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feature_extraction.py
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feature_extraction.py
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# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： main
Description :
Author : joleo
date： 2018/5/8 0008
-------------------------------------------------
Change Activity:
2018/5/8 0008:
-------------------------------------------------
"""
__author__ = 'joleo'
import pandas as pd
import numpy as np
import datetime
import time
from math import radians, cos, sin, asin, sqrt
class LYFeatureExtraction(object):
def haversine1(self, lon1, lat1, lon2, lat2): # 经度1，纬度1，经度2，纬度2 （十进制度数）
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
"""
# 将十进制度数转化为弧度
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine公式
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
c = 2 * asin(sqrt(a))
r = 6371 # 地球平均半径，单位为公里
return c * r * 1000
def timestamp_datetime(self, value):
fmt = '%Y-%m-%d %H:%M:%S'
value = time.localtime(value)
dt = time.strftime(fmt, value)
return dt
# def modify_y(self, y):
# if y>0 and y<0.1: return y+0.91
# elif y<0.2: return y+0.81
# elif y<0.3:
def get_user_Y(self, data):
"""
获取用户Y值
:param data:
:return:
"""
user_id = data.groupby(['TERMINALNO'], as_index = False)['Y'].first()
return user_id
def user_driver_time(self, data):
"""
用户时间特征特征
"""
user_time_data = data.loc[:, ['TERMINALNO', 'TRIP_ID', 'TIME','HEIGHT','DIRECTION','SPEED']]
del data
user_time_data['hour'] = user_time_data['TIME'].map(lambda x: datetime.datetime.fromtimestamp(x).hour)
user_time_per = user_time_data.groupby('TERMINALNO', as_index=False)['TRIP_ID'].count()
user_time_data.loc[:, 'hour_5'] = user_time_data.hour.map(lambda x: 1 if x == 5 else 0)
user_time_data.loc[:, 'hour_6'] = user_time_data.hour.map(lambda x: 1 if x == 6 else 0)
user_time_data.loc[:, 'hour_10'] = user_time_data.hour.map(lambda x: 1 if x == 10 else 0)
user_time_data.loc[:, 'hour_15'] = user_time_data.hour.map(lambda x: 1 if x == 15 else 0)
user_time_data.loc[:, 'hour_7'] = user_time_data.hour.map(lambda x: 1 if x == 7 else 0)
user_time_data.loc[:, 'hour_8'] = user_time_data.hour.map(lambda x: 1 if x == 8 else 0)
user_time_data.loc[:, 'hour_9'] = user_time_data.hour.map(lambda x: 1 if x == 9 else 0)
user_time_data.loc[:, 'hour_17'] = user_time_data.hour.map(lambda x: 1 if x == 17 else 0)
user_time_data.loc[:, 'hour_18'] = user_time_data.hour.map(lambda x: 1 if x == 18 else 0)
user_time_data.loc[:, 'hour_11'] = user_time_data.hour.map(lambda x: 1 if x == 11 else 0)
user_time_data.loc[:, 'hour_12'] = user_time_data.hour.map(lambda x: 1 if x == 12 else 0)
user_time_data.loc[:, 'hour_13'] = user_time_data.hour.map(lambda x: 1 if x == 13 else 0)
user_time_data.loc[:, 'hour_14'] = user_time_data.hour.map(lambda x: 1 if x == 14 else 0)
user_time_data.loc[:, 'hour_0'] = user_time_data.hour.map(lambda x: 1 if x == 0 else 0)
user_time_data.loc[:, 'hour_1'] = user_time_data.hour.map(lambda x: 1 if x == 1 else 0)
user_time_data.loc[:, 'hour_2'] = user_time_data.hour.map(lambda x: 1 if x == 2 else 0)
user_time_data.loc[:, 'hour_3'] = user_time_data.hour.map(lambda x: 1 if x == 3 else 0)
user_time_data.loc[:, 'hour_4'] = user_time_data.hour.map(lambda x: 1 if x == 4 else 0)
driver_time_colmns = ['hour_5', 'hour_6', 'hour_10', 'hour_15',
'hour_7', 'hour_8', 'hour_9', 'hour_17', 'hour_18',
'hour_11', 'hour_12', 'hour_13',
'hour_14', 'hour_0', 'hour_1', 'hour_2', 'hour_3',
'hour_4']
# 计算每个时间段的比例
driver_time_num = user_time_data.groupby(['TERMINALNO'], as_index=False)[driver_time_colmns].sum()
# user_time_data.loc[(user_time_data['hour'] >= 0) & (user_time_data['hour'] <= 6), 'hour_period'] = 1
# user_time_data.loc[(user_time_data['hour'] >= 18) & (user_time_data['hour'] <= 24), 'hour_period'] = 3
# user_period_std_height = user_time_data.groupby(['TERMINALNO','hour_period'])['HEIGHT'].agg(
# {'user_period_mean_height':'mean'}).reset_index()
#
# user_period_1_std_height = user_period_std_height[user_period_std_height['hour_period']==1].rename(columns={'user_period_std_height': 'user_period_1_std_height'})
# user_period_1_std_height.drop('hour_period', axis=1, inplace=True)
# user_period_3_std_height = user_period_std_height[user_period_std_height['hour_period']==3].rename(columns={'user_period_std_height': 'user_period_3_std_height'})
# user_period_3_std_height.drop('hour_period', axis=1, inplace=True)
#
# user_period_std_direc = user_time_data.groupby(['TERMINALNO', 'hour_period'])['DIRECTION'].agg(
# {'user_period_mean_direc': 'mean'}).reset_index()
# user_period_1_std_direc = user_period_std_direc[user_period_std_direc['hour_period'] == 1].rename(
# columns={'user_period_mean_direc': 'user_period_1_mean_direc'})
# user_period_1_std_direc.drop('hour_period', axis=1, inplace=True)
# user_period_3_std_direc = user_period_std_direc[user_period_std_direc['hour_period'] == 3].rename(
# columns={'user_period_mean_direc': 'user_period_3_mean_direc'})
# user_period_3_std_direc.drop('hour_period', axis=1, inplace=True)
# user_period_std_speed = user_time_data.groupby(['TERMINALNO', 'hour_period'])['SPEED'].agg(
# {'user_period_std_speed': 'std'}).reset_index()
# user_period_1_std_speed = user_period_std_speed[user_period_std_direc['hour_period'] == 1].rename(
# columns={'user_period_std_speed': 'user_period_1_std_speed'})
# user_period_1_std_speed.drop('hour_period', axis=1, inplace=True)
# user_period_3_std_speed = user_period_std_speed[user_period_std_direc['hour_period'] == 3].rename(
# columns={'user_period_std_speed': 'user_period_3_std_speed'})
# user_period_3_std_speed.drop('hour_period', axis=1, inplace=True)
# user_time_per = pd.merge(user_time_per, user_period_1_std_height, on="TERMINALNO", how="left")
# user_time_per = pd.merge(user_time_per, user_period_3_std_height, on="TERMINALNO", how="left")
# user_time_per = pd.merge(user_time_per, user_period_1_std_direc, on="TERMINALNO", how="left")
# user_time_per = pd.merge(user_time_per, user_period_3_std_direc, on="TERMINALNO", how="left")
# user_time_per = pd.merge(user_time_per, user_period_1_std_speed, on="TERMINALNO", how="left")
# user_time_per = pd.merge(user_time_per, user_period_3_std_speed, on="TERMINALNO", how="left")
del user_time_data
user_time_per.loc[:, 'hour_5_per'] = driver_time_num['hour_5'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_6_per'] = driver_time_num['hour_6'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_10_per'] = driver_time_num['hour_10'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_15_per'] = driver_time_num['hour_15'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_7_per'] = driver_time_num['hour_7'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_8_per'] = driver_time_num['hour_8'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_9_per'] = driver_time_num['hour_9'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_17_per'] = driver_time_num['hour_17'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_18_per'] = driver_time_num['hour_18'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_11_per'] = driver_time_num['hour_11'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_12_per'] = driver_time_num['hour_12'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_13_per'] = driver_time_num['hour_13'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_14_per'] = driver_time_num['hour_14'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_0_per'] = driver_time_num['hour_0'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_1_per'] = driver_time_num['hour_1'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_2_per'] = driver_time_num['hour_2'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_3_per'] = driver_time_num['hour_3'] / user_time_per['TRIP_ID']
user_time_per.loc[:, 'hour_4_per'] = driver_time_num['hour_4'] / user_time_per['TRIP_ID']
del driver_time_num
user_time_per.drop('TRIP_ID', axis=1, inplace=True)
return user_time_per
def user_night_stat(self, data):
"""
夜晚开车和每天用车时长的统计
"""
train = data.loc[:,['TERMINALNO','TIME','LONGITUDE','HEIGHT']]
del data
train['TIME'] = train['TIME'].apply(lambda x: self.timestamp_datetime(x), 1)
train['TIME'] = train['TIME'].apply(lambda x: str(x)[:13], 1)
train = train.sort_values(by=["TERMINALNO", 'TIME'])
train.index = range(len(train))
train['hour'] = train.TIME.apply(lambda x: str(x)[11:13], 1)
train['hour'] = train['hour'].astype(int)
train['hour_0'] = train.hour.apply(lambda x: 1 if x == 0 else 0, 1)
train['hour_1'] = train.hour.apply(lambda x: 1 if x == 1 else 0, 1)
train['hour_2'] = train.hour.apply(lambda x: 1 if x == 2 else 0, 1)
train['hour_3'] = train.hour.apply(lambda x: 1 if x == 3 else 0, 1)
train['hour_4'] = train.hour.apply(lambda x: 1 if x == 4 else 0, 1)
# train['hour_5'] = train.hour.apply(lambda x: 1 if x == 5 else 0, 1)
# train['hour_6'] = train.hour.apply(lambda x: 1 if x == 6 else 0, 1)
# train['hour_7'] = train.hour.apply(lambda x: 1 if x == 7 else 0, 1)
# train['hour_8'] = train.hour.apply(lambda x: 1 if x == 8 else 0, 1)
# train['hour_9'] = train.hour.apply(lambda x: 1 if x == 9 else 0, 1)
#
# train['hour_18'] = train.hour.apply(lambda x: 1 if x == 18 else 0, 1)
# train['hour_19'] = train.hour.apply(lambda x: 1 if x == 19 else 0, 1)
train['hour_21'] = train.hour.apply(lambda x: 1 if x == 21 else 0, 1)
train['hour_22'] = train.hour.apply(lambda x: 1 if x == 22 else 0, 1)
train['hour_23'] = train.hour.apply(lambda x: 1 if x == 23 else 0, 1)
train_hour = train.groupby(['TERMINALNO', 'TIME'], as_index=False).count()
train_hour.TIME = train_hour.TIME.apply(lambda x: str(x)[:10], 1)
train_day = train_hour.groupby(
['TERMINALNO', 'TIME'], as_index=False).count()
del train_hour
train_hour_count = train_day.groupby('TERMINALNO')['LONGITUDE'].agg(
{"hour_count_max": "max", "hour_count_mean": "mean", "hour_count_std": "std",
"hour_count_skew": "skew"}).reset_index()
train_hour_first = train.groupby(
['TERMINALNO', 'TIME'], as_index=False).first()
del train
train_hour_first.TIME = train_hour_first.TIME.apply(
lambda x: str(x)[:10], 1)
train_day_sum = train_hour_first.groupby(
['TERMINALNO', 'TIME'], as_index=False).sum()
train_day_sum['hour_count'] = train_day['LONGITUDE']
train_day_sum['night_drive_count'] = train_day_sum.apply(lambda x: x['hour_0'] + x['hour_1'] +
x['hour_2'] + x['hour_3'] +
x['hour_4'] + # x['hour_5'] + x['hour_6'] +
# x['hour_7'] + x['hour_8'] + x['hour_9'] +
# x['hour_18'] + x['hour_19'] +
x['hour_21'] + x['hour_22'] + x['hour_23']
, 1)
train_day_sum['night_delta'] = train_day_sum['night_drive_count'] / \
train_day_sum['hour_count']
train_day_sum['night'] = train_day_sum['night_drive_count'].apply(
lambda x: 1 if x != 0 else 0, 1)
train_hour_count['night__day_delta'] = train_day_sum.groupby(['TERMINALNO'], as_index=False).sum(
)['night'] / (train_day_sum.groupby(['TERMINALNO'], as_index=False).count()['HEIGHT'])
train_night_count = train_day_sum.groupby('TERMINALNO')['night_delta'].agg(
{"night_count_max": "max", "night_count_mean": "mean", "night_count_std": "std",
"night_count_skew": "skew"}).reset_index()
del train_day_sum
train_data = pd.merge(
train_hour_count, train_night_count, on="TERMINALNO", how="left")
return train_data
def user_driver_stat(self, data):
"""
用户驾驶行为基本特征统计
"""
# trid 特征
df_user_trid_num = data.groupby('TERMINALNO', as_index=False)['TRIP_ID'].max()
# 驾驶行为特征
user_speed_stat = data.groupby('TERMINALNO')['SPEED'].agg(
{'user_speed_mean': 'mean','user_speed_std': 'std',
'user_speed_max': 'max'}).reset_index()
user_height_stat = data.groupby('TERMINALNO')['HEIGHT'].agg(
{'user_height_mean': 'mean', 'user_height_std': 'std'}).reset_index()
user_direction_stat = data.groupby('TERMINALNO')['DIRECTION'].agg(
{'user_direction_mean': 'mean'}).reset_index()
# user_height_stat = data.groupby('TERMINALNO')['HEIGHT'].agg(
# {'user_height_mean': 'mean', 'user_height_std': 'std'}).reset_index()
# user_direction_stat = data.groupby('TERMINALNO')['DIRECTION'].agg(
# {'user_direction_mean': 'mean'}).reset_index()
user_long_stat = data.groupby(['TERMINALNO', 'TRIP_ID'])['LONGITUDE'].agg(
{'user_lon_max': 'max', 'user_lon_min': 'min'}).reset_index()
user_long_stat['user_long_diff'] = user_long_stat['user_lon_max'] - user_long_stat['user_lon_min']
user_long_lat_stat = user_long_stat.groupby(['TERMINALNO'], as_index=False)['user_long_diff'].sum()
user_lat_stat = data.groupby(['TERMINALNO','TRIP_ID'])['LATITUDE'].agg(
{'user_lat_max': 'max', 'user_lat_min': 'min'}).reset_index()
user_lat_stat['user_lat_diff'] = user_lat_stat['user_lat_max'] - user_lat_stat['user_lat_min']
user_long_lat_stat.loc[:, 'user_lat_diff'] = user_lat_stat.groupby(['TERMINALNO'], as_index=False)['user_lat_diff'].sum()
# 峰度系数是用来反映频数分布曲线顶端尖峭或扁平程度的指标
user_kurt = data.groupby('TERMINALNO')['HEIGHT','DIRECTION'].apply(lambda x: x.kurt()).reset_index()
user_kurt.rename(columns={'HEIGHT': 'user_height_kurt', 'DIRECTION': 'user_direc_kurt'}, inplace=True)
# 偏态系数是根据众数、中位数与均值各自的性质，通过比较众数或中位数与均值来衡量偏斜度的，即偏态系数是对分布偏斜方向和程度的刻画
user_skew_1 = data.groupby(['TERMINALNO','TRIP_ID'], as_index=False)['HEIGHT','DIRECTION'].skew()
# user_quantile = data.groupby('TERMINALNO')['SPEED','HEIGHT','DIRECTION'].\
# apply(lambda x: x.quantile(0.5)).reset_index()
# user_quantile.rename(
# columns={'SPEED': 'user_speed_25_quantile', 'HEIGHT': 'user_height_25_quantile',
# 'DIRECTION': 'user_direc_25_quantile'}, inplace=True)
# user_long_stat = data.groupby('TERMINALNO')['LONGITUDE'].agg(
# {'user_lon_mean': 'mean', 'user_lon_max': 'max'}).reset_index()
del data
user_max_skew = user_skew_1.groupby('TERMINALNO')['HEIGHT','DIRECTION'].max().reset_index()
user_min_skew = user_skew_1.groupby(['TERMINALNO'])['HEIGHT', 'DIRECTION'].min().reset_index()
user_max_skew.rename(
columns={'HEIGHT': 'user_height_max_skew', 'DIRECTION': 'user_direc_max_skew'}, inplace=True)
user_min_skew.rename(
columns={'HEIGHT': 'user_height_min_skew', 'DIRECTION': 'user_direc_min_skew'}, inplace=True)
# 合并
user_driver_stat = pd.merge(df_user_trid_num, user_speed_stat, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_height_stat, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_direction_stat, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_kurt, how='left', on='TERMINALNO')
# user_driver_stat = pd.merge(user_driver_stat, user_long_lat_stat, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_max_skew, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_min_skew, how='left', on='TERMINALNO')
return user_driver_stat
def user_test_driver_stat(self, data):
"""
用户驾驶行为基本特征统计
"""
# trid 特征
df_user_trid_num = data.groupby('TERMINALNO', as_index=False)['TRIP_ID'].max()
# 驾驶行为特征
user_speed_stat = data.groupby('TERMINALNO')['SPEED'].agg(
{'user_speed_mean': 'mean','user_speed_std': 'std', 'user_speed_max': 'max'}).reset_index()
user_height_stat = data.groupby('TERMINALNO')['HEIGHT'].agg(
{'user_height_mean': 'mean', 'user_height_std': 'std'}).reset_index()
user_direction_stat = data.groupby('TERMINALNO')['DIRECTION'].agg(
{'user_direction_mean': 'mean'}).reset_index()
# user_height_stat = data.groupby('TERMINALNO')['HEIGHT'].agg(
# {'user_height_mean': 'mean','user_height_std': 'std'}).reset_index()
# user_direction_stat = data.groupby('TERMINALNO')['DIRECTION'].agg(
# {'user_direction_mean': 'mean'}).reset_index()
user_kurt = data.groupby('TERMINALNO')['HEIGHT', 'DIRECTION'].apply(lambda x: x.kurt()).reset_index()
user_kurt.rename(columns={'HEIGHT': 'user_height_kurt', 'DIRECTION': 'user_direc_kurt'}, inplace=True)
user_quantile = data.groupby('TERMINALNO')['SPEED', 'HEIGHT', 'DIRECTION'].apply(
lambda x: x.quantile(0.5)).reset_index()
user_quantile.rename(
columns={'SPEED': 'user_speed_25_quantile', 'HEIGHT': 'user_height_25_quantile',
'DIRECTION': 'user_direc_25_quantile'}, inplace=True)
user_kurt.rename(
columns={'HEIGHT': 'user_height_kurt', 'DIRECTION': 'user_direc_kurt'}, inplace=True)
user_skew_1 = data.groupby(['TERMINALNO','TRIP_ID'], as_index=False)['HEIGHT','DIRECTION'].skew()
user_long_stat = data.groupby(['TERMINALNO', 'TRIP_ID'])['LONGITUDE'].agg(
{'user_lon_max': 'max', 'user_lon_min': 'min'}).reset_index()
user_long_stat['user_long_diff'] = user_long_stat['user_lon_max'] - user_long_stat['user_lon_min']
user_long_lat_stat = user_long_stat.groupby(['TERMINALNO'], as_index=False)['user_long_diff'].sum()
user_lat_stat = data.groupby(['TERMINALNO', 'TRIP_ID'])['LATITUDE'].agg(
{'user_lat_max': 'max', 'user_lat_min': 'min'}).reset_index()
user_lat_stat['user_lat_diff'] = user_lat_stat['user_lat_max'] - user_lat_stat['user_lat_min']
user_long_lat_stat.loc[:, 'user_lat_diff'] = user_lat_stat.groupby(['TERMINALNO'], as_index=False)[
'user_lat_diff'].sum()
del data
user_max_skew = user_skew_1.groupby('TERMINALNO')['HEIGHT', 'DIRECTION'].max().reset_index()
user_min_skew = user_skew_1.groupby(['TERMINALNO'])['HEIGHT', 'DIRECTION'].min().reset_index()
user_max_skew.rename(
columns={'HEIGHT': 'user_height_max_skew', 'DIRECTION': 'user_direc_max_skew'}, inplace=True)
user_min_skew.rename(
columns={'HEIGHT': 'user_height_min_skew', 'DIRECTION': 'user_direc_min_skew'}, inplace=True)
# 合并
user_driver_stat = pd.merge(df_user_trid_num, user_speed_stat, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_height_stat, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_direction_stat, how='left', on='TERMINALNO')
# user_driver_stat = pd.merge(user_driver_stat, user_long_lat_stat, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_kurt, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_max_skew, how='left', on='TERMINALNO')
user_driver_stat = pd.merge(user_driver_stat, user_min_skew, how='left', on='TERMINALNO')
user_driver_stat.loc[:, 'Y'] = -1
return user_driver_stat
def min_max_normalize(self, df, name):
"""
归一化
"""
max_number = df[name].max()
min_number = df[name].min()
# assert max_number != min_number, 'max == min in COLUMN {0}'.format(name)
df[name] = df[name].map(lambda x: float(x - min_number + 1) / float(max_number - min_number + 1))
return df
def get_distance(self, data):
"""
经纬度转距离
"""
lon_lat_data = data.loc[:, ['TERMINALNO', 'LONGITUDE', 'LATITUDE']]
del data
user_distance = lon_lat_data.groupby('TERMINALNO', as_index=False)[['LONGITUDE', 'LATITUDE']].first()
user_distance.loc[:, 'user_distance'] = user_distance.apply(
lambda row: self.haversine1(row['LONGITUDE'], row['LATITUDE'], 113.9177317, 22.54334333), axis=1)
user_distance.drop(['LONGITUDE', 'LATITUDE'], axis=1, inplace=True)
# user_norm_distance = self.min_max_normalize(user_distance, 'user_distance')
return user_distance
def geo_stat(self, data):
# data = data.loc[:, ['TERMINALNO','TIME','LONGITUDE','LATITUDE']]
feature = []
user_id = data['TERMINALNO'].min()
user_max_lon = data['LONGITUDE'].max()
user_min_lon = data['LONGITUDE'].min()
user_max_lat = data['LATITUDE'].max()
user_min_lat = data['LATITUDE'].min()
time_duration = (data['TIME'].max() - data['TIME'].min()) / 3600.0 + 1.0
del data
user_lon_ratio = (user_max_lon - user_min_lon) / time_duration
user_lat_ratio = (user_max_lat - user_min_lat) / time_duration
feature.extend([user_id, user_lon_ratio, user_lat_ratio])
return feature
def user_geo_stat(self, data):
lat_lon_feature = ['TERMINALNO',
'user_lon_ratio', 'user_lat_ratio'
]
geo_feature = []
for uid in data['TERMINALNO'].unique():
hour_fea = data.loc[data['TERMINALNO'] == uid]
geo_feature.append(self.geo_stat(hour_fea))
geo_feature = pd.DataFrame(geo_feature, columns=lat_lon_feature) # , dtype=np.float32)
return geo_feature