def __alter_repeat_for_dont_think_SV(fixed_df):
try:
# TODO see if its neccesary to care about should and cant
idx_of_think = Nlp_util.get_idx_list_of_word("think", fixed_df["base_form"])[0]
df_after_think = fixed_df.loc[idx_of_think + 1:, :].reset_index(drop=True)
verb_list = Nlp_util.make_verb_list(df_after_think, type="normal")
noun_list = Nlp_util.make_noun_list(df_after_think)
# possibly bug happen here since amount of verbs are different in cant do/dont do
is_negative_form = Df_util.anything_isin(["not", "never"], df_after_think.loc[:, "base_form"])
# can add possibly or likely(when its negative)
head_words = ["so ", "so probably ", "probably ", "so maybe ", "maybe "]
random_idx_for_heads_words = randint(0, len(head_words) - 1)
if is_negative_form:
# まず主語とるそのあとにwouldntいれるその後ろに動詞の原型をいれて、それ以降はつづける
head_word = head_words[random_idx_for_heads_words]
subj = noun_list["word"].iloc[0]
auxiliary_verb = " would "
idx_of_not = Nlp_util.get_idx_list_of_word_list(["not", "never"], df_after_think.loc[:, "base_form"])[0]
verb_row = verb_list.loc[idx_of_not:, :].iloc[0]
verb = verb_row.base_form + " "
after_verb = WordFormatter.Series2Str(df_after_think.loc[verb_row.name + 1:, "word"])
return [head_word + subj + auxiliary_verb + verb + after_verb]
else:
head_word = head_words[random_idx_for_heads_words]
subj = noun_list["word"].iloc[0]
auxiliary_verb = " wouldnt "
verb = verb_list["base_form"].iloc[0] + " "
after_verb = WordFormatter.Series2Str(df_after_think.loc[verb_list.index[0] + 1:, "word"])
return [head_word + subj + auxiliary_verb + verb + after_verb]
except:
logging.exception('')
return []
def __alter_repeat_for_need_sent(fixed_df):
idx_of_need = Nlp_util.get_idx_list_of_word("need",
fixed_df["base_form"])[0]
row_of_first_noun = \
Nlp_util.get_wordsDF_of_wordlist_after_idx(fixed_df, Nlp_util.pos_NOUNs + Nlp_util.pos_PRPs,
idx_of_need,
column_name="pos").iloc[0]
if fixed_df.loc[row_of_first_noun.name - 1,
"pos"] in Nlp_util.pos_ADJECTIVEs + ["PRP$", "DT"]:
noun = WordFormatter.Series2Str(
fixed_df.loc[row_of_first_noun.name - 1:row_of_first_noun.name,
"word"])
else:
noun = fixed_df.loc[row_of_first_noun.name, "word"]
noun_nominative = Nlp_util.convert_objective_noun_to_nominative(noun)
options = [[
"so " + noun_nominative + " is very important thing for you..",
"and sounds its kinda hard to get it now right😢"
],
[
"so its like its not easy to get " + noun +
" now but you really want..",
"and it can frustrate you😞"
],
[
"sounds you really want " + noun + "..",
"might be tough time for you to seek for it now😓"
]]
random_idx_for_options = randint(0, len(options) - 1)
return options[random_idx_for_options]
def __alter_repeat_for_wish(fixed_df):
wish_idx = Nlp_util.get_idx_list_of_word("wish", fixed_df["base_form"])[0]
row_of_subj = Nlp_util.get_wordsDF_of_wordlist_after_idx(fixed_df, Nlp_util.pos_NOUNs+Nlp_util.pos_PRPs, wish_idx, column_name="pos").iloc[0]
row_of_verb = Nlp_util.get_wordsDF_of_wordlist_after_idx(fixed_df, Nlp_util.pos_VERBs, row_of_subj.name, column_name="pos").iloc[0]
subj = row_of_subj.word
verb = row_of_verb.word
after_verb = WordFormatter.Series2Str(fixed_df.loc[row_of_verb.name+1:, "word"])
objective_subj = Nlp_util.convert_nominative_noun_to_objective(subj)
if subj == "you":
repeat_list = [
["you really want to "+verb+" "+after_verb],
["so you seriously hope to "+verb+" "+after_verb],
["so you are dying to "+verb+" "+after_verb]
]
else:
repeat_list = [
["you really want "+objective_subj+" to "+verb+" "+after_verb],
["you really wanna have "+objective_subj+" "+verb+" "+after_verb],
["you really wanna make "+objective_subj+" "+verb+" "+after_verb]
]
cmp_list = [
["but sounds you feel bit too much to expect that now..?"],
["and sounds you feel like its impossible..?"],
["and seems like you dont think it never happen😓"]
]
random_idx_for_repeat_list = randint(0, len(repeat_list) - 1)
random_idx_for_cmp_list = randint(0, len(cmp_list) - 1)
return repeat_list[random_idx_for_repeat_list]+cmp_list[random_idx_for_cmp_list]
def get_idx_list_of_idiom(idiom, series):
tokenized_word = nltk.word_tokenize(idiom)
ngram_list_of_the_series = Nlp_util.create_ngrams(
WordFormatter.Series2Str(series), len(tokenized_word))
return [
idx for idx, ngram in enumerate(ngram_list_of_the_series)
if ngram == tokenized_word
]
def __get_sidx_of_normal_and_too_long_sent(cls, df):
delete_sidx_list = []
for sidx in set(df.sidx.values):
target_df = df[df.sidx == sidx].copy().reset_index(drop=True)
if cls.__is_special_type(target_df):
pass
else:
if len(WordFormatter.Series2Str(target_df.word)) > 75:
delete_sidx_list.append(sidx)
else:
pass
return delete_sidx_list
def anything_isin(word_list, series):
for word in word_list:
tokenized_word = nltk.word_tokenize(word)
if len(tokenized_word) == 1:
if any(series.isin(word_list)):
return True
else:
pass
else:
ngram_list_of_the_series = Nlp_util.create_ngrams(
WordFormatter.Series2Str(series), len(tokenized_word))
if tokenized_word in ngram_list_of_the_series:
return True
else:
pass
return False
