def __init__(self, d):
yyyymmdd = map(int, self.__date_re.match(d["date"]).groups())
self.date = datetime.date(*yyyymmdd)
self.url = util.unicode_to_ascii(d["url"])
self.title = util.unicode_to_ascii(d.get("title", ""))
self.body = util.unicode_to_ascii(d.get("body", ""))
if self.title == "" and self.body != "":
self.title = self.body.splitlines()[0]
elif self.body == "" and self.title != "":
self.body = self.title
elif self.body != "" and self.title != "":
pass
else:
util.Log("%s epic-fail" % (self.url))
return
def __init__(self, title, lead, text):
title, lead, text = [util.unicode_to_ascii(x) for x in [title, lead, text]]
# lead, text = [x.lower() for x in [lead, text]]
self.title = title
self.lead = lead
self.text = text
return
def _prepare_cache_key(self, key):
"""
Ensures a key is valid for memcached by converting to ascii if
necessary and replacing spaces. Returns the modified key,
or raises a ValueError if the key is empty or too long.
"""
try:
ascii_key = key if isinstance(key, str) else key.encode("ascii")
except UnicodeEncodeError:
ascii_key = unicode_to_ascii(key)
ascii_key = ascii_key.replace(" ", "_")
if not ascii_key:
raise ValueError("Cache key is empty")
if not ascii_key or len(ascii_key) > SERVER_MAX_KEY_LENGTH:
raise ValueError("Cache key is too long: " + ascii_key)
return ascii_key
def readData():
trcount = dict()
tecount = dict()
trdata = list()
trlabel = list()
tedata = list()
telabel = list()
for f in tr_list_files:
if not f.endswith(".txt"):
continue
with open(TR_DOC_DIR + '/' + f) as fin:
text = json.load(fin)
target = text['section'].encode("utf-8").replace("\xe2\x80\x99", "'")
# if target in art:
# target = 'art'
if target in sport:
target = 'sport'
elif target in business:
target = 'business'
elif target in tech:
target = 'tech'
elif target in entertain:
target = 'entertain'
elif target in opinion:
target = 'opinion'
elif target in world:
target = 'world'
elif target in us:
target = 'us'
else:
continue
if target not in trcount:
trcount[target] = 0
trcount[target] = trcount[target] + 1
label = category_to_label(target)
content = tokenize(unicode_to_ascii(text['text'].encode('utf-8')))
if target not in CORPUS:
CORPUS[target] = []
CORPUS[target].append(" ".join(content))
# for word in content:
# WORD_DICT.add(word)
trdata.append(content)
trlabel.append(label)
for f in te_list_files:
if not f.endswith(".txt"):
continue
with open(TE_DOC_DIR + '/' + f) as fin:
text = json.load(fin)
target = text['section'].encode("utf-8").replace("\xe2\x80\x99", "'")
# if target in art:
# target = 'art'
if target in sport:
target = 'sport'
elif target in business:
target = 'business'
elif target in tech:
target = 'tech'
elif target in entertain:
target = 'entertain'
elif target in opinion:
target = 'opinion'
elif target in world:
target = 'world'
elif target in us:
target = 'us'
else:
continue
if target not in tecount:
tecount[target] = 0
tecount[target] = tecount[target] + 1
label = category_to_label(target)
content = tokenize(unicode_to_ascii(text['text'].encode('utf-8')))
# for word in content:
# WORD_DICT.add(word)
tedata.append(content)
telabel.append(label)
print("------Training data------")
print(trcount)
print("------Testing data------")
print(tecount)
# print(CORPUS['sport'])
return trdata, trlabel, tedata, telabel
def test_Given_unicodeName_When_unicodeToAscii_Then_returnAsciiName(
input, output):
assert unicode_to_ascii(input) == output
def __str__(self):
filename = unicode_to_ascii(self.filename(with_path=False))
if self._db_id > 0:
return filename + ' ' + self.get_cache_key()
else:
return filename
def classify(f_dir):
model = pkl.load(open(model_file, 'rb'))
features = pkl.load(open(features_file, 'rb'))
outputs = list()
files = os.listdir(f_dir)
for f in files:
output = dict()
traindata = list()
if not f.endswith(".txt"):
continue
with open(f_dir + "/" + f) as fin:
text = json.load(fin)
# if source == "NYT":
output["title"] = unicode_to_ascii(text["title"].encode("utf-8"))
output["date"] = get_time(
unicode_to_ascii(text["time"].encode("utf-8")))
output["url"] = unicode_to_ascii(text["url"].encode("utf-8"))
output["source"] = unicode_to_ascii(text["source"].encode("utf-8"))
# elif source == "FOX":
# output["title"] = unicode_to_ascii(text["title"].encode("utf-8"))
# output["date"] = get_time(unicode_to_ascii(text["time"].encode("utf-8")))
# output["url"] = unicode_to_ascii(text["url"].encode("utf-8"))
# output["source"] = source
# else:
# output["title"] = unicode_to_ascii(text["title"].encode("utf-8"))
# output["date"] = get_time(unicode_to_ascii(text["tile"].encode("utf-8")))
# output["url"] = unicode_to_ascii(text["url"].encode("utf-8"))
# output["source"] = source
content = tokenize(unicode_to_ascii(text["text"].encode("utf-8")))
if content is None or len(content) == 0:
print("File %s has no text" % f)
continue
raw_text = unicode_to_ascii(text["text"].encode("utf-8"))
indexes = [i.start() for i in re.finditer(r"\.", raw_text)]
if len(indexes) == 0:
continue
if len(indexes) <= 3:
stop = len(indexes) - 1
else:
stop = 3
output["snippet"] = raw_text[0:indexes[stop] + 1]
traindata.append(content)
X_arr = encoding(traindata, features)
probs = model.predict_proba(X_arr).tolist()
Y = np.argmax(probs[0])
output["category"] = label_to_category(Y)
output["score"] = probs[0][Y]
outputs.append(output)
return outputs
