def train_n_invests_for_name(self,
corp_names: list,
invest_only: bool = False) -> None:
"""회사이름으로 검색하여 학습시킴 """
corp = Corp()
comp_rmses = []
no = 1
date = None
for corp_name in corp_names:
corp_code = corp.get_corp_code(corp_name)
try:
result, invest_daily = self.train_n_invest(
corp_code, corp_name, no, invest_only)
date = result[1]
except Exception:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type,
exc_value,
exc_traceback,
file=sys.stdout)
no += 1
continue
#result = self.let_train_invest(corp_code, corp_name, no)
comp_rmses.append(result)
no += 1
df_comp_rmses = pd.DataFrame(comp_rmses, columns=self.result_columns)
DataUtils.save_csv(df_comp_rmses, self.get_result_file_path(date))
def let_train_invests_for_name(self, corp_names):
"""회사이름으로 검색하여 학습시킴 """
corp = Corp()
comp_rmses = []
no = 1
for corp_name in corp_names:
corp_code = corp.get_corp_code(corp_name)
result = self.let_train_invest(corp_code, corp_name, no)
comp_rmses.append(result)
no += 1
df_comp_rmses = pd.DataFrame(comp_rmses, columns=self.result_columns)
DataUtils.save_excel(df_comp_rmses, self.get_result_file_path())
verbose_eval=10)
print('Plotting metrics recorded during training...')
ax = lgb.plot_metric(evals_result, metric='l1')
plt.show()
print('Plotting feature importances...')
ax = lgb.plot_importance(gbm, max_num_features=10)
plt.show()
print('Plotting 84th tree...') # one tree use categorical feature to split
ax = lgb.plot_tree(gbm, tree_index=83, figsize=(20, 8), show_info=['split_gain'])
plt.show()
print('Plotting 84th tree with graphviz...')
graph = lgb.create_tree_digraph(gbm, tree_index=83, name='Tree84')
graph.render(view=True)
if __name__ == '__main__':
corp_name = "카카오"
corp = Corp()
corp_code = corp.get_corp_code(corp_name)
learning = LearningPlainModels('light_gbm')
learning.train(corp_code, corp_name, 1)
# from sklearn.datasets import load_iris
#
# iris = load_iris()
# X = iris.data[:, [2, 3]]
# y = iris.target
# print(X, y)