def drivel(noun):
""" Generates drivel by shifting nouns in the description of the shifted noun,
and prepending random alliterative adjectives.
"""
drivel = []
description = shift(noun)[1]
description = description.split(";")[0]
for sentence in parsetree(description):
for i, w in enumerate(sentence.words):
w, tag = w.string, w.tag
if tag in ("VBD", "VBZ"):
w = conjugate(w, "infinitive")
w = conjugate(w, "past")
if tag == "NN": # noun
try:
w = shift(w)[0]
a = list(alliterate(w))
if a:
if i > 0 and sentence.words[i].tag == "JJ": # adjective
drivel.pop()
drivel.append(choice(a))
except:
pass
drivel.append(w)
return " ".join(drivel)
def negate_verb_leaves(leaf_nodes):
"""
Takes a list of leaf nodes and tries to negate it by searching for "not"
TODO: This code could be cleaner. It's repeated all over the place.
"""
idx = None
for i in xrange(len(leaf_nodes)):
leaf = leaf_nodes[i]
# We have to call is_leaf because sometimes we have
# non-verb nodes treated as leaves
if is_leaf(leaf) and leaf.label() == 'RB' and leaf[0].lower() == 'not':
idx = i
break
if idx:
negative = leaf_nodes
positive = leaf_nodes[:idx] + leaf_nodes[idx+1:]
else:
positive = leaf_nodes
if len(leaf_nodes) == 1 and conjugate(leaf_nodes[0][0], 'VB') != 'be':
pos = leaf_nodes[0].label()
negative = [
Tree(pos, [conjugate('do', pos)]),
Tree('RB', ['not']),
Tree('VB', [conjugate(leaf_nodes[0], 'VB')])
]
else:
negative = [leaf_nodes[0], Tree('RB', ['not'])] + leaf_nodes[1:]
return (positive, negative)
def cj(word, num):
''' Conjugate verb based on count '''
if num != 1:
word = conjugate(word, 'pl') # Conjugate as plural
else:
word = conjugate(word, '3sg') # Conjugate in 3rd-person singular
return word
def testBasic():
from pattern.en import referenced
print referenced('hour')
from pattern.en import conjugate, lemma, lexeme
print lexeme('purr')
print lemma('purring')
print conjugate('purred', '3sg') # he / she / it
def find_verb_form(original_form, original_lemma, new_lemma):
"""
Figure out original tense of the verb, then apply that tense to new_lemma
There might be more than one, let's keep it simple and just apply
the first one
"""
possible_conjugations = tenses(original_form)
if len(possible_conjugations) > 1:
return conjugate(new_lemma, possible_conjugations[1])
else:
return conjugate(new_lemma, possible_conjugations[0])
def explore_syn_tree(self, word_tuple, pos=None):
if self.check_in_words(word_tuple[0]):
return word_tuple[0]
if word_tuple[0] == "difficult":
return "hard"
if word_tuple[0] == "saturn":
return "up goer"
synsets = None
posSet = False
if pos is None:
synsets = wn.synsets(word_tuple[0])
else:
synsets = wn.synsets(word_tuple[0], pos=pos)
posSet = True
for synset in synsets:
matching_hypernym = self.explore_hypernyms(synset)
if matching_hypernym != None:
if pos == wordnet.NOUN:
if self.is_plural(word_tuple[0]):
# The word is a plural, yo.
return self.inflect_engine.plural(matching_hypernym)
if pos == wordnet.VERB:
pattern_tag = self.get_pattern_tense(word_tuple[1])
person = 1
if word_tuple[1] == "VBZ":
person = 3
matching_hypernym = conjugate(matching_hypernym, tense=pattern_tag, person=person, parse=True)
return matching_hypernym
return matching_hypernym
matching_hyponym = self.explore_hyponyms(synset)
if matching_hyponym != None:
if pos == wordnet.NOUN:
if self.is_plural(word_tuple[0]):
# The word is a plural, yo.
return self.inflect_engine.plural(matching_hyponym)
if pos == wordnet.VERB:
pattern_tag = self.get_pattern_tense(word_tuple[1])
person = 1
if word_tuple[1] == "VBZ":
person = 3
matching_hypornym = conjugate(matching_hyponym, tense=pattern_tag, person=person, parse=True)
return matching_hyponym
return matching_hyponym
# Some handling logic
wn_pos = word_tuple[1]
possible_solutions = set()
for tagged_tuple in self.tagged_words:
if wn_pos == tagged_tuple[0][1]:
possible_solutions.add(tagged_tuple[0][0])
return random.sample(possible_solutions, 1)[0]
def stem_word(word):
try:
if word.endswith("s"):
if singularize(word) in nltk_words:
return singularize(word)
else:
return word
if word.endswith("d") or word.endswith('ing'):
if conjugate(word) in nltk_words:
return conjugate(word)
else:
return word
except:
return word
return word
def dictionary_tag(sentence):
client = MongoClient('127.0.0.1')
db = client.dictionaries
collection = db.sfu
emots = db.emoticons
for word in sentence:
qu = emots.find_one({'word':word[0].encode('utf-8').strip(),'pos':'em'})
if qu:
word[1] = 'em'
word.append(qu['polarity'])
continue
qu = word[0].encode('utf-8').strip().lower()
qu = re.sub(":"," ",qu)
if 'n\'t' in qu or 'not' in qu or qu == 'not' or qu == 'n\'t' or qu == 'no':
word[1] = 'neg'
word.append("")
continue
single = None
single = collection.find_one({'word':qu,'pos':word[1][:2].lower()})
if single: #word as it is
word[1] = single['pos']
word.append(single['polarity'])
else: #convert to 1 person present tense
qu = conjugate(qu,'1sg')
single = collection.find_one({'word':qu,'pos':word[1][:2].lower()})
if single:
word[1] = single['pos']
word.append(single['polarity'])
else:
word.append('')
return sentence
def create_description(self):
pat = 'VB|VBD|VBZ|VBG * NN IN * NN'
#pat = 'PRP * VB|VBD|VBZ|VBG * NN'
phrases = search.search_out(self.source_text, pat)
conjugated_phrases = []
for phrase in phrases:
words = []
for word, pos in tag(phrase):
if pos in ["VBZ", "VBD", "VB", "VBG"]:
words.append(conjugate(word, "3sg"))
#elif pos == "NN" and random.random() < .1:
#words.append(self.define_word(word))
else:
words.append(word)
conjugated_phrases.append(' '.join(words))
artifacts = list(self.artifacts)
sentence_prefixes = ["The present invention", "The device", "The invention"]
paragraph_prefixes = ["The present invention", "According to a beneficial embodiment, the invention", "According to another embodiment, the device", "According to a preferred embodiment, the invention", "In accordance with an alternative specific embodiment, the present invention"]
i = 0
self.description = ''
for phrase in conjugated_phrases:
line = ""
if i == 0:
line = paragraph_prefixes[0] + " " + phrase
else:
if random.random() < .1:
line = "\n\n" + random.choice(paragraph_prefixes) + " " + phrase
else:
line = random.choice(sentence_prefixes) + " " + phrase
self.description += line + ". "
i += 1
def test_conjugate(self):
# Assert different tenses with different conjugations.
for (v1, v2, tense) in (
("be", "be", en.INFINITIVE),
("be", "am", en.PRESENT_1ST_PERSON_SINGULAR),
("be", "are", en.PRESENT_2ND_PERSON_SINGULAR),
("be", "is", en.PRESENT_3RD_PERSON_SINGULAR),
("be", "are", en.PRESENT_PLURAL),
("be", "being", en.PRESENT_PARTICIPLE),
("be", "was", en.PAST_1ST_PERSON_SINGULAR),
("be", "were", en.PAST_2ND_PERSON_SINGULAR),
("be", "was", en.PAST_3RD_PERSON_SINGULAR),
("be", "were", en.PAST_PLURAL),
("be", "were", en.PAST),
("be", "been", en.PAST_PARTICIPLE),
("had", "have", "inf"),
("had", "have", "1sg"),
("had", "have", "2sg"),
("had", "has", "3sg"),
("had", "have", "pl"),
("had", "having", "part"),
("has", "had", "1sgp"),
("has", "had", "2sgp"),
("has", "had", "3sgp"),
("has", "had", "ppl"),
("has", "had", "p"),
("has", "had", "ppart"),
("imaginerify", "imaginerified", "3sgp")):
self.assertEqual(en.conjugate(v1, tense), v2)
print "pattern.en.conjugate()"
def _transform_word(self, word, pos, less, more):
"""transforms a word to be less less and more more
:param word: word to transform
:type word: str
:param pos: part of speech of the word
:type pos: str
:param less: list of 'less' words
:type less: list
:param more: list of 'more' words
:type more: list
:returns: transformed word
:rtype: str
"""
new_word = self._get_similar_word(word, less, more)
new_pos = en.tag(new_word)[0][1]
if (pos[:2] != new_pos[:2]) or word == new_word:
return word
# handle noun
if pos.startswith('NN'):
# pluralization
if pos.endswith('S') and not new_pos.endswith('S'):
new_word = en.pluralize(new_word)
elif not pos.endswith('S') and new_pos.endswith('S'):
new_word = en.singularize(new_word)
# capitalization
if word[0].isupper():
new_word = new_word[0].upper() + new_word[1:]
else:
new_word = new_word.lower()
# handle verb
elif pos.startswith('VB'):
tense, person, number = en.tenses(word)[0][:3]
# conjugation
conjugated = en.conjugate(new_word,
tense=tense,
person=person,
number=number,
parse=False)
if conjugated is not None:
new_word = conjugated
# remove underscores for joint words
new_word = new_word.replace('_', ' ')
return new_word
def verbConjugate(lemma, rel, aan):
relAvoid = ["/r/CapableOf", "/r/PartOf", "/r/MemberOf"
"/r/IsA", "/r/HasA", "/r/TranslationOf",
"/r/HasProperty"]
if not rel in relAvoid:
s = parsetree(lemma, relations=True)
try:
vb = s[0].verbs[0].words[0].string
result = lemma.replace(vb, conjugate(vb, "part"))
except:
result = lemma
else:
if vb == "to":
result = lemma
# if not aan:
# try:
# firstWord = s[0].chunks[0].words[0].string
# reconjugated = conjugate(firstWord, "part")
# result = lemma.replace(firstWord, reconjugated)
# except:
# result = lemma
else:
result = lemma
return result
def make_thesaurus(file_path):
"""
Returns dict of counters 'thesaurus', where
thesaurus[word] = { synonym1: 4, syn2: 8, syn3: 1, ... }
"""
thesaurus = defaultdict(lambda: Counter())
with open(file_path, "r") as f:
for line in f:
# Ignore repeated book title headers
if _is_title(line):
continue
parsed = parse(line)
for tagged_word in parsed.split()[0]:
word = tagged_word[0].strip().lower()
pos = tagged_word[1][0] # get pos for word
# Reject non-ASCII characters
try:
word = word.decode("ascii")
except (UnicodeDecodeError, UnicodeEncodeError):
continue
# Reject whitespace character
if re.match("^[\s]*$", word):
continue
# Increment word count of word w
thesaurus[word].update([word])
# Retrieve syn = synonym[w], add to thesaurus[syn]
for syn in wn.get_synonyms(word):
syn = syn.name().split(".")[0]
# if noun, add plural form if word is plural, else add singular
if pos == "N":
if word == pluralize(word):
thesaurus[pluralize(syn)].update([word])
else:
thesaurus[syn].update([word])
# if verb, conjugate synonyms to the right form before adding them to thes
elif pos == "V":
word_tenses = tenses(word)
if word_tenses:
thesaurus[conjugate(syn, tense=word_tenses[0][0])].update([word])
else:
thesaurus[syn].update([word])
else:
thesaurus[syn].update([word])
# Update thesaurus with mappings, if map_file exists
file_path = file_path.replace(config.CORPUS_FOLDER, config.MAPPING_FOLDER)
map_file = file_path.replace(config.CORP_TAG, config.MAP_TAG)
thesaurus = _add_mappings(map_file, thesaurus)
return thesaurus
def get_conjugations(lem):
vforms = []
if lemma(lem) == 'be':
vforms = [i for i in EXCEPTIONS]
else:
for ta in TENSE_ASPECTS:
c = conjugate(lemma(lem), ta)
vforms.append( c +'|||'+ ta)
return vforms
def generalize(self, words):
# Convert to generalized sentence (not company specific)
we = 'We__PRP'
words[0] = we
# Pluralize the verb using pattern library
pl = conjugate(self.remove_pos_tag(words[1]), 'pl') + '__VBP'
words[1] = pl
return words
def deconjugate_leaves(T):
if T.height() == 1:
return
if T.height() == 2:
if T.label()[0] == "V":
T.set_label("VB")
T[0] = conjugate(T[0], "VB")
else:
for i in T:
deconjugate_leaves(i)
def uncompleteSentence(self, sentence):
full_sentence = nltk.word_tokenize(' '.join(sentence))
tags = nltk.pos_tag(full_sentence)
new_sentence = []
for i in range(len(full_sentence) - 1):
isVerb = True
synsets = wordnet.synsets(tags[i][0])
for syn in synsets:
if 'verb.' not in syn.lexname:
isVerb = False
break
if ('NN' == tags[i][1] or tags[i][1] == 'RB') and isVerb:
new_sentence.append(conjugate(tags[i][0], 'part'))
elif tags[i][1] == 'JJ' and isVerb:
new_sentence.append(conjugate(tags[i][0], 'ppart'))
else:
new_sentence.append(tags[i][0])
new_sentence.append(full_sentence[-1])
return new_sentence
def process(wrd):
tmp = ''
ignore_pos = ['IN', 'RP', 'TO']
exception_lemma = ['flatter', 'flattered']
if tag(wrd)[0][1] in ignore_pos:
tmp = wrd
elif any(wrd in ex_l for ex_l in exception_lemma):
tmp = wrd
else:
tmp = conjugate(wrd, tense=PAST)
return tmp
def make_verbs(inp_fn, out_dir):
verbs = set()
with open(inp_fn) as f:
for i, line in enumerate(f):
verbs.add(conjugate(line.strip(), 'p'))
print '\tFound', len(verbs), 'verbs'
out_fn = out_dir + '/verbs.txt'
with open(out_fn, 'w') as f:
for n in verbs:
f.write(n + '\n')
return
def test_parse_lemma(self):
# Assert the accuracy of the verb lemmatization algorithm.
# Note: the accuracy is higher (95%) when measured on CELEX word forms
# (presumably because en.inflect.VERBS has high percentage irregular verbs).
i, n = 0, 0
for v in en.inflect.VERBS.infinitives:
for tense in en.inflect.VERBS.TENSES:
if en.inflect._parse_lemma(en.conjugate(v, tense)) == v:
i += 1
n += 1
self.assertTrue(float(i) / n > 0.88)
print "pattern.en.inflect._parse_lemma()"
def random_imperative(noun=None, get_related=True, verb=None, adj=None):
if noun:
n = get_related_or_not(noun, True, 'NN') if get_related else noun
else:
n = random.choice(NOUNS)
if verb:
v = get_related_or_not(verb, True, 'VB')
if v is None:
v = verb
else:
v = random.choice(VERBS)
if not adj:
adj = random.choice(ADJS) if coin_flip(0.5) else ''
c = ''
if coin_flip(0.7):
n = pluralize(n)
c = random.choice(C2)
else:
i = random.randint(1, 5)
n = quantify(adj + ' ' + n, amount=i)
adj = ''
if coin_flip(0.25):
a = ''
v = conjugate(v)
elif coin_flip(0.33):
v = conjugate(v, 'part') # present participle
a = random.choice(A1)
c = random.choice(C)
elif coin_flip(0.5):
v = conjugate(v)
a = random.choice(A2)
else:
v = conjugate(v)
a = random.choice(A3)
phrase = '{0} {1} {2} {3} {4}'.format(a, v, c, adj, n)
phrase = phrase[1:] if phrase.startswith(' ') else phrase
return re.sub(' +', ' ', phrase)
def create_defined_template(arr):
for x in arr:
x = x.splitlines()[0]
if x[2] == "P":
for t in tenses:
a = conjugate(x[3:], t)
triggers[x[0]][x[:3]].append(str(a))
elif x[3:].find(" ") != -1 and x[3:] not in triggers_bi:
triggers_bi.append(x[3:])
elif x[:3] in ["BPA", "BPE"] and x[3:] not in triggers_p:
triggers_p.append(x[3:])
else:
triggers["OTHERS"].append(x[3:])
def generate_phrase_1():
selections = [VERBS, ADJECTIVES, NOUNS, ADVERBS, TRANSITIVE_VERBS, VERBS, ADJECTIVES, NOUNS]
entropy = sum([log(len(item), 2) for item in selections])
conjugations = ['part', None, None,
None, [random_item_from_list([PAST, PRESENT])],
'part', None, None]
entropy += 1
print('%.2f bits of entropy' % entropy)
sub_list = [random_item_from_list(item) for item in selections]
for idx, word in enumerate(sub_list):
if conjugations[idx]:
sub_list[idx] = conjugate(word, *conjugations[idx])
return ('the %s %s %s %s %s the %s %s %s' % tuple(sub_list)).replace('_', ' ')
def generate_phrase_2():
'''Return a phrase and its entropy (in bits) of the form
(# adj noun) (adverb verb) (adjective noun punctuation)
E.g.,
17 MODERATE TRAYS At once live outed wORTH bOSSES
'''
selections = [ADJECTIVES, NOUNS,
ADVERBS, TRANSITIVE_VERBS,
ADJECTIVES, NOUNS, TERMINAL_PUNCTUATION]
entropy = sum([log(len(item), 2) for item in selections])
conjugations = [None, None,
None, [random_item_from_list([PAST, PRESENT]), 3, PLURAL],
None, None,
None]
sub_list = [random_item_from_list(item) for item in selections]
for idx, word in enumerate(sub_list):
if conjugations[idx]:
sub_list[idx] = conjugate(word, *conjugations[idx])
entropy += 1
sub_list[1] = pluralize(sub_list[1])
sub_list[5] = pluralize(sub_list[5])
entropy += log(997, 2)
for idx, item in enumerate(sub_list):
rnd = randint(4)
if rnd == 1:
sub_list[idx] = item.capitalize()
if rnd == 2:
sub_list[idx] = item.upper()
if rnd == 3:
sub_list[idx] = item[0] + item[1:].upper()
entropy += 2
phrase = ('%i %s %s %s %s %s %s%s' % tuple([randint(997) + 2] + sub_list)).replace('_', ' ')
# Insert a random symbol into the sentence
insert_point = randint(len(phrase) + 1)
entropy += log(len(phrase) + 1, 2) + log(len(SYMBOLS), 2)
phrase = phrase[:insert_point] + random_item_from_list(SYMBOLS) + phrase[insert_point:]
insert_point = randint(len(phrase) + 1)
entropy += log(len(phrase) + 1, 2) + log(len(SYMBOLS), 2)
phrase = phrase[:insert_point] + random_item_from_list(SYMBOLS) + phrase[insert_point:]
return phrase, entropy
def _interface(sentence,edblist):
target_words, word_pre, person_taggers, org_taggers = _Stem(sentence, edblist)
token_list =[]
#import pdb; pdb.set_trace()
#print "word_pre:", word_pre
if len(word_pre) > 0:
word_pre[0] = word_pre[0][0].upper() + word_pre[0][1:]
#import pdb; pdb.set_trace()
for word in word_pre:
#import pdb; pdb.set_trace()
tokens = {}
#if word == "He": # is a person, subject?
# tokens[word] = ["He", "She"]
if word.strip().lower() == person_taggers.strip().lower():
tokens[word] = [word, "He", "She"]
#tokens[word] = [ "They"]
elif word.strip().lower() == org_taggers.strip().lower():
if _isplural(org_taggers.strip().split()[-1]) or (org_taggers.strip().split()[-1] == 'they'):
tokens[word] = [word, "They"]
else:
tokens[word] = [word, "It"]
#tokens[word] = [ "It"]
# pass
else:
if lmtzr.lemmatize(word) not in target_words:
token_list.append(word)
else:
r_sent = []
candidates = Generate_candidates_topN(word,sentence,19,edblist)
for i in range(len(candidates)):
r_sent.append(candidates[i] + "@" + sentence.replace(word,candidates[i]))
sub_top10 = kenlm_topn(r_sent,9,sentence)
if lmtzr.lemmatize(word) not in sub_top10:
sub_top10.insert(0,word)
if len(tenses(word)) > 0:
_sub_top10 = []
for w in sub_top10:
_sub_top10.append(conjugate(w, tenses(word)[0][0], 3))
tokens[word] = _sub_top10
else:
tokens[word] = sub_top10
if tokens: token_list.append(tokens)
return token_list
def verb_extract(child_speech):
tokenized = [nltk.word_tokenize(line) for line in child_speech]
tokenized = list(itertools.chain.from_iterable(tokenized))
tokens = [token for token in tokenized if re.match("^[A-Za-z.]*$", token)]
verbs = []
if TAGGER == "senna" or TAGGER == "nltk":
tokens = ' '.join(tokens).split('.')
tagged = [[verb.lower() for (verb,POS) in liste if POS.startswith('VB')] for \
liste in [nltk.pos_tag(nltk.word_tokenize(token)) for token in tokens]]
verbs = filter(None, tagged)
verbs = [item for sublist in verbs for item in sublist]
elif TAGGER == "spacy":
tokens = ' '.join(tokens)
tokens = TAG(unicode(tokens), entity=False)
tokens = zip(tokens, [str(tok.tag_) for tok in tokens])
verbs = [str(verb).lower() for (verb, pos) in tokens if pos.startswith('VB')]
verbs = [(verb, STEM(verb).encode("utf8")) for verb in verbs]
verbs = [(verb, stem) for (verb, stem) in verbs if not (verb in EXCLUSIONS or \
not conjugate(stem, tense="infinitive") or \
is_no_change(stem) or \
conjugate(stem, tense="infinitive") in LIGHT or \
not in_vocabulary(conjugate(stem, tense="infinitive")) or \
conjugate(stem, tense="past").endswith("ed"))]
return verbs
def tagLemma(self, word_old): #print tag(word_old) for word, pos in tag(word_old): if pos=="NNS": #plurales x = singularize(word) elif pos in ["VB","VBG","VBZ","VBP","VBD","VBN","MD"]: # verbos a infinitivo x = conjugate(word, INFINITIVE) #To-Do: fix this if x: # a veces da error al conjugar x = x else: x = word else: x = word return x
def processAction(statement,database_name = DATABASE_NAME):
#raise Exception(statement)
match = re.search(r"what happens (?:(?:if)|(?:when)) (?:the)? ([\s\w]+) ([\s\w]+?) ([\s\w]+)\??",statement)
#raise Exception(match.group(0))
if match:
# need to search action table for
subj = match.group(1)
verb = match.group(2)
verb = conjugate(verb,tense='infinitive')
obj = match.group(3)
result = queryTable("actions",{"origin":subj,"ident":verb,"target":obj},database_name)
if result == None:
return "Sorry, I don't what happens when " + subj + " " + verb + " " + obj
result = queryTable("reactions",{"origin":obj,"action":verb},database_name)
(table,thing) = findTableContainingEntityWithIdentOrName(obj, database_name)
return thing[0] + " says " + result['name']
def add_qualifier(phrase):
n = random.choice(NOUNS)
v = random.choice(VERBS)
if coin_flip(0.5):
a = 'cannot'
else:
a = 'can'
if coin_flip(0.5):
b = 'of'
else:
b = 'for the'
n = pluralize(n)
v = conjugate(v)
qual = '{0} you {1} {2}'.format(n, a, v)
return '{0} {1} {2}'.format(phrase, b, qual)
def verbConjugate(lemma, rel, aan):
relAvoid = ["/r/CapableOf", "/r/PartOf", "/r/MemberOf"
"/r/IsA", "/r/HasA", "/r/TranslationOf",
"/r/HasProperty"]
if not rel in relAvoid:
s = parsetree(lemma, relations=True)
try:
vb = s[0].verbs[0].words[0].string
result = lemma.replace(vb, conjugate(vb, "part"))
except:
result = lemma
else:
if vb in ["to", "can"]:
result = lemma
# if not aan:
# try:
# firstWord = s[0].chunks[0].words[0].string
# reconjugated = conjugate(firstWord, "part")
# result = lemma.replace(firstWord, reconjugated)
# except:
# result = lemma
else:
result = lemma
# NEW PART TO ADD ARTICLES TO BARE NOUN PHRASES FROM CONCEPTNET
# try:
# nns = s[0].subjects + s[0].objects
# except:
# pass
# else:
# if nns:
# for nn in nns:
# if not startsWithCheck(nn.string, ['a', 'an', 'the', 'your', 'his', 'her', 'its']):
# result = result.replace(nn.string, a_or_an(nn.string))
# else:
# pass
return result
"""Transformations for extracting data."""
from pattern.en import (conjugate, PRESENT, INDICATIVE, PROGRESSIVE)
import spacy
from . import base
# TODO: remove the following lines once this issue:
# https://github.com/clips/pattern/issues/243, is resolved.
try:
conjugate('give')
except:
pass
class GerundifyingTransformer(base.LoggedCallable):
"""Transform the input into a gerund phrase."""
_nlp = spacy.load('en', disable=['ner'])
@classmethod
def _is_root(cls, token: spacy.tokens.token.Token) -> bool:
return token.dep_ == 'ROOT' and token.pos_ == 'VERB'
@classmethod
def _is_rootlike(cls, token: spacy.tokens.token.Token) -> bool:
return (cls._is_root(token)
or any(cls._is_root(c) for c in token.conjuncts))
@classmethod
def _conjugate(cls, text: str) -> str:
def toPresent(verb):
return conjugate(verb, PRESENT)
def failure_description_ngram_detect(sentences):
#stop_word_to_investigae = ['to','is' ,'are' , 'not' , 'need' , 'reported' ,'seem' ,'seems' ,'appear' ,'appears']
stop_word_to_investigae = ['is', 'are', 'not', 'to', 'cannot']
for stop_word in stop_word_to_investigae:
stopwords_2 = copy.deepcopy(Utility.stopwords_nltk_pattern_custom)
if stop_word in stopwords_2:
stopwords_2.remove(stop_word)
phrases = Phrases(
sentences,
max_vocab_size=max_vocab_size,
min_count=bigram_minimum_count_threshold,
threshold=threshold,
common_terms=frozenset(stopwords_2),
delimiter=delimiter,
progress_per=progress_per
) # use # as delimiter to distinguish from ~ used in previous stages
with open(save_folder_name + '/' + stop_word + '_bigrams.txt',
"w") as bigram_2_file:
c = 1
for key in phrases.vocab.keys():
#if key not in Utility.stopwords:
if key not in Utility.stopwords_nltk_pattern_custom:
flag = True
a = key.decode()
a = a.split("#")
if len(a) > 1:
if stop_word not in a: # or ('not' not in a and 'be' not in a) :
flag = False
if a[0] != stop_word: #only look for n-grams starting with the stop-word
flag = False
if stop_word == 'to' and 'be' not in a: #if stop_word is 'to', only look for bigram that also has 'bo'.
flag = False
for w in a[
1:]: # go through the rest of the list, and see if positional word are there
if w in Utility.List_of_positional_word:
flag = False
if flag:
# aspell_checker is the original n grams delimited by #
if stop_word == 'to':
last_word = a[-1]
conjugated_last_word = conjugate(last_word)
if conjugated_last_word in Utility.List_of_maintenance_verb:
logger.info("action word found: " +
conjugated_last_word)
s = '~'.join(a)
if s not in List_of_maintenance_action_ngram:
List_of_maintenance_action_ngram.append(
s)
continue #skip the rest code, so that it is not writen into the file
if stop_word == 'is' or stop_word == 'are':
w = a[1:]
ngram_without_is_are = delimiter.decode().join(
w)
if ngram_without_is_are not in List_of_failure_description_ngram_without_is_are:
List_of_failure_description_ngram_without_is_are.append(
ngram_without_is_are)
if len(w) == 1 and w[
0] not in List_of_failure_description_single_word:
List_of_failure_description_single_word.append(
w[0])
s = key.decode()
print('{0}\t\t{1:<30}\t\t{2:<10}'.format(
c, s, phrases.vocab[key]),
file=bigram_2_file)
c += 1
with open(
"./Input_Output_Folder/Failure_Description/List_of_failure_description_ngram_without_is_are.txt",
"w") as words_file:
for index_no, w in enumerate(
List_of_failure_description_ngram_without_is_are):
print('{0}\t\t{1:<10}'.format(index_no, w), file=words_file)
with open(
"./Input_Output_Folder/Failure_Description/List_of_failure_description_single_word.txt",
"w") as words_file:
for index_no, w in enumerate(
sorted(List_of_failure_description_single_word)):
print('{0}\t\t{1:<10}\t\t{2:<10}'.format(
index_no, w, normalized_token_freq_dict[w]),
file=words_file)
with open(
"./Input_Output_Folder/Failure_Description/List_of_maintenance_action_ngram.txt",
"w") as words_file:
for index_no, w in enumerate(sorted(List_of_maintenance_action_ngram)):
print('{0}\t\t{1:<10}'.format(index_no, w), file=words_file)
def conjugateVerb(self, verb, tense): conjugated = conjugate(verb, tense) return conjugated
def extractFeaturesAndWriteBio(READ_PATH, file_type):
global ALL_poems, bio, cnt
for subdir, dirs, files in os.walk(READ_PATH):
for file in files:
num_of_files = len(files) - 1 # deduct the DS_store
#print (num_of_files,'readDirectory',READ_PATH)
if file_type in file and 'readme' not in file:
# ID
id = file.split(".")[0]
print "\nID:", id.split("_")[1]
filenames.append(id)
cnt += 1
# print('')
# print('')
# print('OPENED:',id)
# print('')
# print('')
poem_replaced = ""
replacement_word = ""
previous_replacement_word = ""
author = ""
titles = ""
title = ""
new_title = ""
replaced_ls = []
new_titles_ls = []
quit_language = 0
oscillator = 0
word_cnt = 0
# if EXCEPTION is raised... do not add to html
SKIP_bool = False
##########################
# Load POEM TEXT FILE #
##########################
txt_fn_path = DATA_DIR + READ_TXT_PATH + id.split(
"_")[1] + ".txt"
print "txt_fn_path:", txt_fn_path
if os.path.isfile(txt_fn_path) and cnt > 0:
txt_data = open(txt_fn_path).read()
# http://blog.webforefront.com/archives/2011/02/python_ascii_co.html
# txt_data.decode('ISO-8859-2') .decode('utf-8')
# unicode(txt_data)
author = txt_data.split("****!****")[0].strip(' \t\n\r')
title = txt_data.split("****!****")[1].strip(' \t\n\r')
bio = txt_data.split("****!****")[2] #.strip(' \t\n\r')
###### CLEAN BIO
bio.replace("\t", "	")
bio.replace("\n", " <br>")
bio.replace("\r", " <br>")
poem_replaced = bio
#print poem_replaced
###############################
# REPLACE AUTHOR NAME in poem
##############################
author_ln = author.split(" ")[-1].lstrip()
author_fn = author.split(" ")[:-1]
author = " ".join(n for n in author_fn) + author_ln
#
#poem_replaced = poem_replaced.replace(author_ln,"Jhave")
#######################
# replace BOOK TITLES
#######################
#print "TITLES"]
new_title = getNewTitle("title").encode('utf-8')
#######################
# fake AUTHOR
#######################
new_author = " ".join(
random.choice(authors).split(" ")
[1:-2]) + " " + random.choice(authors).split(" ")[-2]
#print "new AUTHOR",new_author
############################
# replace years with another
############################
for w1 in poem_replaced.split("("):
for w2 in w1.split(")"):
if w2 is not None and w2.isdigit():
new_num = random.randint(
int(w2) - 5,
int(w2) + 5)
#print "REPLACING #:",w2,new_num
poem_replaced = poem_replaced.replace(
w2, str(new_num))
replaced_ls.append(new_num)
#################
# Load JSON #
#################
response = loadJSONfile(READ_JSON_PATH +
"poetryFoundation_" +
id.split("_")[1] +
"_Alchemy_JSON.txt")
if response != "failed":
if response.get('entities') is not None:
for idx, entity in enumerate(response['entities']):
#print idx
ce = entity['text'].replace("0xc2", " ")
ce = ce.replace("0xe2", "'")
ce = re.sub(
'(' +
'|'.join(import_utilities.chars.keys()) +
')', import_utilities.replace_chars, ce)
ce = ce.encode('utf-8')
try:
content = ce.decode('utf-8').encode(
'ascii', 'xmlcharrefreplace')
except UnicodeDecodeError:
"AAAARGGGGHHH!!!!"
if content in poem_replaced:
################################################
# Replace similar entities from other JSON #
################################################
replacement_entity = findSimilarEntityinRandomJSON(
content, entity['type'])
cr = re.sub(
'(' + '|'.join(
import_utilities.chars.keys()) +
')', import_utilities.replace_chars,
replacement_entity)
poem_replaced = poem_replaced.replace(
content, replacement_entity)
replaced_ls.append(replacement_entity)
##########################
# POS REPLACMENT #
##########################
token_tuples = nltk.word_tokenize(poem_replaced)
tt = nltk.pos_tag(token_tuples)
#################
# ADJECTIVES #
#################
for i in tt:
if "/i" not in i[0] and len(
i[0]) > 3 and i[0] != "died":
origw = re.sub(
'(' + '|'.join(import_utilities.chars.keys()) +
')', import_utilities.replace_chars, i[0])
origw = import_utilities.strip_punctuation(origw)
if i[1] == 'JJ':
JJr = random.choice(JJ)
# # JJr = re.sub('(' + '|'.join(import_utilities.chars.keys()) + ')', import_utilities.replace_chars, JJr)
# JJr = import_utilities.strip_punctuation(JJr)
JJr = import_utilities.moveBeginAndEndPunctuationFromStrToString(
i[0],
JJr.lstrip().lstrip())
if i[0].istitle():
JJr = JJr.title()
poem_replaced = re.sub(
r'\b' +
import_utilities.strip_punctuation(i[0]) +
r'\b', JJr, poem_replaced,
1) #poem_replaced.replace(i[0],JJr,1)
replaced_ls.append(JJr)
if i[1] == 'RB':
RBr = random.choice(RB)
RBr = import_utilities.moveBeginAndEndPunctuationFromStrToString(
i[0],
RBr.lstrip().lstrip())
if i[0].istitle():
RBr = RBr.title()
poem_replaced = re.sub(
r'\b' +
import_utilities.strip_punctuation(i[0]) +
r'\b', RBr, poem_replaced, 1)
replaced_ls.append(RBr)
########################
# IS IT ENGLISH? #
########################
for line in poem_replaced.split('\n\r'):
if len(line) > 0:
if "english" not in import_utilities.get_language(
line):
quit_language += 1
#print "NOT english:",quit_language,line
else:
quit_language -= 1
#########################
# SYNSET REPLACE #
#########################
for idx, word in enumerate(poem_replaced.split(' ')):
if "<br>" not in word and "	" not in word and len(
word) > 0:
#########################
# PRONOUN ' VERB #
#########################
if len(word.split("'")) > 1:
if word.split("'")[0] in personal_pronouns:
replacement_word = random.choice(
personal_pronouns) + "'" + word.split(
"'")[1] + ' '
poem_replaced.replace(word, replacement_word)
#print "word,",word,"replacement_word:",replacement_word
####################################################
# Replacement of OTHERs #
####################################################
elif not word.lower().strip(
" \n\t\r") in stopwords.words('english'):
# take off leading brackets, commas etc...
word_punct_nopunct = import_utilities.strip_punctuation_bool(
word)
word_nopunct = word_punct_nopunct[
'word'].strip(" \n\t\r")
word_punct = word_punct_nopunct['punct']
punct_bool = word_punct_nopunct['punct_bool']
#######################################################
# MAIN EXCHANGE PROCESS CALL >>>>>>> GET THE SYNSET #
#######################################################
if word_nopunct[-4:].lower() == "here":
similarterm = random.choice(
import_utilities.heres)
else:
#print "WORD:",word_nopunct
if len(word_nopunct) > 3:
oscillator = oscillator + 1
############################################
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# STYLE SWITCH..... should in future use POS
# ... i.e. if noun & oscillator%3, do...
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
############################################
# synset
similarterm = import_utilities.synset_creeley(
word_nopunct)
#print "synset", similarterm
if similarterm is not None and similarterm == word_nopunct and len(
word_nopunct) > 5:
RESERVOIR.sort(key=len)
similarterm = RESERVOIR[
idx % len(RESERVOIR)]
print idx, len(
RESERVOIR
), similarterm, word_nopunct, "PRE>>>>>>>>LAST CHANGE STOP: ", word, "~", similarterm
############################################
# manually get rid of some terrible choices
############################################
naw_terms = ["mind", "lonely"]
if similarterm == "ilk":
##print "like"
similarterm = "like"
if similarterm == "Nox":
##print "like"
similarterm = "nite"
if similarterm == "ope":
##print "doth"
similarterm = "does"
if similarterm == "information technology":
##print "doth"
similarterm = "it"
if similarterm == "velleity":
##print "doth"
similarterm = "want"
if similarterm == "Crataegus laevigata":
##print "doth"
similarterm = "may"
if similarterm == "eff":
##print "doth"
similarterm = "know"
if similarterm == "naw":
##print "doth"
similarterm = "mind"
#######################################
# abbreviations for f*****g states! #
#######################################
if word_nopunct.upper(
) in import_utilities.state_abbrev and word_nopunct.lower(
) not in stopwords.words(
'english') and "me," not in word:
tmp = similarterm
if word_nopunct == "oh":
similarterm = random.choice(
import_utilities.exclaims)
else:
similarterm = random.choice(RESERVOIR)
#print word_nopunct," replaced by", tmp, "replaced with:",similarterm, "in:",line
##############
# hyphenated #
##############
hyp = word.split("-")
#print word,len(hyp)
if len(hyp) > 1:
similarterm = ""
for w in hyp:
if len(w) > 2:
if import_utilities.synset_creeley(
w) is not None:
similarterm += import_utilities.synset_creeley(
w) + "-"
else:
similarterm += w + "-"
similarterm = import_utilities.strip_underscore(
similarterm[:-1])
#print "hyphenated:",word,"replaced by: "+similarterm
# #########################################################
# # is it a TRUNCATED VERB slang as in singin or wishin #
# #########################################################
# if similarterm == word_nopunct and len(word)>2 and 'in' in word_nopunct[-2:]:
# similarterm = import_utilities.synset_creeley(word_nopunct+'g')
# ## #print "TRUNCATED SLANG word: '"+word+"'",similarterm
# interim = import_utilities.lemma(similarterm)
# ## #print interim
# similarterm = import_utilities.conjugate(interim, tense=import_utilities.PARTICIPLE, parse=True)[:-1]
# # # # #print word,"widx:",widx," line_pos_tags[widx][0]:",line_pos_tags[widx][0]," line_pos_tags[widx][1]:",line_pos_tags[widx][1]
#################
# SWEAR WORD #
#################
##print "at the garden of if:", word
if word_nopunct in import_utilities.curses:
similarterm = random.choice(
import_utilities.curses)
##print "SWEAR WORD word: '"+word+"'",similarterm
#print "SIMILAR:",similarterm
if similarterm is not None:
if len(hyp) > 1:
replacement_word = similarterm
else:
replacement_word = word.replace(
word_nopunct, similarterm)
replacement_word = import_utilities.strip_underscore(
replacement_word)
replacement_word = import_utilities.replaceNumbers(
replacement_word)
#########################
# RESERVOIR_OF_WEIRDNESS #
#########################
if word_nopunct.lower(
) in import_utilities.impera:
replacement_word = random.choice(
import_utilities.impera)
#print word,"IMPERA:",replacement_word
elif word_nopunct.lower(
) in import_utilities.conjuncts:
replacement_word = random.choice(
import_utilities.conjuncts)
#print word," CONJUNCTION replaced with",replacement_word
elif word_nopunct.lower(
) in import_utilities.indef_prono:
replacement_word = random.choice(
import_utilities.indef_prono)
#print word," INDEF_prono replaced with",replacement_word
elif word_nopunct.lower(
) in import_utilities.prepo:
replacement_word = random.choice(
import_utilities.prepo)
#print word," prepo replaced with",replacement_word
elif word_nopunct.lower(
) in import_utilities.rel_prono:
replacement_word = word
#print word," rel_prono LEAVE alone: ",replacement_word
elif word_nopunct.lower()[-2:] == "ly":
if import_utilities.synset_creeley(
word) is not None:
replacement_word = import_utilities.strip_underscore(
import_utilities.synset_creeley(
word)) #(word[:-2])
#print word," ADVERB: ",replacement_word
# if replacement_word[-2:] !="ly":
# replacement_word +="ly"
else:
if len(
hyp
) < 2 and "like" not in word_nopunct and import_utilities.singularize(
word_nopunct
) == import_utilities.singularize(
replacement_word
) and word_nopunct.lower(
) not in import_utilities.stopwords_ls:
if word not in RESERVOIR and quit_language < 0 and import_utilities.countPunctuation(
word
) < 1 and len(
word_nopunct
) > 3 and not word_nopunct.istitle():
#print "ADDING",word,"to reservoir"
############################
# ADDING ONLY SMALL WORDS
############################
if len(word) < 7:
RESERVOIR.append(word)
replacement_word = random.choice(
rap_mouth) # RESERVOIR)
#print word_nopunct,"replaced from reservoir with", replacement_word
# print "'"+word_nopunct+"' vs RESERVOIR replacement_word:",replacement_word #," new_line:",new_line
if quit_language > 1 and not word_nopunct.istitle(
):
#print quit_language, "Probably foreign language: make a word salad in english"
replacement_word = random.choice(
rap_mouth) #RESERVOIR)
#print word_nopunct,"OTHER replaced from reservoir with", replacement_word
###################################################
# MOST REPLACEMENT occurs here... #
###################################################
poem_ls = poem_replaced.split(' ')
idx = poem_ls.index(word)
# print idx,",", poem_ls[idx],",", word ,",",replacement_word
#print word ," --- ",previous_replacement_word,replacement_word
if len(word) > 3 and replacement_word.lstrip(
).rstrip() == word_nopunct.lstrip().rstrip():
# try alchemy?
# a
RESERVOIR.sort(key=len)
replacement_word = RESERVOIR[
idx % len(RESERVOIR)]
print idx, len(
RESERVOIR
), "LAST CHANGE STOP: ", word, "~", replacement_word
try:
if poem_ls[
idx] == word and "****" not in word and "." != word and "\n" not in word:
poem_ls[
idx] = replacement_word #.encode('utf-8')
poem_replaced = " ".join(poem_ls)
# store this word so that conjugation can be checked
previous_replacement_word = replacement_word
except Exception, e:
#print "PENULTIMATE SKIP_bool replace FAIL",e
SKIP_bool = True
continue
###########################################################################
# testing Pattern.en as parser for conjugation and article replacement #
# much more robust than my hand-coded hacks #
###########################################################################
# correct CONJUGATion of paticiple verbs with pattern.en
parsed = parse(poem_replaced, tags=True)
pre_verbal = ["'m", "'s", "'re"]
for idx, p in enumerate(parsed.split(" ")):
tok = p.split("/")[0]
typ = p.split("/")[1]
#print idx,tok,typ
if tok in pre_verbal:
#print "pre_verbal:",tok
next_word = parsed.split(" ")[idx + 1].split("/")
# try try try
for ix, n in enumerate(next_word):
next_word[ix] = re.sub(
'(' +
'|'.join(import_utilities.chars.keys()) +
')', import_utilities.replace_chars,
n).encode('utf-8')
try:
#print next_word,next_word[0],next_word[1][:2]
# if it's a verb that follows
if next_word[1][:2] == "VB":
before_verb = " ".join(
w for w in poem_replaced.split(" ")
[:idx]) #.encode('utf-8')
after_verb = " ".join(
w for w in poem_replaced.split(" ")
[idx + 1:]) #.encode('utf-8')
new_verb = conjugate(
next_word[0],
tense=PARTICIPLE,
parse=True).encode('utf-8')
# insert new
#print "CONJUGATION needed, changing:",poem_replaced.split(" ")[idx],"to",parsed.split(" ")[idx],poem_replaced.split(" ")[idx-1]+" "+new_verb
poem_replaced = before_verb + " " + new_verb + " " + after_verb
except Exception, e:
# print "INside parsed COnjugation loop",e
continue
# correct ARTICLES
for idx, word in enumerate(poem_replaced.split(" ")):
if len(word) > 0 and idx != 0 and " " not in word:
# A or AN
if poem_replaced.split(" ")[idx - 1].lower(
) == "a" or poem_replaced.split(" ")[
idx - 1].lower() == "an":
#print word,"---",article(word)+" "+word
before_article = " ".join(
w for w in poem_replaced.split(" ")[:idx -
1])
after_article = " ".join(
w for w in poem_replaced.split(" ")[idx +
1:])
new_conj = referenced(word)
# capitalize
if poem_replaced.split(" ")[idx - 1].istitle():
new_conj = new_conj.split(" ")[0].title(
) + " " + new_conj.split(" ")[1]
poem_replaced = before_article + " " + new_conj + " " + after_article
#########################
# WRITE SINGLE POEM #
#########################
if not SKIP_bool:
tmp_poem = ""
# poem_replaced.replace("\t","	")
# poem_replaced.replace("\n"," <br>")
# poem_replaced.replace("\r"," <br>")
HTML_poem = ""
for line in poem_replaced.split("\n"):
#print "LINE", line
HTML_poem += line + "<br>"
if len(response) > 0 and len(id.split("_")) > 1:
# ALL_poems = ALL_poems_intro + " ".join(i for i in ALL_poems.split("</h2>.")[0:])+"<br><br>~~~~~~~~~~~~~~~~~~~~~~~~~~<br>[ A poem generated from template : <b>"+ author+"</b>, <i>"+ title +"</i> ]<br><br><b>"+new_title+"<br><br></b>"+HTML_poem
try:
ALL_poems = "<br>[ A generated-poem based upon: <i>" + title + "</i> by <b>" + author + "</b>]<br><br><i>" + new_title + "</i><br> by <b>" + new_author + "</b><br>" + HTML_poem + ALL_poems.split(
"</h2>")[1].replace(" ", " ")
tmp_poem = "[A generated-poem based upon: '" + title + "' by " + author + "]\n\n" + new_title + "\nby " + new_author + "\n" + poem_replaced
print "\n~~~\n\n" + tmp_poem
#print "\nORIGINAL:",bio
txt_fn = id.split("_")[1] + "_POEMs.txt"
WRITE_BIO_PATH = DATA_DIR + "generated/POEMS/POEMS_" + datetime.datetime.now(
).strftime('%Y-%m-%d_%H') + "/"
if not os.path.exists(WRITE_BIO_PATH):
os.makedirs(WRITE_BIO_PATH)
txt_fn_path = WRITE_BIO_PATH + txt_fn
f_txt = open(txt_fn_path, 'w')
f_txt.write(tmp_poem) #.encode('utf-8'))
f_txt.close()
#print "\nTXT file created at:",txt_fn_path
#######
# write them all.... wasteful... but useful if run is interrupted....
###########
# if cnt==1:
# ALL_poems = ALL_poems_intro+ALL_poems
# else:
ALL_poems = ALL_poems_intro + ALL_poems.replace(
" ", " ")
ALL_poems = ALL_poems.replace(
"$$datetime$$",
datetime.datetime.now().strftime(
'%Y-%m-%d at %H:%M'))
ALL_poems = ALL_poems.replace(
"$$cnt$$", str(cnt))
print "cnt", cnt
ALL_poems = ALL_poems.replace(
"$$gentime$$",
str(time.time() - start_time))
# ALL POEMS
txt_fn = datetime.datetime.now().strftime(
'%Y-%m-%d_%H'
) + "_poetryFoundation_generatedPOEMS_CREELEYstyle_" + type_of_run + ".html"
txt_fn_path = DATA_DIR + "generated/POEMS/" + txt_fn
f_txt = open(txt_fn_path, 'w')
f_txt.write(ALL_poems + "</hmtl>")
f_txt.close()
print "\nTXT file created at:", txt_fn_path
except Exception, e:
print "At the final LOOP", e
continue
else:
print "~! EMPTY response:", author
else:
cnt = cnt - 1
def _find_verbs(self, word):
word_bigrams = [(a[0], b[0]) for a, b in self.word_tag_pairs
if a[0] == word.name and a[1] == 'NOUN' and b[1] == 'VERB'
and en.conjugate(b[0], "inf") not in ('be', 'have')]
return self.__get_best_collocations(word, word_bigrams)
def change_tense_fixed(text, tense):
if text == 'leave' and tense == PAST:
return 'left'
if text == 'quit' and tense == PAST:
return 'quit'
else: return conjugate(text, tense)