def __is_no_idea(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
noun_list = Nlp_util.make_noun_list(df)
verb_list = Nlp_util.make_verb_list(df, type="normal")
is_subj_himself = Nlp_util.is_first_subject_in(["i"], noun_list,
verb_list)
exist_subj_for_first_verb = Nlp_util.exist_subj_for_first_verb(
noun_list, verb_list)
is_idk_what_to_do = Df_util.anything_isin(
{"do not know", "not sure", "have no idea"},
df_ex_adverb["base_form"]) and Df_util.anything_isin(
{"what to do", "how to do", "what to deal", "how to deal"},
df_ex_adverb["base_form"])
is_want_advice = Df_util.anything_isin(
{"want", "need", "give me"},
df_ex_adverb["base_form"]) and Df_util.anything_isin(
{"advice", "suggestion"}, df_ex_adverb["word"])
is_give_me_advice = Df_util.anything_isin(
{"need", "want", "give me"},
df_ex_adverb["base_form"]) and Df_util.anything_isin(
{"advice", "suggestion"}, df_ex_adverb["word"])
what_should_i_do = Nlp_util.are_words1_words2_words3_in_order(
df_ex_adverb, ["what"], ["should"], ["i"])
return (is_subj_himself and (is_idk_what_to_do or is_want_advice)) or (
not exist_subj_for_first_verb
and is_give_me_advice) or what_should_i_do
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 __exists_SV_around_cc(cls, df, cc_in_message, cc):
try:
sent_with_cc = df[df.sidx == cc.sidx]
nouns_in_sent = Nlp_util.make_noun_list(sent_with_cc)
verbs_in_sent = Nlp_util.make_verb_list(sent_with_cc, type="normal")
first_half_of_sent = sent_with_cc[sent_with_cc.widx < cc.widx]
nouns_in_first_half = nouns_in_sent[(nouns_in_sent.sidx == cc.sidx) & (nouns_in_sent.widx < cc.widx)]
verbs_in_first_half = verbs_in_sent[(verbs_in_sent.sidx == cc.sidx) & (verbs_in_sent.widx < cc.widx)]
other_cc_sidx = cc_in_message[(cc_in_message.sidx == cc.sidx) & (cc_in_message.widx > cc.widx)].sidx
exists_other_cc_in_same_sentence = any(i == cc.sidx for i in other_cc_sidx)
if exists_other_cc_in_same_sentence:
other_cc_widx = cc_in_message[cc_in_message.index > cc.widx].index[0]
else:
other_cc_widx = len(sent_with_cc)
second_half_of_sent = sent_with_cc[(sent_with_cc.widx >= cc.widx) & (sent_with_cc.widx < other_cc_widx)]
if cls.__is_finishing_with_question_mark(second_half_of_sent):
second_half_of_sent = second_half_of_sent.iloc[:-1]
nouns_in_second_half = cls.__get_second_half_words(nouns_in_sent, cc, other_cc_widx)
verbs_in_second_half = cls.__get_second_half_words(verbs_in_sent, cc, other_cc_widx)
return cls.__is_complete_sent(nouns_in_first_half, verbs_in_first_half, first_half_of_sent) and \
cls.__is_complete_sent(nouns_in_second_half, verbs_in_second_half, second_half_of_sent)
except:
logging.exception('')
return False
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 __replace_verb(text_df):
text_df.loc[:, "word"] = text_df.apply(
lambda row: Nlp_util.adjust_be_verb_for_changed_subject(
row["word"], row["pos"], row.name,
Nlp_util.make_noun_list(text_df)),
axis=1)
return text_df
def __has_wish_S_V(target_df):
if Df_util.anything_isin(["wish"], target_df["base_form"]):
wish_idx = Nlp_util.get_idx_list_of_word("wish", target_df["base_form"])[0]
if Nlp_util.are_words1_words2_words3_in_order(target_df.loc[wish_idx:,:], Nlp_util.pos_NOUNs+Nlp_util.pos_PRPs, Nlp_util.pos_VERBs, df_column="pos"):
return True
else:
return False
else:
return False
def __is_complaint_or_dissing(df):
noun_list = Nlp_util.make_noun_list(df)
verb_list = Nlp_util.make_verb_list(df, type="normal")
said_you_dont_listen = Nlp_util.is_first_subject_in(
["you"], noun_list, verb_list) and Df_util.anything_isin(
["not listen", "never listen"], df["base_form"])
is_dissing = Df_util.anything_isin(["f**k you", "hate you"],
df["base_form"])
return said_you_dont_listen or is_dissing
def __mean_no_friends(cls, df):
exists_nobody_likes_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["nobody", "no one"], ["like", "love"], ["me"])
exists_friends_dont_like_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["friend", "they", "everybody"],
["not like", "not love", "hate"], ["me"])
exists_have_no_friend = Nlp_util.are_words1_words2_words3_in_order(
df, ["i"], ["not have", "have no"], ["friend"])
return exists_nobody_likes_me or exists_friends_dont_like_me or exists_have_no_friend
def __is_jullie_useless(df):
if df is None:
return False
c1 = Nlp_util.are_words1_words2_words3_in_order(
df, ["you", "this"], ["be not", "be never"],
["helpful", "help", "helping"])
c2 = Nlp_util.are_words1_words2_words3_in_order(
df, ["you"], ["be"], ['useless', 'helpless'])
return c1 or c2
def __is_anxious(cls, df_by_sentence: DataFrame):
try:
word1_1 = ['anxious']
word1_2 = ['be', 'being', 'am', 'was', 'been', 'feel', 'feeling']
word2_1 = ['anxiety']
word2_2 = ['have', 'having', 'had', 'feel', 'feeling']
return Nlp_util.are_words1_words2_words3_in_order(df_by_sentence, word1_1, word1_2) \
or Nlp_util.are_words1_words2_words3_in_order(df_by_sentence, word2_1, word2_2)
except:
logging.exception('')
return False
def __is_anxious(cls, df_by_sentence: DataFrame):
word1_1 = ['anxious']
word1_2 = ['be', 'being', 'am', 'was', 'been', 'feel', 'feeling']
word2_1 = ['anxiety']
word2_2 = ['have', 'having', 'had', 'feel', 'feeling']
pattern_1 = Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, word1_1, word1_2)
pattern_2 = Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, word2_1, word2_2)
return pattern_1 or pattern_2
def __is_jullie_useless(df):
try:
if df is None:
return False
is_useless = Nlp_util.are_words1_words2_words3_in_order(
df, ["you", "this"], ["be not", "be never"],
["helpful", "help", "helping"
]) or Nlp_util.are_words1_words2_words3_in_order(
df, ["you"], ["be"], ['useless', 'helpless'])
return is_useless
except:
logging.exception('')
return False
def cut_sent_by_interjection(cls, df):
try:
separators = [",", "and", "but", "or", "then", "so", "plus", "cause", "because"]
exists_separator = True
while exists_separator:
separators_in_message = df[(df.word.isin(separators)) & (df.widx != 0)]
if separators_in_message.empty:
exists_separator = False
else:
for idx, separator in separators_in_message.iterrows():
if separator.word == 'so' and separator.pos == 'RB':
continue
elif separator.word == 'cause' and separator.pos != 'VB':
continue
if cls.__exists_SV_around_cc(df, separators_in_message, separator):
df.loc[df[(df.sidx >= separator.sidx) & (df.index >= idx)].index, "sidx"] += 1
df = Nlp_util.reset_widx(df)
break
else:
exists_separator = False
return df
except:
logging.exception('')
return df
def __like_someone(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
target_list = [{
"subjects": ["guy", "friend", "boy", "man"],
"word1": ["i"],
"word2": ["like"],
"exceptions": []
}, {
"subjects": ["i"],
"word1": ["like"],
"word2": ["guy", "friend", "boy", "man"],
"exceptions": ["feel like"]
}, {
"subjects": ["i"],
"word1": ["be", "fall"],
"word2": ["in love"],
"exceptions": []
}]
return any(
Nlp_util.are_words1_words2_words3_in_order(
df_ex_adverb,
target["subjects"],
target["word1"],
target["word2"],
exception_list=target["exceptions"]) for target in target_list)
def __create_response_for_what_to_V(df):
df_after_what_to = df.loc[Nlp_util.get_idx_list_of_idiom_list(
["what to", "how to"], df["base_form"])[0] + 2:, :]
words_after_what_to = WordFormatter.Df2Str(df_after_what_to)
cmp = [
["it must be not easy to find how to" + words_after_what_to],
["now you are looking for the way to" + words_after_what_to],
["should be not that easy to find how to" + words_after_what_to],
]
encourage = [
[
"but i am sure that thinking about it and speaking out it helps you🤗"
],
[
"eventho its always tough to find the right way, you try to figure it out. Thats impressing me😊"
],
[
"plz let me know any idea comes to your mind now. it might help you figuring it out☺️"
],
[
"tell me if you have any little idea. It could help you finding ur way😊"
],
]
random_idx_for_cmp = randint(0, len(cmp) - 1)
random_idx_for_encourage = randint(0, len(encourage) - 1)
return cmp[random_idx_for_cmp] + encourage[random_idx_for_encourage]
def __is_call_me_names(cls, df_by_sentence):
word1 = ['call']
word2 = ['me']
word3 = ['names']
return Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, word1, word2, word3)
def __is_about_money(cls, df_by_sentence: DataFrame):
money_words = ['money', 'finance', 'financial', 'financially']
if any(i in df_by_sentence.word.values for i in money_words):
prp = ['they', 'he', 'she']
have = ['have']
if Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, prp, have, money_words):
return False
else:
return True
elif Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, ['i'], ['poor']):
return True
else:
return False
def cut_sent_by_unimportant_words_at_head(cls, df):
try:
fixed_df = df
while len(fixed_df) != 0:
is_fixed_df_modified = False
for sidx in set(fixed_df.sidx):
if len(fixed_df[fixed_df.sidx == sidx]) == 1:
continue
head_row = fixed_df[fixed_df.sidx == sidx].iloc[0]
head_word = head_row.word
if head_word in UNIMPORTANT_WORDS_FOR_REPEAT.word.values:
fixed_df.loc[fixed_df.index > head_row.name, "sidx"] += 1
fixed_df = Nlp_util.reset_widx(fixed_df)
is_fixed_df_modified = True
break
elif not head_word.isalpha():
if not head_word.isdigit():
fixed_df = cls.__remove_nums_n_symbols(fixed_df, sidx)
is_fixed_df_modified = True
break
if not is_fixed_df_modified:
break
return fixed_df
except:
logging.exception('')
return df
def __lack_of_confidence(cls, df):
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
target_list = [
[["i"], ["hate", "not like"], ["myself"]],
[["i"], ["be"],
["fat", "w***e", "s**t", "bitch", "stupid", "ugly", "burden"]],
[["i"], ["bother"], ["people", "everyone", "friends"]],
]
return any(
Nlp_util.are_words1_words2_words3_in_order(
df_ex_adverb,
target[0],
target[1],
target[2],
exception_list=["say", "tell"]) for target in
target_list) or Nlp_util.are_words1_words2_words3_in_order(
df, ["i"], ["be"], ["not good enough"])
def get_sidx_of_not_basic_svo_sent(text_df):
delete_sidx_list = []
for sidx in set(text_df.sidx.values):
df = text_df[text_df.sidx == sidx]
noun_list = Nlp_util.make_noun_list(df)
verb_list = Nlp_util.make_verb_list(df, type="normal")
# catch the case such as "Dont judge me"
if Nlp_util.is_any_verb_before_first_noun(noun_list, verb_list):
delete_sidx_list.append(sidx)
# catch the case such as "the situation horrible as like he said"
elif not Nlp_util.is_any_verb_for_first_noun(noun_list, verb_list):
delete_sidx_list.append(sidx)
else:
pass
return delete_sidx_list
def __alter_repeat_for_make_S_feel_ADJ(df):
idx_of_make = Nlp_util.get_idx_list_of_word_list(["make"],
df["base_form"])[0]
subj = Nlp_util.change_object_pronoun_to_pronoun(df.loc[idx_of_make +
1, "word"])
df_after_subj = df.loc[idx_of_make + 2:idx_of_make + 4, :]
adj = df_after_subj.loc[
df_after_subj["pos"].isin(Nlp_util.pos_ADJECTIVEs), "word"].iloc[0]
subj_adj_list = [subj, adj]
options = [
["{0[0]} feel {0[1]} because of that".format(subj_adj_list)],
["thats getting {0[0]} feel {0[1]}".format(subj_adj_list)],
["thats the moment {0[0]} feel {0[1]}".format(subj_adj_list)],
]
random_idx = randint(0, len(options) - 1)
return options[random_idx]
def __has_what_to_do(df):
try:
df_ex_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
return Nlp_util.are_words1_words2_words3_in_order(df_ex_adverb, ["i"], ["not know", "not sure"],
["what to", "how to"])
except:
logging.exception('')
return False
def __mean_no_friends(cls, df):
try:
exists_nobody_likes_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["nobody", "no one"], ["like", "love"], ["me"])
exists_friends_dont_like_me = Nlp_util.are_words1_words2_words3_in_order(
df, ["friend", "they", "everybody"],
["not like", "not love", "hate"], ["me"])
exists_have_no_friend = Nlp_util.are_words1_words2_words3_in_order(
df, ["i"], ["not have", "have no"], ["friend"])
if exists_nobody_likes_me or exists_friends_dont_like_me or exists_have_no_friend:
return True
else:
return False
except:
logging.exception('')
return False
def __exists_word_after_want_to(df):
try:
idx_of_i = Nlp_util.get_idx_list_of_idiom("want to", df.word)[0]
length_after_want_to = len(df.loc[idx_of_i + 2, :]) if len(df) >= idx_of_i + 3 else 0
return length_after_want_to > 2
except:
logging.exception('')
return False
def __exists_want_to(cls, df):
df_without_adverb = df[~df.pos.isin(Nlp_util.pos_ADVERBs)]
noun_list = Nlp_util.make_noun_list(df)
verb_list = Nlp_util.make_verb_list(df, type="basic")
idx_of_i_wanna = Nlp_util.get_idx_list_of_idiom(
"i want to", df_without_adverb.base_form)
if len(idx_of_i_wanna) != 0 and len(
df.loc[idx_of_i_wanna[0] + 2:, :]) > 1:
if cls.__exists_word_after_want_to(
df) and Nlp_util.is_first_subject_in({"i"}, noun_list,
verb_list):
return True
else:
return False
else:
return False
def __has_say_plus_bad_word(df):
if any([
Nlp_util.are_words1_words2_words3_in_order(
df, ["say", "tell"], ["i be", "i look"], [negative_word])
for negative_word in KWDF[KWDF['Type'] ==
'n'].keyword.tolist()
]):
return True
elif any([
Nlp_util.are_words1_words2_words3_in_order(
df, ["say", "tell"], ["i be not", "i do not look"],
[positive_word]) for positive_word in KWDF[
KWDF['Type'] == 'p'].keyword.tolist()
]):
return True
else:
return False
def __remove_nums_n_symbols(cls, fixed_df, sidx):
try:
fixed_df = fixed_df.drop(fixed_df[fixed_df.sidx == sidx].iloc[0].name)
fixed_df = fixed_df.reset_index(drop=True)
fixed_df.loc[fixed_df.sidx == sidx] = Nlp_util.reset_widx(fixed_df.loc[fixed_df.sidx == sidx])
return fixed_df
except:
logging.exception('')
return fixed_df
def __is_about_missing(cls, df_by_sentence):
try:
phrase1 = ['i']
phrase2 = ['miss']
return Nlp_util.are_words1_words2_words3_in_order(
df_by_sentence, phrase1, phrase2)
except:
logging.exception('')
return False
def __has_nobody_V(df):
idx_list_of_nobody = Nlp_util.get_idx_list_of_word(
"nobody", df["base_form"])
if len(idx_list_of_nobody) == 0:
return False
else:
if any(df.loc[idx_list_of_nobody[0]:,
"pos"].isin(Nlp_util.pos_VERBs)):
return True
else:
return False
