-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathfactors_active_drives.py
177 lines (155 loc) · 6.21 KB
/
factors_active_drives.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
import pandas as pd
import datetime
import numpy as np
# Load the Pandas libraries with alias 'pd'
pd.set_option('display.max_columns', None)
pd.set_option('display.expand_frame_repr', False)
pd.set_option('max_colwidth', -1)
data_ride_id = pd.read_csv("drop_out_drivers 2.csv")
driver_id_table = pd.read_csv("driver_ids.csv")
data_total_table = pd.read_csv('/Users/xinhao/Downloads/lyft_data_challenge/final/erin_code.csv')
print(driver_id_table)
def compute_drives_per_day(current_table):
"""
compute the number of drives per day
:param id:
:return:
"""
date_sec = list(current_table['picked_up_at'])
date = [x[:10] for x in date_sec]
if len(set(date)) == 0:
return 0
else:
return float(len(date)) / len(set(date))
def compute_total_duration(current_table):
total_dur = sum(current_table['ride_duration']/60)
return total_dur
def compute_profit(current_table):
"""
compute Avearge Profit per drive （average earning/day)
have not considered prime time
:param current_table:
:return:
"""
total = 0
for ind,drive in current_table.iterrows():
profit_per_drive = (2+1.15*drive['ride_distance']/1609.34+0.22*drive['ride_duration']/60)
profit_after_prime = profit_per_drive+ drive['ride_prime_time']/float(drive['ride_duration'])*profit_per_drive*0.22
if profit_after_prime < 5:
profit_after_prime = 5
elif profit_after_prime >400:
profit_after_prime = 400
total += profit_after_prime
return total / float(len(current_table))
def compute_profit_per_day(current_table):
total = 0
for ind,drive in current_table.iterrows():
profit_per_drive = 2+1.15*drive['ride_distance']/1609.34+0.22*drive['ride_duration']/60+1.75
if profit_per_drive < 5:
profit_per_drive = 5
elif profit_per_drive >400:
profit_per_drive = 400
total += profit_per_drive
# print(total,len(current_table),total / float(len(current_table)))
date_sec = list(current_table['picked_up_at'])
date = [x[:10] for x in date_sec]
if len(set(date)) == 0:
return 0
else:
return total / len(set(date))
def compute_responding_time(current_table):
"""
# compute average responding time
# :param current_table:
# :return:
# """
responding_time = datetime.timedelta()
for ind,drive in current_table.iterrows():
try:
request = datetime.datetime.strptime(drive['requested_at'],'%Y-%m-%d %H:%M:%S')
pick_up = datetime.datetime.strptime(drive['picked_up_at'],'%Y-%m-%d %H:%M:%S')
time = pick_up-request
responding_time = responding_time+time
responding_time = int(responding_time.seconds)
except:
continue
return responding_time/float(len(current_table))
def compute_arrival_time(current_table):
"""
# compute average responding time
# :param current_table:
# :return:
# """
responding_time = datetime.timedelta()
for ind,drive in current_table.iterrows():
try:
request = datetime.datetime.strptime(drive['accepted_at'],'%Y-%m-%d %H:%M:%S')
pick_up = datetime.datetime.strptime(drive['arrived_at'],'%Y-%m-%d %H:%M:%S')
time = pick_up-request
responding_time = responding_time+time
responding_time = int(responding_time.seconds)
except:
continue
return responding_time/float(len(current_table))
def compute_waiting_time(current_table):
"""
# compute average responding time
# :param current_table:
# :return:
# """
responding_time = datetime.timedelta()
for ind,drive in current_table.iterrows():
try:
request = datetime.datetime.strptime(drive['picked_up_at'],'%Y-%m-%d %H:%M:%S')
pick_up = datetime.datetime.strptime(drive['arrived_at'],'%Y-%m-%d %H:%M:%S')
time = pick_up-request
responding_time = responding_time+time
responding_time = int(responding_time.seconds)
except:
continue
return responding_time/float(len(current_table))
def compute_speed(current_table):
total_dist = sum(list(current_table['ride_distance']))
total_duration = sum(list(current_table['ride_duration']))
return float(total_dist)/total_duration
def compute_pearson_coefficient(factors):
"""
compute pearson_coefficient with career length
:param x:
:param carreer_len:
:return:
"""
for col in factors.columns:
if col != 'driver_id':
print(col, factors[col].corr(factors['career_len']))
def compute_career_length(current_table):
"""
:param current_table:
:return:
"""
current_table = current_table.sort_values(['requested_at'], ascending=False)
last_ride = datetime.datetime.strptime(current_table.iloc[0]['picked_up_at'], '%Y-%m-%d %H:%M:%S')
driver_id = current_table.iloc[0]['driver']
find_onboard = driver_id_table[driver_id_table['driver_id'] == driver_id].iloc[0]['driver_onboard_date']
onboard_time = datetime.datetime.strptime(find_onboard, '%Y-%m-%d %H:%M:%S')
time = last_ride - onboard_time
career_len = int(time.days)
return career_len
factors = pd.DataFrame(columns = ['driver_id','career_len','rides_per_day','profit','responding','arrival','waiting','speed','total_duration'])
for driver_id in driver_id_table['driver_id']:
current_table = data_total_table[data_total_table['driver'] == driver_id]
if len(current_table) > 0:
num_per_day = compute_drives_per_day(current_table)
profit = compute_profit_per_day(current_table)
responding_time = compute_responding_time(current_table)
arrival_time = compute_arrival_time(current_table)
waiting_time = compute_waiting_time(current_table)
speed = compute_speed(current_table)
dur = compute_total_duration(current_table)
career_len = compute_career_length(current_table)
factors.loc[-1] = [driver_id, career_len,num_per_day, profit,responding_time,arrival_time,waiting_time,speed,dur]
factors.index = factors.index + 1
factors = factors.sort_index()
print(len(factors))
factors.to_csv('main_factors_new.csv')
# print(data_total_table)