def OneHotOneColumn(self, data_container, feature_list):
info = data_container.GetFrame()
feature_name = data_container.GetFeatureName()
for feature in feature_list:
assert (feature in feature_name)
new_info = pd.get_dummies(info, columns=feature_list)
new_data = DataContainer()
new_data.SetFrame(new_info)
return new_data
def _MergeClinical(self, dc, cli_df):
# Merge DataContainer and a dataframe of clinical
if 'label' in cli_df.columns.tolist():
del cli_df['label']
elif 'Label' in cli_df.columns.tolist():
del cli_df['Label']
df = pd.merge(dc.GetFrame(), cli_df, how='left', left_index=True, right_index=True)
merge_dc = DataContainer()
merge_dc.SetFrame(df)
merge_dc.UpdateFrameByData()
return merge_dc
def Run(self, data_container, store_folder='', store_key=''):
temp_frame = data_container.GetFrame().select_dtypes(include=None, exclude=['object'])
new_data_container = DataContainer()
new_data_container.SetFrame(temp_frame)
if store_folder and os.path.isdir(store_folder):
feature_store_path = os.path.join(store_folder, 'numeric_feature.csv')
featureinfo_store_path = os.path.join(store_folder, 'feature_select_info.csv')
new_data_container.Save(feature_store_path)
SaveSelectInfo(new_data_container.GetFeatureName(), featureinfo_store_path, is_merge=False)
return new_data_container
