def print_return_by_top_n(self, df_list, cnt=None):
predict_util = prdct.PredictUtil()
for n in range(TOP_N):
if cnt is not None:
print(str(n) + ': ' + str(len(df_list[n])) + ' / ' + str(cnt) + ' = ' + str(len(df_list[n]) / cnt))
if len(df_list[n]) != 0:
df_all = pd.concat(df_list[n], axis=0)
predict_util.print_return(df_all, n + 1)
t_util_4.clf = joblib.load(clf_pkl_4)
t_util_4.sc = joblib.load(sc_pkl_4)
t_util_6.clf = joblib.load(clf_pkl_6)
t_util_6.sc = joblib.load(sc_pkl_6)
else:
print('pklファイルが存在しません。')
sys.exit()
ht_util = ht.HorseTableUtil()
test_race_keys = ht_util.get_race_keys_except_debut_after_ymd(ymd)
if len(test_race_keys) == 0:
print('入力日に対象レースはありません。')
sys.exit()
horse_table_list = t_util_4.get_horse_table_list(test_race_keys)
predict_util = prdct.PredictUtil()
df_list = []
cnt = 0
for n in range(train.TOP_N):
df_list.append([])
for horse_table in horse_table_list:
horse_table.predict_4_6(t_util_4.clf, t_util_4.sc, t_util_6.clf,
t_util_6.sc)
for n in range(train.TOP_N):
pr = horse_table.get_predict_return(n + 1)
if pr is not None:
df_list[n].append(pr)
cnt += 1
for n in range(train.TOP_N):