def run():
args = get_args()
train_set = read_data(data_folder / args.train)
test_set = read_data(data_folder / args.test,
col_names=("tweet_id", "text", "q1_label"))
regular_solution = Naive_Bayes(train_set, test_set, ['yes', 'no'], False)
filtered_solution = Naive_Bayes(train_set, test_set, ['yes', 'no'], True)
output_trace(output_folder / "trace_NB-BOW-OV.txt", regular_solution)
output_trace(output_folder / "trace_NB-BOW-FV.txt", filtered_solution)
evaluate(output_folder / "eval_NB-BOW-OV.txt", regular_solution, "yes",
"no")
evaluate(output_folder / "eval_NB-BOW-FV.txt", filtered_solution, "yes",
"no")
#using sanitized input
train_set_sanitized = sanitize(train_set)
test_set_sanitized = sanitize(test_set)
sanitized_solution = Naive_Bayes(train_set_sanitized, test_set_sanitized,
['yes', 'no'], False)
output_trace(output_folder / "trace_NB-BOW-OV_sanitized.txt",
sanitized_solution)
evaluate(output_folder / "eval_NB-BOW-OV_sanitized.txt",
sanitized_solution, "yes", "no")
def test_string_sanitizer_removes_spaces(self):
# Arrange
expected = "00201485672"
# Act
result = sanitize("0 0201485672")
# Assert
self.assertEqual(expected, result)
def __init__(
self,
text,
sanitize=False,
permitted_tags=[
'a',
'b',
'blockquote',
'br/',
'i',
'li',
'ol',
'ul',
'p',
'cite',
'code',
'pre',
'img/',
],
allowed_attributes={
'a': ['href', 'title'],
'img': ['src', 'alt'],
'blockquote': ['type']
},
):
if sanitize:
text = sanitizer.sanitize(text, permitted_tags, allowed_attributes)
if isinstance(text, unicode):
text = text.encode('utf8', 'xmlcharrefreplace')
elif not isinstance(text, str):
text = str(text)
self.text = text
def test_string_sanitizer_removes_dashes(self):
# Arrange
expected = "9780596809485"
# Act
result = sanitize("978-0596809485")
# Assert
self.assertEqual(expected, result)
def __init__(
self,
text,
sanitize=False,
permitted_tags=[
'a',
'b',
'blockquote',
'br/',
'i',
'li',
'ol',
'ul',
'p',
'cite',
'code',
'pre',
'img/',
],
allowed_attributes={'a': ['href', 'title'], 'img': ['src', 'alt'
], 'blockquote': ['type']},
):
if sanitize:
text = sanitizer.sanitize(text, permitted_tags,
allowed_attributes)
if isinstance(text, unicode):
text = text.encode('utf8', 'xmlcharrefreplace')
elif not isinstance(text, str):
text = str(text)
self.text = text
def load_dataframe(filename):
""" input: filename of pickle dataframe ready_for_R csv"""
if os.path.isfile(const.DATAPATH + filename + ".pkl"):
return pd.read_pickle(const.DATAPATH + filename + ".pkl")
else:
logger.error("NO SANITIZED PICKLE: " + const.DATAPATH + filename)
logger.error("SANITIZE DATA FIRST")
df = sanitizer.sanitize()
return df
def get_coach_data(path):
try:
with open(path) as reader:
data = reader.readline()
data_split = data.strip().split(',')
athlete_data = Athlete(data_split.pop(0), data_split.pop(0), sorted(set([sanitize(t) for t in data_split]))[0:3])
return athlete_data
except IOError as ioerror:
print('File Error: ' + str(ioerror))
return(None)
def main():
# Setup logging
logging.basicConfig(format='[%(asctime)s] [%(levelname)s] %(message)s',
level=logging.INFO)
divider = '-' * 30
# Login to database
logging.info(divider + " DATABASE LOGIN " + divider)
db = MongoClient(os.environ.get("TRITON_ANALYTICS_MONGODB")).get_database(
"tritonanalytics")
# Download recent analytics
logging.info(divider + " DOWNLOAD PROCESS " + divider)
download(os.environ.get("TRITON_ANALYTICS_FBID"),
os.environ.get("TRITON_ANALYTICS_FBTOKEN"))
# Export all stored analytics
logging.info(divider + " EXPORT PROCESS " + divider)
export_to(db)
# Sanitize database
logging.info(divider + " SANITIZATION " + divider)
sanitize(db)
def __init__(
self,
text,
sanitize = False,
permitted_tags = [
'a',
'b',
'blockquote',
'br/',
'i',
'li',
'ol',
'ul',
'p',
'cite',
'code',
'pre',
'img/',
],
allowed_attributes = {
'a': ['href', 'title'],
'img': ['src', 'alt'],
'blockquote': ['type']
},
):
"""
:param text: the XML text
:param sanitize: sanitize text using the permitted tags and allowed
attributes (default False)
:param permitted_tags: list of permitted tags (default: simple list of
tags)
:param allowed_attributes: dictionary of allowed attributed (default
for A, IMG and BlockQuote).
The key is the tag; the value is a list of allowed attributes.
"""
if sanitize:
text = sanitizer.sanitize(text, permitted_tags,
allowed_attributes)
if isinstance(text, unicode):
text = text.encode('utf8', 'xmlcharrefreplace')
elif not isinstance(text, str):
text = str(text)
self.text = text
def __init__(
self,
text,
sanitize=False,
permitted_tags=[
'a',
'b',
'blockquote',
'br/',
'i',
'li',
'ol',
'ul',
'p',
'cite',
'code',
'pre',
'img/',
],
allowed_attributes={
'a': ['href', 'title'],
'img': ['src', 'alt'],
'blockquote': ['type']
},
):
"""
:param text: the XML text
:param sanitize: sanitize text using the permitted tags and allowed
attributes (default False)
:param permitted_tags: list of permitted tags (default: simple list of
tags)
:param allowed_attributes: dictionary of allowed attributed (default
for A, IMG and BlockQuote).
The key is the tag; the value is a list of allowed attributes.
"""
if sanitize:
text = sanitizer.sanitize(text, permitted_tags, allowed_attributes)
if isinstance(text, unicode):
text = text.encode('utf8', 'xmlcharrefreplace')
elif not isinstance(text, str):
text = str(text)
self.text = text
def top3(self): return sorted(set([sanitize(t) for t in self.times]))[0:3]
with open('./data/james.txt') as jaf:
data = jaf.readline()
james = data.strip().split(',')
with open('./data/julie.txt') as juf:
data = juf.readline()
julie = data.strip().split(',')
with open('./data/mikey.txt') as mif:
data = mif.readline()
mikey = data.strip().split(',')
with open('./data/sarah.txt') as saf:
data = saf.readline()
sarah = data.strip().split(',')
# 지능형 리스트
clean_james = [sanitize(each_t) for each_t in james]
clean_julie = [sanitize(each_t) for each_t in julie]
clean_mikey = [sanitize(each_t) for each_t in mikey]
clean_sarah = [sanitize(each_t) for each_t in sarah]
# print (sorted(clean_james))
# print (sorted(clean_julie))
# print (sorted(clean_mikey))
# print (sorted(clean_sarah))
print (sorted(clean_james,reverse=True))
print (sorted(clean_julie,reverse=True))
print (sorted(clean_mikey,reverse=True))
print (sorted(clean_sarah,reverse=True))
help(sorted)
with open('./data/james.txt') as jaf:
data = jaf.readline()
james = data.strip().split(',')
with open('./data/julie.txt') as juf:
data = juf.readline()
julie = data.strip().split(',')
with open('./data/mikey.txt') as mif:
data = mif.readline()
mikey = data.strip().split(',')
with open('./data/sarah.txt') as saf:
data = saf.readline()
sarah = data.strip().split(',')
for each_t in james:
clean_james.append(sanitizer.sanitize(each_t))
for each_t in julie:
clean_julie.append(sanitizer.sanitize(each_t))
for each_t in mikey:
clean_mikey.append(sanitizer.sanitize(each_t))
for each_t in sarah:
clean_sarah.append(sanitizer.sanitize(each_t))
# print (sorted(clean_james))
# print (sorted(clean_julie))
# print (sorted(clean_mikey))
# print (sorted(clean_sarah))
print (sorted(clean_james,reverse=True))
print (sorted(clean_julie,reverse=True))
print (sorted(clean_mikey,reverse=True))
print (sorted(clean_sarah,reverse=True))
import os, sanitizer
def getList():
sanitizer = html_sanitizer.Sanitizer()
files = os. listdir('data')
listStr = ''
for item in files:
item = sanitizer.sanitize(item)
listStr = listStr + '<li><a href="index.py?id{name}">{name}</a></ li>'.format(name=item)
return listStr
def top3(self): return (sorted(set([sanitize(t) for t in self]))[0:3])
with open('./data/james.txt') as jaf:
data = jaf.readline()
james = data.strip().split(',')
with open('./data/julie.txt') as juf:
data = juf.readline()
julie = data.strip().split(',')
with open('./data/mikey.txt') as mif:
data = mif.readline()
mikey = data.strip().split(',')
with open('./data/sarah.txt') as saf:
data = saf.readline()
sarah = data.strip().split(',')
james = sorted([sanitize(t) for t in james])
julie = sorted([sanitize(t) for t in julie])
mikey = sorted([sanitize(t) for t in mikey])
sarah = sorted([sanitize(t) for t in sarah])
# set 집합으로 변환하면 자동으로 중복 데이터를 제거해준다
# 중복데이터가 발견되면 무시하고 다음 데이터를 입력한다
# 입력은 무작위로 되기 때문에 sort()내장함수를 사용하여 다시 정렬시킨다
set_james = set(james)
set_julie = set(julie)
set_mikey = set(mikey)
set_sarah = set(sarah)
print(sorted(set_james)[:3])
print(sorted(set_julie)[:3])
print(sorted(set_mikey)[:3])
args = parser.parse_args()
model = args.model
src_folder = args.src_folder
dst_folder = args.dst_folder
num_words = args.num_words
num_tags = args.num_tags
print(model)
print(num_tags)
# Reading Train, Dev, Test Files
train_df = read_train_data(src_folder, 'train.xml')
dev_df = read_train_data(src_folder, 'dev.xml')
test_df = read_train_data(src_folder, 'test.xml')
# Sanitize Training Data
train_df = sanitize(train_df)
# train_df.dropna(subset=['AnswerBody'], inplace=True)
train_df = train_df[train_df['AnswerBody'].apply(len) != 0]
dev_df = sanitize(dev_df)
# dev_df.dropna(subset=['AnswerBody'], inplace=True)
dev_df = dev_df[dev_df['AnswerBody'].apply(len) != 0]
test_df = sanitize(test_df)
# test_df.dropna(subset=['AnswerBody'], inplace=True)
test_df = test_df[test_df['AnswerBody'].apply(len) != 0]
df = pd.concat([train_df, dev_df, test_df])
word_freq = get_vocabs(df)
sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
def clean_data(self): return (sorted(set([sanitize(t) for t in self])))
# max_clean_sheets = player["clean_sheets"]
# max_clean_sheets_player = player
# TODO: sanitize fantasy data
for player in fantasy_data["elements"]:
player["second_name"] = player["second_name"].replace("é", "e")
for (dirpath, dirnames, filenames) in walk(sources_path):
for event_path_id in dirnames:
event_file = "{}/{}/0/index.html".format(dirpath, event_path_id)
sources.append(event_file)
for source in sources:
raw = event_loader.load(source)
if len(raw) > 0:
events = sanitizer.sanitize(raw)
datetime = events["datetime"]
line_ups = events["line_ups"]
for player in line_ups["team_1"]["line_up"]:
# TODO: patch properly
if "Fn" not in player:
player["Fn"] = player["Ln"]
fantasy_player = [item for item in fantasy_data["elements"] if player["Fn"] in item["first_name"] and player["Ln"] in item["second_name"]]
if len(fantasy_player) == 1:
now_cost = fantasy_player[0]["now_cost"] # TODO: evaluate initial season cost
player["cost"] = now_cost
if len(fantasy_player) > 1:
with open('./data/james.txt') as jaf:
data = jaf.readline()
james = data.strip().split(',')
with open('./data/julie.txt') as juf:
data = juf.readline()
julie = data.strip().split(',')
with open('./data/mikey.txt') as mif:
data = mif.readline()
mikey = data.strip().split(',')
with open('./data/sarah.txt') as saf:
data = saf.readline()
sarah = data.strip().split(',')
# 각기 다른 시간/분 구분자를 ":" 으로 통일하기 위한 sanitize()메소드를 수행한 후 정렬
james = sorted([sanitize(t) for t in james])
julie = sorted([sanitize(t) for t in julie])
mikey = sorted([sanitize(t) for t in mikey])
sarah = sorted([sanitize(t) for t in sarah])
# set 집합으로 변환하면 자동으로 중복 데이터를 제거해준다
# 중복데이터가 발견되면 무시하고 다음 데이터를 입력한다
# 입력은 무작위로 되기 때문에 sort()내장함수를 사용하여 다시 정렬시킨다
set_james = set(james)
set_julie = set(julie)
set_mikey = set(mikey)
set_sarah = set(sarah)
# 상위 3개의 기록만 보여주기
print(sorted(set_james)[:3])
print(sorted(set_julie)[:3])