forked from flabbergastedbd/nightfury
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help2vec.py
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help2vec.py
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import copy
import nltk
import random
import string
import text2num
import utilities
from pattern.en import parsetree, pprint, singularize, wordnet
def custom_similarity(word, synsets, pos=None):
word = singularize(word.lower())
similarities = []
if pos:
word_synsets = wordnet.synsets(word, pos=pos)
else:
word_synsets = wordnet.synsets(word)
for i in synsets:
for j in word_synsets:
try:
similarities.append(wordnet.similarity(i, j))
except Exception, e:
pass
# print(e)
return(max(similarities) if len(similarities) > 0 else 0)
def alphabet_similarity(word):
return(custom_similarity(word, [wordnet.synsets('alphabet')[0], wordnet.synsets('character')[-2], wordnet.synsets('letter')[1]], pos=wordnet.NOUN))
def capital_similarity(word):
return(custom_similarity(word, [wordnet.synsets('capital')[3]], pos=wordnet.NOUN))
def number_similarity(word):
return(custom_similarity(word, [wordnet.synsets('number')[0], wordnet.synsets('number')[1], wordnet.synsets('number')[4]], pos=wordnet.NOUN))
def lowercase_similarity(word):
return(custom_similarity(word, [wordnet.synsets('lowercase')[0], wordnet.synsets('lower')[0]]))
def uppercase_similarity(word):
return(custom_similarity(word, [wordnet.synsets('uppercase')[0], wordnet.synsets('upper')[0]]))
def special_similarity(word):
if word.startswith("special"):
return(1.0)
return(custom_similarity(word, [wordnet.synsets('special', pos=wordnet.ADJECTIVE)[1], wordnet.synsets('special', pos=wordnet.ADJECTIVE)[3]]))
def mandatory_similarity(word):
return(custom_similarity(word, [wordnet.synsets('mandatory', pos=wordnet.ADJECTIVE)[0]]))
def operate(old_chars, new_chars, op):
if op.get() == 0:
old_chars.append(random.choice(new_chars))
return(old_chars)
class Operator(object):
OPS = [0, 1]
def __init__(self, v=0):
self.op = v
def get(self):
t = self.op
self.op = 0
return(t)
def set(self, v):
self.op = v
INPUT_VECTOR = {"length": 0, "chars": []}
def input_help_to_vec(p):
t = parsetree(p)
requirements = []
mandatory = False
# pprint(t)
for sen in t:
for i, chunk in enumerate(sen.chunks):
if chunk.type == "ADJP":
vector = copy.deepcopy(INPUT_VECTOR)
for w in chunk.words:
if w.type.startswith("JJ") and mandatory_similarity(w.string) > 0.9:
mandatory = True
if chunk.type == "NP":
vector = copy.deepcopy(INPUT_VECTOR)
adjv_nn_bridge = []
op = Operator() # 0 = and & 1 = or
ignore = False # Useful when have DT like no etc..
for w in chunk.words:
if w.type == "CD":
try:
op.get()
vector["length"] = int(w.string)
except ValueError:
try:
vector["length"] = text2num.text2num(w.string)
except text2num.NumberException:
pass
elif w.type == "CC":
ignore = False
if w.string.lower() == "and":
op.set(0)
elif w.string.lower() == "or":
op.set(1)
elif w.type.startswith("NN"):
similarities = [alphabet_similarity(w.string), capital_similarity(w.string), number_similarity(w.string)]
m = max(similarities)
m_index = similarities.index(m)
if m > 0.9 and not ignore:
if m_index == 0:
if len(adjv_nn_bridge) == 0: adjv_nn_bridge.append(random.choice(list(string.lowercase)))
vector["chars"] = operate(vector["chars"], adjv_nn_bridge, op)
elif m_index == 1:
vector["chars"] = operate(vector["chars"], [random.choice(list(string.uppercase))], op)
elif m_index == 2:
vector["chars"] = operate(vector["chars"], [random.choice([str(i) for i in range(0, 10)])], op)
elif w.type.startswith("JJ"):
similarities = [lowercase_similarity(w.string), uppercase_similarity(w.string), special_similarity(w.string)]
m = max(similarities)
m_index = similarities.index(m)
if m > 0.9 and not ignore:
if m_index == 0:
adjv_nn_bridge = operate(adjv_nn_bridge, [random.choice(list(string.lowercase))], op)
elif m_index == 1:
adjv_nn_bridge = operate(adjv_nn_bridge, [random.choice(list(string.uppercase))], op)
elif m_index == 2:
adjv_nn_bridge = operate(adjv_nn_bridge, [random.choice(['!', '$'])], op)
if vector["length"] == 0: vector["length"] = 1
else:
op.get() # If there is a CC it gets cleaned because we couldn't identify the adjective
elif w.type.startswith("DT"):
if w.string.lower().startswith("no"):
ignore = True
requirements.append(vector)
if mandatory and len(requirements) == 0: requirements.append({"length": 1, "chars": ['x']})
# Handling conjunctions at sentence level
# Merging vectors based on 'and' and 'or' as of now
l = []
last_chunk = None
for w in t.words:
if w.chunk == None and w.type.startswith("CC"):
if w.string.lower() == "or":
l.append(1)
elif w.chunk and w.chunk.type == "NP":
if last_chunk == None or (last_chunk != w.chunk):
l.append(requirements.pop(0))
last_chunk = w.chunk
final = []
i = 0
while i < len(l):
if l[i] == 1:
i += 2
else:
if l[i]["length"] != 0 and len(l[i]["chars"]) > 0: final.append(l[i])
i += 1
return(final)
def input_vec_to_string(vectors):
payload = ''
if len(vectors) == 1 and vectors[0]["length"] > 0:
payload = ''.join(vectors[0]["chars"]) * (vectors[0]["length"]/len(vectors[0]["chars"]))
else:
length = 0
payload = ''
for i, v in enumerate(vectors):
if v["length"] > 1 and length == 0 and i == 0:
length = v["length"]
else:
payload += ''.join(v["chars"]) * (v["length"]/len(v["chars"]))
if length and len(payload) < length:
payload += ''.join([random.choice(list(string.lowercase)) for i in range(0, length - len(payload))])
return(''.join([utilities.get_alternate_char(c) for c in payload]))
def form_help_to_vec(p):
t = parsetree(p)
requirements = []
# pprint(t)
for sen in t:
for i, chunk in enumerate(sen.chunks):
if chunk.type == "NP":
requirements.append(vector)
return(final)
if __name__ == "__main__":
texts = [
"Must have at least 6 characters (with letters and numbers) and no special characters.",
"Your password must be at least 8 characters long and contain at least one upper case letter, one number and any of these special characters !@#$%^&*()",
"Use at least one lowercase letter, one numeral, and seven characters",
"6 or more characters",
"5 or 7 numbers",
"5 numbers or 7 capitals",
"5 numbers and 7 capitals",
"Cannot contain special characters",
"max 20 characters. no special characters"
]
for p in texts:
print(p)
print(input_help_to_vec(p))
print("\n")