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 __correct_pos_tag(df, dic):
if any(df["word"].isin(dic.keys())):
df.loc[df["word"].isin(dic.keys()), "pos"] = df[df["word"].isin(
dic.keys())].apply(lambda row: dic[row["word"]], axis=1)
if any(df["word"].isin(["that", "it", "this"])):
idx_list_of_kws = Nlp_util.get_idx_list_of_word_list(
["that", "it", "this"], df["word"])
for idx_of_kw in idx_list_of_kws:
condition = df.loc[
idx_of_kw + 1,
"pos"] not in Nlp_util.pos_PRPs + Nlp_util.pos_NOUNs
if idx_of_kw == len(df) - 1 or condition:
df.loc[idx_of_kw, "pos"] = "NN"
else:
pass
if Df_util.anything_isin(["like", "care", "guess", "need"],
df["word"]):
idx_list_of_like = Nlp_util.get_idx_list_of_word_list(
["like", "care", "guess", "need"], df["word"])
for idx_of_like in idx_list_of_like:
if not idx_of_like == 0 and df.loc[
idx_of_like - 1,
"pos"] in Nlp_util.pos_NOUNs + Nlp_util.pos_PRPs:
df.loc[idx_of_like, "pos"] = "VB"
else:
pass
if Df_util.anything_isin(["work"], df["word"]):
idx_list_of_work = Nlp_util.get_idx_list_of_word_list(["work"],
df["word"])
for idx_of_work in idx_list_of_work:
if not idx_of_work == 0 and df.loc[idx_of_work - 1,
"word"] in ["this"]:
df.loc[idx_of_work, "pos"] = "VB"
else:
pass
return df
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 __exists_make_S_feel_ADJ(df):
try:
idx_list_of_make = Nlp_util.get_idx_list_of_word_list(["make"], df["base_form"])
if len(idx_list_of_make) == 0:
return False
else:
is_after_make_prp = df.loc[idx_list_of_make[0] + 1, "pos"] in Nlp_util.pos_PRPs
if is_after_make_prp:
is_after_prp_adj = df.loc[idx_list_of_make[0] + 2, "pos"] in Nlp_util.pos_ADJECTIVEs or (
df.loc[idx_list_of_make[0] + 2, "base_form"] == "feel" and any(
df.loc[idx_list_of_make[0] + 2:idx_list_of_make[0] + 4, "pos"].isin(
Nlp_util.pos_ADJECTIVEs)))
return is_after_prp_adj
else:
return False
except:
logging.exception('')
return False