def gen_label_by_followup(min_range_day_CHD=0,
max_range_day_CHD=36500,
min_range_day_Non_CHD=120,
max_range_day_Non_CHD=36500,
step_CHD=120,
step_Non_CHD=120,
version='v1'):
followup = ReadHistData(info='followup', kind='train')
df = followup.groupby('ID').apply(lambda df_person: gen_label_person(
df_person=df_person,
min_range_day_CHD=min_range_day_CHD,
max_range_day_CHD=max_range_day_CHD,
min_range_day_Non_CHD=min_range_day_Non_CHD,
max_range_day_Non_CHD=max_range_day_Non_CHD,
step_CHD=step_CHD,
step_Non_CHD=step_Non_CHD,
version=version)).to_frame('labels').reset_index()
labels = list(df['labels'].values)
label_list = []
for label in labels:
if len(label) > 0:
label_list += label
df_labels = pd.DataFrame(label_list,
columns=['ID', 'TimePoint', 'version', 'label'])
params_dict = {
'min_range_day_CHD': min_range_day_CHD,
'max_range_day_CHD': max_range_day_CHD,
'min_range_day_Non_CHD': min_range_day_Non_CHD,
'max_range_day_Non_CHD': max_range_day_Non_CHD,
'step_CHD': step_CHD,
'step_Non_CHD': step_Non_CHD,
'version': version
}
SaveLabelsData(df_labels, params_dict, kind='train')
return df_labels
def gen_base_feature2(version, kind='train'):
# 1 读取历史数据
followup = ReadHistData(info='followup_person_info', version=version, kind=kind)
labels = ReadLabelsData(version, kind)
# 2 第一次随访的age
labels = labels.merge(followup.groupby('ID')['DATE_OF_BIRTH'].max().reset_index(), on='ID', how='left')
first_followup_time_df = followup.groupby('ID')['FOLLOWUP_DATE'].min().to_frame('first_followup_time').reset_index()
labels = labels.merge(first_followup_time_df, on='ID', how='left')
labels['first_followup_age'] = (labels['first_followup_time'] - labels['DATE_OF_BIRTH']).dt.days
SaveFeature(labels, 'first_followup_age', version, kind)
# 3 第一次随访到确认高血压的时间
labels = labels.merge(followup.groupby('ID')['CONFIRM_DATE'].max().reset_index(), on='ID', how='left')
labels['first_followup_time_diff_confirm_time'] = (labels['first_followup_time'] - labels['CONFIRM_DATE']).dt.days
SaveFeature(labels, 'first_followup_time_diff_confirm_time', version, kind)
# 4 当前时间到随访的时间
labels['TimePoint_diff_first_followup_time'] = (labels['TimePoint'] - labels['first_followup_time']).dt.days
SaveFeature(labels, 'TimePoint_diff_first_followup_time', version, kind)
def gen_base_feature(version, kind='train'):
"""
计算年龄、性别、确认时长
:param kind:
:return:
"""
# 1 读取历史数据
followup = ReadHistData(info='followup_person_info', version=version, kind=kind)
labels = ReadLabelsData(version, kind)
# 1 性别
labels = labels.merge(followup.groupby('ID')['SEX_CODE'].max().to_frame('SEX_CODE').reset_index(), on=['ID'], how='left')
SaveFeature(labels, 'SEX_CODE', version, kind)
# 2 在时间点的年龄
labels['age'] = labels.apply(lambda label: compute_age(filter_hist_data(label, followup), label), axis=1)
SaveFeature(labels, 'age', version, kind)
# 3 确认高血压时的年龄
labels = labels.merge(
followup.groupby('ID').apply(lambda df_person:
(df_person['CONFIRM_DATE'].max() - df_person['DATE_OF_BIRTH'].max()).days).to_frame('confirm_age').reset_index(), on=['ID'], how='left')
SaveFeature(labels, 'confirm_age', version, kind)
# 4 时间点与确认高血压时间的差
labels['time_diff_confirm_2TimePoint'] = (labels['age'] - labels['confirm_age'])
SaveFeature(labels, 'time_diff_confirm_2TimePoint', version, kind)