def sent_heuristic(sentence, normalized=False): print(seed) words = sentence.replace(',', '').split() print(words) adj_relevance = [word_similarity(w+'-a', seed) for w in words] verb_relevance = [word_similarity(lemma(w)+'-n', seed) for w in words] noun_relevance = [word_similarity(lemma(w)+'-v', seed) for w in words] adj_relevance = [s for s in adj_relevance if s > 0] verb_relevance = [s for s in verb_relevance if s > 0] noun_relevance = [s for s in noun_relevance if s > 0 ] total_rels = len(adj_relevance) + len(verb_relevance) + len(noun_relevance) #total_rels = (len(adj_relevance) + len(noun_relevance)) if seed.endswith('-n') else (len(verb_relevance) + len(noun_relevance)) if total_rels == 0: total_rels = 1 relevance = sum(adj_relevance) + sum(verb_relevance) + sum(noun_relevance) #relevance = (sum(adj_relevance) + sum(noun_relevance)) if seed.endswith('-n') else (sum(verb_relevance) + sum(noun_relevance)) print("relevance: {}".format(relevance)) score = score_sentence(sentence.replace(',', ''), normalized=normalized) print("score: {}".format(score)) interpolation = ((relevance / total_rels) * 0.01) + score print("interpolation: {}".format(interpolation)) return interpolation
def sent_heuristic(sentence, normalized=False): print(seed) words = sentence.replace(',', '').split() print(words) adj_relevance = [word_similarity(w + '-a', seed) for w in words] verb_relevance = [ word_similarity(lemma(w) + '-n', seed) for w in words ] noun_relevance = [ word_similarity(lemma(w) + '-v', seed) for w in words ] adj_relevance = [s for s in adj_relevance if s > 0] verb_relevance = [s for s in verb_relevance if s > 0] noun_relevance = [s for s in noun_relevance if s > 0] total_rels = len(adj_relevance) + len(verb_relevance) + len( noun_relevance) #total_rels = (len(adj_relevance) + len(noun_relevance)) if seed.endswith('-n') else (len(verb_relevance) + len(noun_relevance)) if total_rels == 0: total_rels = 1 relevance = sum(adj_relevance) + sum(verb_relevance) + sum( noun_relevance) #relevance = (sum(adj_relevance) + sum(noun_relevance)) if seed.endswith('-n') else (sum(verb_relevance) + sum(noun_relevance)) print("relevance: {}".format(relevance)) score = score_sentence(sentence.replace(',', ''), normalized=normalized) print("score: {}".format(score)) interpolation = ((relevance / total_rels) * 0.01) + score print("interpolation: {}".format(interpolation)) return interpolation
def convert_words(v): return '_'.join(lemma(w.string) for w in v.words if w.pos != "DT" and not w.pos.startswith('PRP') and w.string.isalpha() ).replace('-', '_')
def convert_words(v): return ' '.join(lemma(w.string) if w.pos.startswith('VB') else w.string for w in v.words if lemma(w.string) != "be" and w.string.lower() not in ('to', 'wo') and w.string.isalpha())
def exploration_log(self, type="COMPUTER"): """ Return a string of the exploration log for this article. """ if type == "COMPUTER": def step_to_computer_verbs(string): parse = parsetree(string, relations=True)[0] def convert_words(v): return ' '.join(lemma(w.string) if w.pos.startswith('VB') else w.string for w in v.words if lemma(w.string) != "be" and w.string.lower() not in ('to', 'wo') and w.string.isalpha()) chunks = [convert_words(v) for v in parse.verbs] return ' -- '.join(c for c in chunks if len(c) > 0).upper() def step_to_computer_nouns(string): parse = parsetree(string, relations=True)[0] def convert_words(v): return '_'.join(lemma(w.string) for w in v.words if w.pos != "DT" and not w.pos.startswith('PRP') and w.string.isalpha() ).replace('-', '_') chunks = [convert_words(v) for v in parse.chunk if v.pos == 'NP'] return ' | '.join(c for c in chunks if len(c) > 0).upper() def step_to_computer_adjs(string): try: parse = parsetree(string, relations=True)[0] return ' '.join(a.string for a in parse.adjectives if a.string.isalpha()) except IndexError: return '' verbs = [step_to_computer_verbs(s.main) for s in self.steps] verbs = [v if len(v) > 0 else '???' for v in verbs] nouns = [step_to_computer_nouns(s.main) for s in self.steps] nouns = [n if len(n) > 0 else '***' for n in nouns] adjs = [' '.join(set(' '.join((step_to_computer_adjs(s.main), step_to_computer_adjs(s.extra))).split())) for s in self.steps] adjs = [a.lower() if len(a) > 0 else '!null' for a in adjs] lines = ['{:02d}... {} ( {} ) => {}'.format(i, v, n, a) for i, v, n, a in zip(range(len(verbs)), verbs, nouns, adjs)] title_words = self.title.lower().strip()[7:].split() title_words = [w for w in title_words if w not in ("your", "a", "an", "the")] if title_words[0].lower() != "not": title_words[0] = conjugate(lemma(title_words[0]), 'part').upper() else: title_words[1] = conjugate(lemma(title_words[1]), 'part').upper() title = "{0:^80}".format("- " + ' '.join(title_words) + " -").upper() fixed_lines = [textwrap.fill(textwrap.dedent(line).strip(), initial_indent='', subsequent_indent=' ', width=76) for line in lines] return '\n\n'.join([title] + fixed_lines) else: raise NotImplementedError("Type {} of logging not implemented!".format(type))
def build_dream(self, wordlist, seed_concept): seed = seed_concept.lemma + '-n' if seed_concept.type == Concept.THING else seed_concept.lemma + '-v' words_and_lemmas = [(w, lemma(w.string)) for w in wordlist if len(w.string) > 3 and w.string.isalpha()] noun_blacklist = [ 'none', 'male', 'female', 'women', 'men', 'hear', 'thru', 'weirder', 'guy', 'mother', 'father', 'daughter', 'brother', 'mama', 'wife', 'thing', 'soooo', 'chan' ] adj_blacklist = [ 'such', 'much', 'however', 'about', 'most', 'least', 'more', 'less', 'else', 'enough', 'sooo' ] verb_blacklist = ['men', 'dont', 'left', 'seem'] # STOP SAYING MEN IS A VERB UGH adv_blacklist = [ 'most', 'least', 'kinda', 'quite', 'down', 'there', 'here', 'alot', 'much', 'such', 'more', 'back', 'else', 'very', 'about', 'sooo', 'rather', 'however', 'thus' ] print('getting nouns...') nounset = set(w_lemma for w, w_lemma in words_and_lemmas if w.pos.startswith('NN') and w.pos != 'NNP' and w_lemma not in noun_blacklist) print(len(nounset)) nouns = [w for w in nounset if 'noun' in wordnik_pos(w)] print('getting adjs...') adjset = list( set(w.string.lower() for w, w_lemma in words_and_lemmas if w.pos.startswith('JJ') and w.string.lower() not in adj_blacklist)) adjs = [w for w in adjset if 'adjective' in wordnik_pos(w)] print('getting verbs...') verbset = list( set(w_lemma for w, w_lemma in words_and_lemmas if w.pos.startswith('VB') and w_lemma not in verb_blacklist)) verbs = [ w for w in verbset if any(pos is not None and pos.startswith('verb') for pos in wordnik_pos(w)) ] print('getting advs...') advset = list( set(w.string.lower() for w, w_lemma in words_and_lemmas if w.pos.startswith('RB') and w.string.lower() not in adv_blacklist)) advs = [w for w in advset if 'adverb' in wordnik_pos(w)] #random.shuffle(nouns) #random.shuffle(adjs) nouns = sorted(nouns, key=lambda x: word_similarity(x + '-n', seed), reverse=True) adjs = sorted(adjs, key=lambda x: word_similarity(x + '-a', seed), reverse=True) a_n_shortest = min(len(nouns), len(adjs)) nps = [a_n for a_n in zip(adjs[:a_n_shortest], nouns[:a_n_shortest]) ] + [('', n) for n in nouns[a_n_shortest:]] #random.shuffle(verbs) random.shuffle(advs) verbs = sorted(verbs, key=lambda x: word_similarity(x + '-v', seed), reverse=True) a_v_shortest = min(len(verbs), len(advs)) vps = [a_v for a_v in zip(advs[:a_v_shortest], verbs[:a_v_shortest]) ] + [('', v) for v in verbs[a_v_shortest:]] # sort by similarity nps = sorted(nps, key=lambda x: max(word_similarity(x[0] + '-a', seed), word_similarity(x[1] + '-n', seed))) vps = sorted(vps, key=lambda x: word_similarity(x[1] + '-v', seed)) #random.shuffle(nps) #random.shuffle(vps) def make_noun_string(np, plural=False): # random chance of removing modifier #if random.random() < 0.5: # np[0] == '' # common mass nouns if np[1] in [ 'data', 'information', 'children', 'people', 'stuff', 'equipment' ]: return ' '.join(np).strip() elif any(np[1].lower().startswith(x) for x in ('every', 'any', 'some')) or np[1] in ('nothing', 'nobody'): return np[1] quantifiers = [ 'many', 'few', 'several', 'various', 'multiple', 'fewer', 'more' ] if np[0] in quantifiers: return np[0] + ' ' + pluralize(np[1]) else: die_roll = random.random() if die_roll < 0.15 or plural: return ' '.join((np[0], pluralize(np[1]))).strip() elif die_roll < 0.25: return random.choice( ('his', 'her', 'their', 'your')) + ' ' + ' '.join(np).strip() elif random.random() < 0.45: return referenced(' '.join(np).strip()) else: return 'the ' + ' '.join(np).strip() def make_verb_string(vp, conj='part'): # random chance of removing modifier #if random.random() < 0.5: # vp[0] == '' verb = conjugate(vp[1], conj) if verb == 'thinking': verb = 'thinking of' if verb == 'arriving': verb = 'arriving at' if verb == 'coming': verb = 'coming from' if verb == 'going': verb = 'going to' return ' '.join((vp[0], verb)).strip() def get_transitive_vp(): vp = vps.pop() transitivity = is_transitive(vp[1]) checked = [] while not transitivity: if transitivity is not None: checked.append(vp) vp = vps.pop() transitivity = is_transitive(vp[1]) vps.extend(checked) return vp def get_intransitive_vp(): vp = vps.pop() intransitivity = is_intransitive(vp[1]) checked = [] while not intransitivity: if intransitivity is not None: checked.append(vp) vp = vps.pop() intransitivity = is_intransitive(vp[1]) vps.extend(checked) return vp story = [] while True: try: case = random.randint(0, 12) next_sent = "" if case == 0: template = "{noun_string}, {verb_string}" noun_string = make_noun_string(nps.pop()) verb_string = make_verb_string(get_intransitive_vp()) next_sent = template.format(noun_string=noun_string, verb_string=verb_string) elif case == 1: template = "{noun_string} {verb_string}" noun_string = make_noun_string(nps.pop(), plural=True) verb_string = make_verb_string(get_intransitive_vp()) next_sent = template.format(noun_string=noun_string, verb_string=verb_string) elif case == 2: template = "{} {} and {} {}" two_vp = [ make_verb_string(get_transitive_vp()) for _ in range(3) ] two_np = [make_noun_string(nps.pop()) for _ in range(3)] next_sent = template.format(two_vp[0], two_np[0], two_vp[1], two_np[1]) elif case == 3: template = "{verb_string} {noun_string}" #np = nps.pop() #noun_string = ' '.join((np[0], pluralize(np[1]))).strip() noun_string = make_noun_string(nps.pop(), plural=True) verb_string = make_verb_string(get_transitive_vp()) next_sent = template.format(noun_string=noun_string, verb_string=verb_string) elif 4 <= case <= 8: preps = ('on', 'around', 'in', 'near', 'behind', 'over', 'under', 'like') template = "{} " + random.choice(preps) + " {}" noun_strings = [ make_noun_string(nps.pop()) for _ in range(2) ] if random.random() < 0.5: next_sent = template.format(*noun_strings) else: verb_string = make_verb_string(get_transitive_vp()) next_sent = verb_string + ' ' + template.format( *noun_strings) elif case == 9: template = "{} while {}" verb_strings = [ make_verb_string(get_intransitive_vp()) for _ in range(2) ] next_sent = template.format(*verb_strings) elif 10 <= case <= 12: template = "{noun_string1} {verb_string} {noun_string2}" noun_string1 = make_noun_string(nps.pop(), plural=True) noun_string2 = make_noun_string(nps.pop(), plural=True) verb_string = make_verb_string(get_transitive_vp()) next_sent = template.format(noun_string1=noun_string1, noun_string2=noun_string2, verb_string=verb_string) # move the adverb around """ if random.random() < 0.5 and 'ly' in next_sent and ' and ' not in next_sent: words = next_sent.split() dont_move = ['actually', 'really', 'probably', 'nearly', 'solely'] ly_words = [w for w in words if w.endswith('ly') and w[0] not in 'aeiou' and w not in dont_move] if len(ly_words) == 1: ly_word = ly_words[0] words.remove(ly_word) words.append(ly_word) next_sent = ' '.join(words) """ story.append(next_sent) except IndexError: break
def censored_terms(sent): return [w for w in sent if any(w.pos.startswith(x) for x in ('NN', 'JJ', 'RB', 'VB')) \ and not any(self.concepts.contains(lemma(w.string), t) for t in (Concept.THING, Concept.ACTION, Concept.DESCRIPTOR)) ]
def contains(self, string, type): try: return lemma(string) in self._concept_sets[type] except KeyError: raise KeyError("Invalid or undefined concept type")
def __init__(self, string, type): self.lemma = lemma(string) self.type = type self.relations = ConceptRelationSet()
def contains(self, string, type): try: return lemma(string) in self._concept_sets[type] except KeyError: raise KeyError("Invalid or undefined concept type")
def __init__(self, string, type): self.lemma = lemma(string) self.type = type self.relations = ConceptRelationSet()
def build_dream(self, wordlist, seed_concept): seed = seed_concept.lemma + '-n' if seed_concept.type == Concept.THING else seed_concept.lemma + '-v' words_and_lemmas = [(w, lemma(w.string)) for w in wordlist if len(w.string) > 3 and w.string.isalpha()] noun_blacklist = ['none', 'male', 'female', 'women', 'men', 'hear', 'thru', 'weirder', 'guy', 'mother', 'father', 'daughter', 'brother', 'mama', 'wife', 'thing', 'soooo', 'chan'] adj_blacklist = ['such', 'much', 'however', 'about', 'most', 'least', 'more', 'less', 'else', 'enough', 'sooo' ] verb_blacklist = ['men', 'dont', 'left', 'seem'] # STOP SAYING MEN IS A VERB UGH adv_blacklist = ['most', 'least', 'kinda', 'quite', 'down', 'there', 'here', 'alot', 'much', 'such', 'more', 'back', 'else', 'very', 'about', 'sooo', 'rather', 'however', 'thus'] print('getting nouns...') nounset = set(w_lemma for w, w_lemma in words_and_lemmas if w.pos.startswith('NN') and w.pos != 'NNP' and w_lemma not in noun_blacklist ) print(len(nounset)) nouns = [w for w in nounset if 'noun' in wordnik_pos(w)] print('getting adjs...') adjset = list(set(w.string.lower() for w, w_lemma in words_and_lemmas if w.pos.startswith('JJ') and w.string.lower() not in adj_blacklist )) adjs = [w for w in adjset if 'adjective' in wordnik_pos(w)] print('getting verbs...') verbset = list(set(w_lemma for w, w_lemma in words_and_lemmas if w.pos.startswith('VB') and w_lemma not in verb_blacklist )) verbs = [w for w in verbset if any(pos is not None and pos.startswith('verb') for pos in wordnik_pos(w))] print('getting advs...') advset = list(set(w.string.lower() for w, w_lemma in words_and_lemmas if w.pos.startswith('RB') and w.string.lower() not in adv_blacklist )) advs = [w for w in advset if 'adverb' in wordnik_pos(w)] #random.shuffle(nouns) #random.shuffle(adjs) nouns = sorted(nouns, key=lambda x: word_similarity(x+'-n', seed), reverse=True) adjs = sorted(adjs, key=lambda x: word_similarity(x+'-a', seed), reverse=True) a_n_shortest = min(len(nouns), len(adjs)) nps = [a_n for a_n in zip(adjs[:a_n_shortest], nouns[:a_n_shortest])] + [('', n) for n in nouns[a_n_shortest:]] #random.shuffle(verbs) random.shuffle(advs) verbs = sorted(verbs, key=lambda x: word_similarity(x+'-v', seed), reverse=True) a_v_shortest = min(len(verbs), len(advs)) vps = [a_v for a_v in zip(advs[:a_v_shortest], verbs[:a_v_shortest])] + [('', v) for v in verbs[a_v_shortest:]] # sort by similarity nps = sorted(nps, key=lambda x: max(word_similarity(x[0]+'-a', seed), word_similarity(x[1]+'-n', seed))) vps = sorted(vps, key=lambda x: word_similarity(x[1]+'-v', seed)) #random.shuffle(nps) #random.shuffle(vps) def make_noun_string(np, plural=False): # random chance of removing modifier #if random.random() < 0.5: # np[0] == '' # common mass nouns if np[1] in ['data', 'information', 'children', 'people', 'stuff', 'equipment']: return ' '.join(np).strip() elif any(np[1].lower().startswith(x) for x in ('every', 'any', 'some')) or np[1] in ('nothing', 'nobody'): return np[1] quantifiers = ['many', 'few', 'several', 'various', 'multiple', 'fewer', 'more'] if np[0] in quantifiers: return np[0] + ' ' + pluralize(np[1]) else: die_roll = random.random() if die_roll < 0.15 or plural: return ' '.join((np[0], pluralize(np[1]))).strip() elif die_roll < 0.25: return random.choice(('his', 'her', 'their', 'your')) + ' ' + ' '.join(np).strip() elif random.random() < 0.45: return referenced(' '.join(np).strip()) else: return 'the ' + ' '.join(np).strip() def make_verb_string(vp, conj='part'): # random chance of removing modifier #if random.random() < 0.5: # vp[0] == '' verb = conjugate(vp[1], conj) if verb == 'thinking': verb = 'thinking of' if verb == 'arriving': verb = 'arriving at' if verb == 'coming': verb = 'coming from' if verb == 'going': verb = 'going to' return ' '.join((vp[0], verb)).strip() def get_transitive_vp(): vp = vps.pop() transitivity = is_transitive(vp[1]) checked = [] while not transitivity: if transitivity is not None: checked.append(vp) vp = vps.pop() transitivity = is_transitive(vp[1]) vps.extend(checked) return vp def get_intransitive_vp(): vp = vps.pop() intransitivity = is_intransitive(vp[1]) checked = [] while not intransitivity: if intransitivity is not None: checked.append(vp) vp = vps.pop() intransitivity = is_intransitive(vp[1]) vps.extend(checked) return vp story = [] while True: try: case = random.randint(0, 12) next_sent = "" if case == 0: template = "{noun_string}, {verb_string}" noun_string = make_noun_string(nps.pop()) verb_string = make_verb_string(get_intransitive_vp()) next_sent = template.format(noun_string=noun_string, verb_string=verb_string) elif case == 1: template = "{noun_string} {verb_string}" noun_string = make_noun_string(nps.pop(), plural=True) verb_string = make_verb_string(get_intransitive_vp()) next_sent = template.format(noun_string=noun_string, verb_string=verb_string) elif case == 2: template = "{} {} and {} {}" two_vp = [make_verb_string(get_transitive_vp()) for _ in range(3)] two_np = [make_noun_string(nps.pop()) for _ in range(3)] next_sent = template.format(two_vp[0], two_np[0], two_vp[1], two_np[1]) elif case == 3: template = "{verb_string} {noun_string}" #np = nps.pop() #noun_string = ' '.join((np[0], pluralize(np[1]))).strip() noun_string = make_noun_string(nps.pop(), plural=True) verb_string = make_verb_string(get_transitive_vp()) next_sent = template.format(noun_string=noun_string, verb_string=verb_string) elif 4 <= case <= 8: preps = ('on', 'around', 'in', 'near', 'behind', 'over', 'under', 'like') template = "{} " + random.choice(preps) + " {}" noun_strings = [make_noun_string(nps.pop()) for _ in range(2)] if random.random() < 0.5: next_sent = template.format(*noun_strings) else: verb_string = make_verb_string(get_transitive_vp()) next_sent = verb_string + ' ' + template.format(*noun_strings) elif case == 9: template = "{} while {}" verb_strings = [make_verb_string(get_intransitive_vp()) for _ in range(2)] next_sent = template.format(*verb_strings) elif 10 <= case <= 12: template = "{noun_string1} {verb_string} {noun_string2}" noun_string1 = make_noun_string(nps.pop(), plural=True) noun_string2 = make_noun_string(nps.pop(), plural=True) verb_string = make_verb_string(get_transitive_vp()) next_sent = template.format(noun_string1=noun_string1, noun_string2=noun_string2, verb_string=verb_string) # move the adverb around """ if random.random() < 0.5 and 'ly' in next_sent and ' and ' not in next_sent: words = next_sent.split() dont_move = ['actually', 'really', 'probably', 'nearly', 'solely'] ly_words = [w for w in words if w.endswith('ly') and w[0] not in 'aeiou' and w not in dont_move] if len(ly_words) == 1: ly_word = ly_words[0] words.remove(ly_word) words.append(ly_word) next_sent = ' '.join(words) """ story.append(next_sent) except IndexError: break story = [s for s in story if not any(s.endswith(x) for x in ('to', 'from', 'at', 'of'))] def sent_heuristic(sentence, normalized=False): print(seed) words = sentence.replace(',', '').split() print(words) adj_relevance = [word_similarity(w+'-a', seed) for w in words] verb_relevance = [word_similarity(lemma(w)+'-n', seed) for w in words] noun_relevance = [word_similarity(lemma(w)+'-v', seed) for w in words] adj_relevance = [s for s in adj_relevance if s > 0] verb_relevance = [s for s in verb_relevance if s > 0] noun_relevance = [s for s in noun_relevance if s > 0 ] total_rels = len(adj_relevance) + len(verb_relevance) + len(noun_relevance) #total_rels = (len(adj_relevance) + len(noun_relevance)) if seed.endswith('-n') else (len(verb_relevance) + len(noun_relevance)) if total_rels == 0: total_rels = 1 relevance = sum(adj_relevance) + sum(verb_relevance) + sum(noun_relevance) #relevance = (sum(adj_relevance) + sum(noun_relevance)) if seed.endswith('-n') else (sum(verb_relevance) + sum(noun_relevance)) print("relevance: {}".format(relevance)) score = score_sentence(sentence.replace(',', ''), normalized=normalized) print("score: {}".format(score)) interpolation = ((relevance / total_rels) * 0.01) + score print("interpolation: {}".format(interpolation)) return interpolation raw_rank = sorted(story, key=lambda x: sent_heuristic(x), reverse=True)[:20] for s in raw_rank: print(seed, s) #raw_input() norm_rank = sorted(story, key=lambda x: sent_heuristic(x, normalized=True), reverse=True)[:10] for s in norm_rank: print(seed, s) #raw_input() reranked_story = list(set(raw_rank + norm_rank)) random.shuffle(reranked_story) original = '. '.join(s.lower() for s in story if all(c.isalpha() or c in ' ,' for c in s)) + '.' reranked = '. '.join(s.lower() for s in reranked_story if all(c.isalpha() or c in ' ,' for c in s)) + '.' return reranked
def censored_terms(sent): return [w for w in sent if any(w.pos.startswith(x) for x in ('NN', 'JJ', 'RB', 'VB')) \ and not any(self.concepts.contains(lemma(w.string), t) for t in (Concept.THING, Concept.ACTION, Concept.DESCRIPTOR)) ]
def convert_words(v): return '_'.join( lemma(w.string) for w in v.words if w.pos != "DT" and not w.pos.startswith('PRP') and w.string.isalpha()).replace('-', '_')
def convert_words(v): return ' '.join( lemma(w.string) if w.pos.startswith('VB') else w.string for w in v.words if lemma(w.string) != "be" and w.string.lower() not in ('to', 'wo') and w.string.isalpha())
def exploration_log(self, type="COMPUTER"): """ Return a string of the exploration log for this article. """ if type == "COMPUTER": def step_to_computer_verbs(string): parse = parsetree(string, relations=True)[0] def convert_words(v): return ' '.join( lemma(w.string) if w.pos.startswith('VB') else w.string for w in v.words if lemma(w.string) != "be" and w.string.lower() not in ('to', 'wo') and w.string.isalpha()) chunks = [convert_words(v) for v in parse.verbs] return ' -- '.join(c for c in chunks if len(c) > 0).upper() def step_to_computer_nouns(string): parse = parsetree(string, relations=True)[0] def convert_words(v): return '_'.join( lemma(w.string) for w in v.words if w.pos != "DT" and not w.pos.startswith('PRP') and w.string.isalpha()).replace('-', '_') chunks = [ convert_words(v) for v in parse.chunk if v.pos == 'NP' ] return ' | '.join(c for c in chunks if len(c) > 0).upper() def step_to_computer_adjs(string): try: parse = parsetree(string, relations=True)[0] return ' '.join(a.string for a in parse.adjectives if a.string.isalpha()) except IndexError: return '' verbs = [step_to_computer_verbs(s.main) for s in self.steps] verbs = [v if len(v) > 0 else '???' for v in verbs] nouns = [step_to_computer_nouns(s.main) for s in self.steps] nouns = [n if len(n) > 0 else '***' for n in nouns] adjs = [ ' '.join( set(' '.join((step_to_computer_adjs(s.main), step_to_computer_adjs(s.extra))).split())) for s in self.steps ] adjs = [a.lower() if len(a) > 0 else '!null' for a in adjs] lines = [ '{:02d}... {} ( {} ) => {}'.format(i, v, n, a) for i, v, n, a in zip(range(len(verbs)), verbs, nouns, adjs) ] title_words = self.title.lower().strip()[7:].split() title_words = [ w for w in title_words if w not in ("your", "a", "an", "the") ] if title_words[0].lower() != "not": title_words[0] = conjugate(lemma(title_words[0]), 'part').upper() else: title_words[1] = conjugate(lemma(title_words[1]), 'part').upper() title = "{0:^80}".format("- " + ' '.join(title_words) + " -").upper() fixed_lines = [ textwrap.fill(textwrap.dedent(line).strip(), initial_indent='', subsequent_indent=' ', width=76) for line in lines ] return '\n\n'.join([title] + fixed_lines) else: raise NotImplementedError( "Type {} of logging not implemented!".format(type))