returns:
output file name
"""
of_name = '{self.io_dir}/{self.lang}.model'.format(**locals())
script_file = 'build/terms_to_term_pair_freq.py'
terms_to_term_pair_freq(if_name, of_name, min_freq=1, max_term_len=20)
@timing
def join_terms_files(self, if_names):
"""Join terms files into one
Args:
if_names: input terms files names
args: input arguments
"""
of_name = '{self.io_dir}/terms_{self.lang}'.format(**locals())
with open(of_name, 'w') as output_file:
for if_name in if_names:
with open(if_name, 'r') as input_file:
for line in input_file:
output_file.write(line)
return of_name
def gen_terms_file(self, if_name, o_list):
"""Generate terms file
Args:
if_name: input wiki source file name
args: input arguments
o_list: output list saving generated file name
"""
result = self.remove_symbols_tags(if_name)
result = self.paragraphs_to_sentences(result)
of_name = self.sentences_to_terms(if_name, result)
o_list.append(of_name)
def thread_job(self, input_file_queue, o_term_files):
"""Job to be done by thread (generate terms file)
Args:
input_file_queue: queue containing input files that needs processing
args: input arguments
o_term_files: list for outputting generated term file names
"""
while True:
if_name = input_file_queue.get()
if if_name is None:
break # end of thread
self.gen_terms_file(if_name, o_list=o_term_files)
input_file_queue.task_done()
@timing
def main(self):
"""main function"""
if_list = self.get_source_file_list()
term_files = []
input_file_queue = queue.Queue()
threads = []
for i in range(self.thread_count):
t = Thread(target=self.thread_job,
args=(input_file_queue, term_files))
t.start()
threads.append(t)
for if_name in if_list:
input_file_queue.put(if_name)
# block till all tasks are done (here means all input file processed)
input_file_queue.join()
# stop all threads
for i in range(self.thread_count):
input_file_queue.put(None)
# in thread_job, when input_file_queue.get == None, thread will end
for t in threads:
t.join() # wait till all threads really end
of_name = self.join_terms_files(term_files)
self.terms_to_term_pairs(of_name)
self.import2DB()
def setLang(self, lang):
self.lang = lang
self.io_dir = os.path.join(io_dir, self.lang)
def removeDB(self):
self.Collect.remove({})
def import2DB(self):
result = dict()
with open(os.path.join(self.io_dir, "{0}.model".format(self.lang)),
'r',
encoding='utf8') as f:
for i in f:
tmp = i.split()
result.setdefault(tmp[0], []).append([tmp[1], int(tmp[2])])
result.setdefault(tmp[1], []).append([tmp[0], int(tmp[2])])
documentArr = [{
'key': pair[0],
'value': sorted(pair[1], key=lambda x: -x[1])
} for pair in result.items()]
del result
self.Collect.insert(documentArr)
self.Collect.create_index([("key", pymongo.HASHED)])
def get(self, keyword, amount):
result = self.Collect.find({'key': keyword}, {'_id': False}).limit(1)
if result.count():
return {**(result[0]), 'similarity': 1}
else:
ngramKeyword = self.kcmNgram.find(keyword)
if ngramKeyword:
result = self.Collect.find({
'key': ngramKeyword
}, {
'_id': False
}).limit(1)
return {
'key': ngramKeyword,
'value': result[0]['value'][:amount],
'similarity': self.kcmNgram.compare(keyword, ngramKeyword)
}
return {'key': ngramKeyword, 'value': [], 'similarity': 0}
def get_similar(data, target):
G = NGram(target)
return G.find(data)
