In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import src.misc.paths as path
%matplotlib inline
import src.vector_gen.generateWeatherVectors as gwv
training_files = "../../dataset/training/"
trajectories_file = "trajectories(table 5)_training.csv"
trajectories_df = pd.read_csv(training_files+trajectories_file)
In [2]:
import src.vector_gen.generateWeatherVectors as gwv
x, y = gwv.generate_timeInformationVectors(trajectories_df)
x_df = gwv.generate_timeInformationVectorX_df(trajectories_df, True)# using arrays -.-
#x_df = pd.DataFrame(np.reshape(x,(len(x)/3,3)), columns=['dayofweek', 'hour', 'minute'])
y_df = pd.DataFrame(y, columns=['avg_travel_time'])
In [3]:
x
Out[3]:
array([ 1, 0, 0, ..., 0, 21, 40], dtype=int64)
In [4]:
x_df['date'] = x_df['datetime'].dt.date
x_df = x_df.set_index(['date','hour','minute'])
x_df
Out[4]:
datetime
dayofweek
date
hour
minute
2016-07-19
0
0
2016-07-19 00:00:00
1
20
2016-07-19 00:20:00
1
40
2016-07-19 00:40:00
1
1
0
2016-07-19 01:00:00
1
20
2016-07-19 01:20:00
1
40
2016-07-19 01:40:00
1
2
0
2016-07-19 02:00:00
1
20
2016-07-19 02:20:00
1
40
2016-07-19 02:40:00
1
3
0
2016-07-19 03:00:00
1
20
2016-07-19 03:20:00
1
40
2016-07-19 03:40:00
1
4
0
2016-07-19 04:00:00
1
20
2016-07-19 04:20:00
1
40
2016-07-19 04:40:00
1
5
0
2016-07-19 05:00:00
1
20
2016-07-19 05:20:00
1
40
2016-07-19 05:40:00
1
6
0
2016-07-19 06:00:00
1
20
2016-07-19 06:20:00
1
40
2016-07-19 06:40:00
1
7
0
2016-07-19 07:00:00
1
20
2016-07-19 07:20:00
1
40
2016-07-19 07:40:00
1
8
0
2016-07-19 08:00:00
1
20
2016-07-19 08:20:00
1
40
2016-07-19 08:40:00
1
9
0
2016-07-19 09:00:00
1
20
2016-07-19 09:20:00
1
40
2016-07-19 09:40:00
1
...
...
...
...
...
2016-10-17
12
0
2016-10-17 12:00:00
0
20
2016-10-17 12:20:00
0
40
2016-10-17 12:40:00
0
13
0
2016-10-17 13:00:00
0
20
2016-10-17 13:20:00
0
40
2016-10-17 13:40:00
0
14
0
2016-10-17 14:00:00
0
20
2016-10-17 14:20:00
0
40
2016-10-17 14:40:00
0
15
0
2016-10-17 15:00:00
0
20
2016-10-17 15:20:00
0
40
2016-10-17 15:40:00
0
16
0
2016-10-17 16:00:00
0
20
2016-10-17 16:20:00
0
40
2016-10-17 16:40:00
0
17
0
2016-10-17 17:00:00
0
20
2016-10-17 17:20:00
0
40
2016-10-17 17:40:00
0
18
0
2016-10-17 18:00:00
0
20
2016-10-17 18:20:00
0
40
2016-10-17 18:40:00
0
19
0
2016-10-17 19:00:00
0
20
2016-10-17 19:20:00
0
40
2016-10-17 19:40:00
0
20
0
2016-10-17 20:00:00
0
20
2016-10-17 20:20:00
0
40
2016-10-17 20:40:00
0
21
0
2016-10-17 21:00:00
0
20
2016-10-17 21:20:00
0
40
2016-10-17 21:40:00
0
6546 rows × 2 columns
In [5]:
import itertools
t0 = list(x_df['datetime'])
route_touples = [('A', 2), ('A', 3), ('B', 1), ('B', 3), ('C', 1), ('C', 3)]
index_touples = list(itertools.product(t0, route_touples))
index_touples
# fix
res = []
for z in t0:
for route in route_touples:
tmp = []
tmp.append(z)
tmp.append(route[0])
tmp.append(route[1])
res.append(tmp)
res
Out[5]:
[[Timestamp('2016-07-19 00:00:00'), 'A', 2],
[Timestamp('2016-07-19 00:00:00'), 'A', 3],
[Timestamp('2016-07-19 00:00:00'), 'B', 1],
[Timestamp('2016-07-19 00:00:00'), 'B', 3],
[Timestamp('2016-07-19 00:00:00'), 'C', 1],
[Timestamp('2016-07-19 00:00:00'), 'C', 3],
[Timestamp('2016-07-19 00:20:00'), 'A', 2],
[Timestamp('2016-07-19 00:20:00'), 'A', 3],
[Timestamp('2016-07-19 00:20:00'), 'B', 1],
[Timestamp('2016-07-19 00:20:00'), 'B', 3],
[Timestamp('2016-07-19 00:20:00'), 'C', 1],
[Timestamp('2016-07-19 00:20:00'), 'C', 3],
[Timestamp('2016-07-19 00:40:00'), 'A', 2],
[Timestamp('2016-07-19 00:40:00'), 'A', 3],
[Timestamp('2016-07-19 00:40:00'), 'B', 1],
[Timestamp('2016-07-19 00:40:00'), 'B', 3],
[Timestamp('2016-07-19 00:40:00'), 'C', 1],
[Timestamp('2016-07-19 00:40:00'), 'C', 3],
[Timestamp('2016-07-19 01:00:00'), 'A', 2],
[Timestamp('2016-07-19 01:00:00'), 'A', 3],
[Timestamp('2016-07-19 01:00:00'), 'B', 1],
[Timestamp('2016-07-19 01:00:00'), 'B', 3],
[Timestamp('2016-07-19 01:00:00'), 'C', 1],
[Timestamp('2016-07-19 01:00:00'), 'C', 3],
[Timestamp('2016-07-19 01:20:00'), 'A', 2],
[Timestamp('2016-07-19 01:20:00'), 'A', 3],
[Timestamp('2016-07-19 01:20:00'), 'B', 1],
[Timestamp('2016-07-19 01:20:00'), 'B', 3],
[Timestamp('2016-07-19 01:20:00'), 'C', 1],
[Timestamp('2016-07-19 01:20:00'), 'C', 3],
[Timestamp('2016-07-19 01:40:00'), 'A', 2],
[Timestamp('2016-07-19 01:40:00'), 'A', 3],
[Timestamp('2016-07-19 01:40:00'), 'B', 1],
[Timestamp('2016-07-19 01:40:00'), 'B', 3],
[Timestamp('2016-07-19 01:40:00'), 'C', 1],
[Timestamp('2016-07-19 01:40:00'), 'C', 3],
[Timestamp('2016-07-19 02:00:00'), 'A', 2],
[Timestamp('2016-07-19 02:00:00'), 'A', 3],
[Timestamp('2016-07-19 02:00:00'), 'B', 1],
[Timestamp('2016-07-19 02:00:00'), 'B', 3],
[Timestamp('2016-07-19 02:00:00'), 'C', 1],
[Timestamp('2016-07-19 02:00:00'), 'C', 3],
[Timestamp('2016-07-19 02:20:00'), 'A', 2],
[Timestamp('2016-07-19 02:20:00'), 'A', 3],
[Timestamp('2016-07-19 02:20:00'), 'B', 1],
[Timestamp('2016-07-19 02:20:00'), 'B', 3],
[Timestamp('2016-07-19 02:20:00'), 'C', 1],
[Timestamp('2016-07-19 02:20:00'), 'C', 3],
[Timestamp('2016-07-19 02:40:00'), 'A', 2],
[Timestamp('2016-07-19 02:40:00'), 'A', 3],
[Timestamp('2016-07-19 02:40:00'), 'B', 1],
[Timestamp('2016-07-19 02:40:00'), 'B', 3],
[Timestamp('2016-07-19 02:40:00'), 'C', 1],
[Timestamp('2016-07-19 02:40:00'), 'C', 3],
[Timestamp('2016-07-19 03:00:00'), 'A', 2],
[Timestamp('2016-07-19 03:00:00'), 'A', 3],
[Timestamp('2016-07-19 03:00:00'), 'B', 1],
[Timestamp('2016-07-19 03:00:00'), 'B', 3],
[Timestamp('2016-07-19 03:00:00'), 'C', 1],
[Timestamp('2016-07-19 03:00:00'), 'C', 3],
[Timestamp('2016-07-19 03:20:00'), 'A', 2],
[Timestamp('2016-07-19 03:20:00'), 'A', 3],
[Timestamp('2016-07-19 03:20:00'), 'B', 1],
[Timestamp('2016-07-19 03:20:00'), 'B', 3],
[Timestamp('2016-07-19 03:20:00'), 'C', 1],
[Timestamp('2016-07-19 03:20:00'), 'C', 3],
[Timestamp('2016-07-19 03:40:00'), 'A', 2],
[Timestamp('2016-07-19 03:40:00'), 'A', 3],
[Timestamp('2016-07-19 03:40:00'), 'B', 1],
[Timestamp('2016-07-19 03:40:00'), 'B', 3],
[Timestamp('2016-07-19 03:40:00'), 'C', 1],
[Timestamp('2016-07-19 03:40:00'), 'C', 3],
[Timestamp('2016-07-19 04:00:00'), 'A', 2],
[Timestamp('2016-07-19 04:00:00'), 'A', 3],
[Timestamp('2016-07-19 04:00:00'), 'B', 1],
[Timestamp('2016-07-19 04:00:00'), 'B', 3],
[Timestamp('2016-07-19 04:00:00'), 'C', 1],
[Timestamp('2016-07-19 04:00:00'), 'C', 3],
[Timestamp('2016-07-19 04:20:00'), 'A', 2],
[Timestamp('2016-07-19 04:20:00'), 'A', 3],
[Timestamp('2016-07-19 04:20:00'), 'B', 1],
[Timestamp('2016-07-19 04:20:00'), 'B', 3],
[Timestamp('2016-07-19 04:20:00'), 'C', 1],
[Timestamp('2016-07-19 04:20:00'), 'C', 3],
[Timestamp('2016-07-19 04:40:00'), 'A', 2],
[Timestamp('2016-07-19 04:40:00'), 'A', 3],
[Timestamp('2016-07-19 04:40:00'), 'B', 1],
[Timestamp('2016-07-19 04:40:00'), 'B', 3],
[Timestamp('2016-07-19 04:40:00'), 'C', 1],
[Timestamp('2016-07-19 04:40:00'), 'C', 3],
[Timestamp('2016-07-19 05:00:00'), 'A', 2],
[Timestamp('2016-07-19 05:00:00'), 'A', 3],
[Timestamp('2016-07-19 05:00:00'), 'B', 1],
[Timestamp('2016-07-19 05:00:00'), 'B', 3],
[Timestamp('2016-07-19 05:00:00'), 'C', 1],
[Timestamp('2016-07-19 05:00:00'), 'C', 3],
[Timestamp('2016-07-19 05:20:00'), 'A', 2],
[Timestamp('2016-07-19 05:20:00'), 'A', 3],
[Timestamp('2016-07-19 05:20:00'), 'B', 1],
[Timestamp('2016-07-19 05:20:00'), 'B', 3],
[Timestamp('2016-07-19 05:20:00'), 'C', 1],
[Timestamp('2016-07-19 05:20:00'), 'C', 3],
[Timestamp('2016-07-19 05:40:00'), 'A', 2],
[Timestamp('2016-07-19 05:40:00'), 'A', 3],
[Timestamp('2016-07-19 05:40:00'), 'B', 1],
[Timestamp('2016-07-19 05:40:00'), 'B', 3],
[Timestamp('2016-07-19 05:40:00'), 'C', 1],
[Timestamp('2016-07-19 05:40:00'), 'C', 3],
[Timestamp('2016-07-19 06:00:00'), 'A', 2],
[Timestamp('2016-07-19 06:00:00'), 'A', 3],
[Timestamp('2016-07-19 06:00:00'), 'B', 1],
[Timestamp('2016-07-19 06:00:00'), 'B', 3],
[Timestamp('2016-07-19 06:00:00'), 'C', 1],
[Timestamp('2016-07-19 06:00:00'), 'C', 3],
[Timestamp('2016-07-19 06:20:00'), 'A', 2],
[Timestamp('2016-07-19 06:20:00'), 'A', 3],
[Timestamp('2016-07-19 06:20:00'), 'B', 1],
[Timestamp('2016-07-19 06:20:00'), 'B', 3],
[Timestamp('2016-07-19 06:20:00'), 'C', 1],
[Timestamp('2016-07-19 06:20:00'), 'C', 3],
[Timestamp('2016-07-19 06:40:00'), 'A', 2],
[Timestamp('2016-07-19 06:40:00'), 'A', 3],
[Timestamp('2016-07-19 06:40:00'), 'B', 1],
[Timestamp('2016-07-19 06:40:00'), 'B', 3],
[Timestamp('2016-07-19 06:40:00'), 'C', 1],
[Timestamp('2016-07-19 06:40:00'), 'C', 3],
[Timestamp('2016-07-19 07:00:00'), 'A', 2],
[Timestamp('2016-07-19 07:00:00'), 'A', 3],
[Timestamp('2016-07-19 07:00:00'), 'B', 1],
[Timestamp('2016-07-19 07:00:00'), 'B', 3],
[Timestamp('2016-07-19 07:00:00'), 'C', 1],
[Timestamp('2016-07-19 07:00:00'), 'C', 3],
[Timestamp('2016-07-19 07:20:00'), 'A', 2],
[Timestamp('2016-07-19 07:20:00'), 'A', 3],
[Timestamp('2016-07-19 07:20:00'), 'B', 1],
[Timestamp('2016-07-19 07:20:00'), 'B', 3],
[Timestamp('2016-07-19 07:20:00'), 'C', 1],
[Timestamp('2016-07-19 07:20:00'), 'C', 3],
[Timestamp('2016-07-19 07:40:00'), 'A', 2],
[Timestamp('2016-07-19 07:40:00'), 'A', 3],
[Timestamp('2016-07-19 07:40:00'), 'B', 1],
[Timestamp('2016-07-19 07:40:00'), 'B', 3],
[Timestamp('2016-07-19 07:40:00'), 'C', 1],
[Timestamp('2016-07-19 07:40:00'), 'C', 3],
[Timestamp('2016-07-19 08:00:00'), 'A', 2],
[Timestamp('2016-07-19 08:00:00'), 'A', 3],
[Timestamp('2016-07-19 08:00:00'), 'B', 1],
[Timestamp('2016-07-19 08:00:00'), 'B', 3],
[Timestamp('2016-07-19 08:00:00'), 'C', 1],
[Timestamp('2016-07-19 08:00:00'), 'C', 3],
[Timestamp('2016-07-19 08:20:00'), 'A', 2],
[Timestamp('2016-07-19 08:20:00'), 'A', 3],
[Timestamp('2016-07-19 08:20:00'), 'B', 1],
[Timestamp('2016-07-19 08:20:00'), 'B', 3],
[Timestamp('2016-07-19 08:20:00'), 'C', 1],
[Timestamp('2016-07-19 08:20:00'), 'C', 3],
[Timestamp('2016-07-19 08:40:00'), 'A', 2],
[Timestamp('2016-07-19 08:40:00'), 'A', 3],
[Timestamp('2016-07-19 08:40:00'), 'B', 1],
[Timestamp('2016-07-19 08:40:00'), 'B', 3],
[Timestamp('2016-07-19 08:40:00'), 'C', 1],
[Timestamp('2016-07-19 08:40:00'), 'C', 3],
[Timestamp('2016-07-19 09:00:00'), 'A', 2],
[Timestamp('2016-07-19 09:00:00'), 'A', 3],
[Timestamp('2016-07-19 09:00:00'), 'B', 1],
[Timestamp('2016-07-19 09:00:00'), 'B', 3],
[Timestamp('2016-07-19 09:00:00'), 'C', 1],
[Timestamp('2016-07-19 09:00:00'), 'C', 3],
[Timestamp('2016-07-19 09:20:00'), 'A', 2],
[Timestamp('2016-07-19 09:20:00'), 'A', 3],
[Timestamp('2016-07-19 09:20:00'), 'B', 1],
[Timestamp('2016-07-19 09:20:00'), 'B', 3],
[Timestamp('2016-07-19 09:20:00'), 'C', 1],
[Timestamp('2016-07-19 09:20:00'), 'C', 3],
[Timestamp('2016-07-19 09:40:00'), 'A', 2],
[Timestamp('2016-07-19 09:40:00'), 'A', 3],
[Timestamp('2016-07-19 09:40:00'), 'B', 1],
[Timestamp('2016-07-19 09:40:00'), 'B', 3],
[Timestamp('2016-07-19 09:40:00'), 'C', 1],
[Timestamp('2016-07-19 09:40:00'), 'C', 3],
[Timestamp('2016-07-19 10:00:00'), 'A', 2],
[Timestamp('2016-07-19 10:00:00'), 'A', 3],
[Timestamp('2016-07-19 10:00:00'), 'B', 1],
[Timestamp('2016-07-19 10:00:00'), 'B', 3],
[Timestamp('2016-07-19 10:00:00'), 'C', 1],
[Timestamp('2016-07-19 10:00:00'), 'C', 3],
[Timestamp('2016-07-19 10:20:00'), 'A', 2],
[Timestamp('2016-07-19 10:20:00'), 'A', 3],
[Timestamp('2016-07-19 10:20:00'), 'B', 1],
[Timestamp('2016-07-19 10:20:00'), 'B', 3],
[Timestamp('2016-07-19 10:20:00'), 'C', 1],
[Timestamp('2016-07-19 10:20:00'), 'C', 3],
[Timestamp('2016-07-19 10:40:00'), 'A', 2],
[Timestamp('2016-07-19 10:40:00'), 'A', 3],
[Timestamp('2016-07-19 10:40:00'), 'B', 1],
[Timestamp('2016-07-19 10:40:00'), 'B', 3],
[Timestamp('2016-07-19 10:40:00'), 'C', 1],
[Timestamp('2016-07-19 10:40:00'), 'C', 3],
[Timestamp('2016-07-19 11:00:00'), 'A', 2],
[Timestamp('2016-07-19 11:00:00'), 'A', 3],
[Timestamp('2016-07-19 11:00:00'), 'B', 1],
[Timestamp('2016-07-19 11:00:00'), 'B', 3],
[Timestamp('2016-07-19 11:00:00'), 'C', 1],
[Timestamp('2016-07-19 11:00:00'), 'C', 3],
[Timestamp('2016-07-19 11:20:00'), 'A', 2],
[Timestamp('2016-07-19 11:20:00'), 'A', 3],
[Timestamp('2016-07-19 11:20:00'), 'B', 1],
[Timestamp('2016-07-19 11:20:00'), 'B', 3],
[Timestamp('2016-07-19 11:20:00'), 'C', 1],
[Timestamp('2016-07-19 11:20:00'), 'C', 3],
[Timestamp('2016-07-19 11:40:00'), 'A', 2],
[Timestamp('2016-07-19 11:40:00'), 'A', 3],
[Timestamp('2016-07-19 11:40:00'), 'B', 1],
[Timestamp('2016-07-19 11:40:00'), 'B', 3],
[Timestamp('2016-07-19 11:40:00'), 'C', 1],
[Timestamp('2016-07-19 11:40:00'), 'C', 3],
[Timestamp('2016-07-19 12:00:00'), 'A', 2],
[Timestamp('2016-07-19 12:00:00'), 'A', 3],
[Timestamp('2016-07-19 12:00:00'), 'B', 1],
[Timestamp('2016-07-19 12:00:00'), 'B', 3],
[Timestamp('2016-07-19 12:00:00'), 'C', 1],
[Timestamp('2016-07-19 12:00:00'), 'C', 3],
[Timestamp('2016-07-19 12:20:00'), 'A', 2],
[Timestamp('2016-07-19 12:20:00'), 'A', 3],
[Timestamp('2016-07-19 12:20:00'), 'B', 1],
[Timestamp('2016-07-19 12:20:00'), 'B', 3],
[Timestamp('2016-07-19 12:20:00'), 'C', 1],
[Timestamp('2016-07-19 12:20:00'), 'C', 3],
[Timestamp('2016-07-19 12:40:00'), 'A', 2],
[Timestamp('2016-07-19 12:40:00'), 'A', 3],
[Timestamp('2016-07-19 12:40:00'), 'B', 1],
[Timestamp('2016-07-19 12:40:00'), 'B', 3],
[Timestamp('2016-07-19 12:40:00'), 'C', 1],
[Timestamp('2016-07-19 12:40:00'), 'C', 3],
[Timestamp('2016-07-19 13:00:00'), 'A', 2],
[Timestamp('2016-07-19 13:00:00'), 'A', 3],
[Timestamp('2016-07-19 13:00:00'), 'B', 1],
[Timestamp('2016-07-19 13:00:00'), 'B', 3],
[Timestamp('2016-07-19 13:00:00'), 'C', 1],
[Timestamp('2016-07-19 13:00:00'), 'C', 3],
[Timestamp('2016-07-19 13:20:00'), 'A', 2],
[Timestamp('2016-07-19 13:20:00'), 'A', 3],
[Timestamp('2016-07-19 13:20:00'), 'B', 1],
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[Timestamp('2016-07-19 13:20:00'), 'C', 1],
[Timestamp('2016-07-19 13:20:00'), 'C', 3],
[Timestamp('2016-07-19 13:40:00'), 'A', 2],
[Timestamp('2016-07-19 13:40:00'), 'A', 3],
[Timestamp('2016-07-19 13:40:00'), 'B', 1],
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[Timestamp('2016-07-19 13:40:00'), 'C', 1],
[Timestamp('2016-07-19 13:40:00'), 'C', 3],
[Timestamp('2016-07-19 14:00:00'), 'A', 2],
[Timestamp('2016-07-19 14:00:00'), 'A', 3],
[Timestamp('2016-07-19 14:00:00'), 'B', 1],
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[Timestamp('2016-07-19 14:00:00'), 'C', 1],
[Timestamp('2016-07-19 14:00:00'), 'C', 3],
[Timestamp('2016-07-19 14:20:00'), 'A', 2],
[Timestamp('2016-07-19 14:20:00'), 'A', 3],
[Timestamp('2016-07-19 14:20:00'), 'B', 1],
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[Timestamp('2016-07-19 14:20:00'), 'C', 1],
[Timestamp('2016-07-19 14:20:00'), 'C', 3],
[Timestamp('2016-07-19 14:40:00'), 'A', 2],
[Timestamp('2016-07-19 14:40:00'), 'A', 3],
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[Timestamp('2016-07-19 14:40:00'), 'C', 1],
[Timestamp('2016-07-19 14:40:00'), 'C', 3],
[Timestamp('2016-07-19 15:00:00'), 'A', 2],
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[Timestamp('2016-07-19 15:00:00'), 'B', 1],
[Timestamp('2016-07-19 15:00:00'), 'B', 3],
[Timestamp('2016-07-19 15:00:00'), 'C', 1],
[Timestamp('2016-07-19 15:00:00'), 'C', 3],
[Timestamp('2016-07-19 15:20:00'), 'A', 2],
[Timestamp('2016-07-19 15:20:00'), 'A', 3],
[Timestamp('2016-07-19 15:20:00'), 'B', 1],
[Timestamp('2016-07-19 15:20:00'), 'B', 3],
[Timestamp('2016-07-19 15:20:00'), 'C', 1],
[Timestamp('2016-07-19 15:20:00'), 'C', 3],
[Timestamp('2016-07-19 15:40:00'), 'A', 2],
[Timestamp('2016-07-19 15:40:00'), 'A', 3],
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[Timestamp('2016-07-19 15:40:00'), 'C', 1],
[Timestamp('2016-07-19 15:40:00'), 'C', 3],
[Timestamp('2016-07-19 16:00:00'), 'A', 2],
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[Timestamp('2016-07-19 16:00:00'), 'C', 3],
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[Timestamp('2016-07-19 16:20:00'), 'C', 1],
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[Timestamp('2016-07-19 16:40:00'), 'A', 2],
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[Timestamp('2016-07-19 16:40:00'), 'B', 1],
[Timestamp('2016-07-19 16:40:00'), 'B', 3],
[Timestamp('2016-07-19 16:40:00'), 'C', 1],
[Timestamp('2016-07-19 16:40:00'), 'C', 3],
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[Timestamp('2016-07-20 20:20:00'), 'A', 2],
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[Timestamp('2016-07-21 01:40:00'), 'A', 2],
[Timestamp('2016-07-21 01:40:00'), 'A', 3],
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[Timestamp('2016-07-21 01:40:00'), 'C', 3],
[Timestamp('2016-07-21 02:00:00'), 'A', 2],
[Timestamp('2016-07-21 02:00:00'), 'A', 3],
[Timestamp('2016-07-21 02:00:00'), 'B', 1],
[Timestamp('2016-07-21 02:00:00'), 'B', 3],
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[Timestamp('2016-07-21 05:00:00'), 'C', 3],
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[Timestamp('2016-07-21 05:20:00'), 'A', 3],
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[Timestamp('2016-07-21 05:20:00'), 'B', 3],
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[Timestamp('2016-07-21 05:20:00'), 'C', 3],
[Timestamp('2016-07-21 05:40:00'), 'A', 2],
[Timestamp('2016-07-21 05:40:00'), 'A', 3],
[Timestamp('2016-07-21 05:40:00'), 'B', 1],
[Timestamp('2016-07-21 05:40:00'), 'B', 3],
[Timestamp('2016-07-21 05:40:00'), 'C', 1],
[Timestamp('2016-07-21 05:40:00'), 'C', 3],
[Timestamp('2016-07-21 06:00:00'), 'A', 2],
[Timestamp('2016-07-21 06:00:00'), 'A', 3],
[Timestamp('2016-07-21 06:00:00'), 'B', 1],
[Timestamp('2016-07-21 06:00:00'), 'B', 3],
[Timestamp('2016-07-21 06:00:00'), 'C', 1],
[Timestamp('2016-07-21 06:00:00'), 'C', 3],
[Timestamp('2016-07-21 06:20:00'), 'A', 2],
[Timestamp('2016-07-21 06:20:00'), 'A', 3],
[Timestamp('2016-07-21 06:20:00'), 'B', 1],
[Timestamp('2016-07-21 06:20:00'), 'B', 3],
[Timestamp('2016-07-21 06:20:00'), 'C', 1],
[Timestamp('2016-07-21 06:20:00'), 'C', 3],
[Timestamp('2016-07-21 06:40:00'), 'A', 2],
[Timestamp('2016-07-21 06:40:00'), 'A', 3],
[Timestamp('2016-07-21 06:40:00'), 'B', 1],
[Timestamp('2016-07-21 06:40:00'), 'B', 3],
[Timestamp('2016-07-21 06:40:00'), 'C', 1],
[Timestamp('2016-07-21 06:40:00'), 'C', 3],
[Timestamp('2016-07-21 07:00:00'), 'A', 2],
[Timestamp('2016-07-21 07:00:00'), 'A', 3],
[Timestamp('2016-07-21 07:00:00'), 'B', 1],
[Timestamp('2016-07-21 07:00:00'), 'B', 3],
[Timestamp('2016-07-21 07:00:00'), 'C', 1],
[Timestamp('2016-07-21 07:00:00'), 'C', 3],
[Timestamp('2016-07-21 07:20:00'), 'A', 2],
[Timestamp('2016-07-21 07:20:00'), 'A', 3],
[Timestamp('2016-07-21 07:20:00'), 'B', 1],
[Timestamp('2016-07-21 07:20:00'), 'B', 3],
...]
In [6]:
df = pd.DataFrame(res, columns=['datetime', 'intersection_id', 'tollgate_id'])
df['datetime'] = pd.to_datetime(df['datetime'])
df['date'] = df['datetime'].dt.date
df['hour'] = df['datetime'].dt.hour
df['minute'] = df['datetime'].dt.minute
df['dayofweek'] = df['datetime'].dt.dayofweek
df = df.join(y_df)
df
Out[6]:
datetime
intersection_id
tollgate_id
date
hour
minute
dayofweek
avg_travel_time
0
2016-07-19 00:00:00
A
2
2016-07-19
0
0
1
37.09
1
2016-07-19 00:00:00
A
3
2016-07-19
0
0
1
35.27
2
2016-07-19 00:00:00
B
1
2016-07-19
0
0
1
15.58
3
2016-07-19 00:00:00
B
3
2016-07-19
0
0
1
67.81
4
2016-07-19 00:00:00
C
1
2016-07-19
0
0
1
8.36
5
2016-07-19 00:00:00
C
3
2016-07-19
0
0
1
17.12
6
2016-07-19 00:20:00
A
2
2016-07-19
0
20
1
42.64
7
2016-07-19 00:20:00
A
3
2016-07-19
0
20
1
77.61
8
2016-07-19 00:20:00
B
1
2016-07-19
0
20
1
10.38
9
2016-07-19 00:20:00
B
3
2016-07-19
0
20
1
25.51
10
2016-07-19 00:20:00
C
1
2016-07-19
0
20
1
24.81
11
2016-07-19 00:20:00
C
3
2016-07-19
0
20
1
11.66
12
2016-07-19 00:40:00
A
2
2016-07-19
0
40
1
40.17
13
2016-07-19 00:40:00
A
3
2016-07-19
0
40
1
29.30
14
2016-07-19 00:40:00
B
1
2016-07-19
0
40
1
13.44
15
2016-07-19 00:40:00
B
3
2016-07-19
0
40
1
167.55
16
2016-07-19 00:40:00
C
1
2016-07-19
0
40
1
20.74
17
2016-07-19 00:40:00
C
3
2016-07-19
0
40
1
7.62
18
2016-07-19 01:00:00
A
2
2016-07-19
1
0
1
39.12
19
2016-07-19 01:00:00
A
3
2016-07-19
1
0
1
93.09
20
2016-07-19 01:00:00
B
1
2016-07-19
1
0
1
17.06
21
2016-07-19 01:00:00
B
3
2016-07-19
1
0
1
36.47
22
2016-07-19 01:00:00
C
1
2016-07-19
1
0
1
14.96
23
2016-07-19 01:00:00
C
3
2016-07-19
1
0
1
17.62
24
2016-07-19 01:20:00
A
2
2016-07-19
1
20
1
41.92
25
2016-07-19 01:20:00
A
3
2016-07-19
1
20
1
32.12
26
2016-07-19 01:20:00
B
1
2016-07-19
1
20
1
11.06
27
2016-07-19 01:20:00
B
3
2016-07-19
1
20
1
31.36
28
2016-07-19 01:20:00
C
1
2016-07-19
1
20
1
13.87
29
2016-07-19 01:20:00
C
3
2016-07-19
1
20
1
11.76
...
...
...
...
...
...
...
...
...
39246
2016-10-17 20:20:00
A
2
2016-10-17
20
20
0
50.18
39247
2016-10-17 20:20:00
A
3
2016-10-17
20
20
0
71.97
39248
2016-10-17 20:20:00
B
1
2016-10-17
20
20
0
136.78
39249
2016-10-17 20:20:00
B
3
2016-10-17
20
20
0
115.60
39250
2016-10-17 20:20:00
C
1
2016-10-17
20
20
0
115.16
39251
2016-10-17 20:20:00
C
3
2016-10-17
20
20
0
185.25
39252
2016-10-17 20:40:00
A
2
2016-10-17
20
40
0
55.83
39253
2016-10-17 20:40:00
A
3
2016-10-17
20
40
0
83.76
39254
2016-10-17 20:40:00
B
1
2016-10-17
20
40
0
28.32
39255
2016-10-17 20:40:00
B
3
2016-10-17
20
40
0
60.92
39256
2016-10-17 20:40:00
C
1
2016-10-17
20
40
0
65.62
39257
2016-10-17 20:40:00
C
3
2016-10-17
20
40
0
150.53
39258
2016-10-17 21:00:00
A
2
2016-10-17
21
0
0
50.90
39259
2016-10-17 21:00:00
A
3
2016-10-17
21
0
0
103.69
39260
2016-10-17 21:00:00
B
1
2016-10-17
21
0
0
25.26
39261
2016-10-17 21:00:00
B
3
2016-10-17
21
0
0
60.49
39262
2016-10-17 21:00:00
C
1
2016-10-17
21
0
0
194.33
39263
2016-10-17 21:00:00
C
3
2016-10-17
21
0
0
35.69
39264
2016-10-17 21:20:00
A
2
2016-10-17
21
20
0
79.21
39265
2016-10-17 21:20:00
A
3
2016-10-17
21
20
0
157.23
39266
2016-10-17 21:20:00
B
1
2016-10-17
21
20
0
97.54
39267
2016-10-17 21:20:00
B
3
2016-10-17
21
20
0
131.68
39268
2016-10-17 21:20:00
C
1
2016-10-17
21
20
0
159.78
39269
2016-10-17 21:20:00
C
3
2016-10-17
21
20
0
34.71
39270
2016-10-17 21:40:00
A
2
2016-10-17
21
40
0
42.27
39271
2016-10-17 21:40:00
A
3
2016-10-17
21
40
0
113.94
39272
2016-10-17 21:40:00
B
1
2016-10-17
21
40
0
23.50
39273
2016-10-17 21:40:00
B
3
2016-10-17
21
40
0
39.47
39274
2016-10-17 21:40:00
C
1
2016-10-17
21
40
0
35.92
39275
2016-10-17 21:40:00
C
3
2016-10-17
21
40
0
21.77
39276 rows × 8 columns
In [ ]:
In [7]:
y
Out[7]:
array([ 37.09, 35.27, 15.58, ..., 39.47, 35.92, 21.77])
In [8]:
y_df.head()
Out[8]:
avg_travel_time
0
37.09
1
35.27
2
15.58
3
67.81
4
8.36
5
17.12
6
42.64
7
77.61
8
10.38
9
25.51
10
24.81
11
11.66
12
40.17
13
29.30
14
13.44
15
167.55
16
20.74
17
7.62
18
39.12
19
93.09
20
17.06
21
36.47
22
14.96
23
17.62
24
41.92
25
32.12
26
11.06
27
31.36
28
13.87
29
11.76
...
...
39246
50.18
39247
71.97
39248
136.78
39249
115.60
39250
115.16
39251
185.25
39252
55.83
39253
83.76
39254
28.32
39255
60.92
39256
65.62
39257
150.53
39258
50.90
39259
103.69
39260
25.26
39261
60.49
39262
194.33
39263
35.69
39264
79.21
39265
157.23
39266
97.54
39267
131.68
39268
159.78
39269
34.71
39270
42.27
39271
113.94
39272
23.50
39273
39.47
39274
35.92
39275
21.77
39276 rows × 1 columns
In [9]:
df['hour_index'] = df['hour']
df['minute_index'] = df['minute']
df = df.set_index(['date', 'hour_index', 'minute_index'])
Out[9]:
datetime
intersection_id
tollgate_id
hour
minute
dayofweek
avg_travel_time
date
hour_index
minute_index
2016-07-19
0
0
2016-07-19 00:00:00
A
2
0
0
1
37.09
0
2016-07-19 00:00:00
A
3
0
0
1
35.27
0
2016-07-19 00:00:00
B
1
0
0
1
15.58
0
2016-07-19 00:00:00
B
3
0
0
1
67.81
0
2016-07-19 00:00:00
C
1
0
0
1
8.36
0
2016-07-19 00:00:00
C
3
0
0
1
17.12
20
2016-07-19 00:20:00
A
2
0
20
1
42.64
20
2016-07-19 00:20:00
A
3
0
20
1
77.61
20
2016-07-19 00:20:00
B
1
0
20
1
10.38
20
2016-07-19 00:20:00
B
3
0
20
1
25.51
20
2016-07-19 00:20:00
C
1
0
20
1
24.81
20
2016-07-19 00:20:00
C
3
0
20
1
11.66
40
2016-07-19 00:40:00
A
2
0
40
1
40.17
40
2016-07-19 00:40:00
A
3
0
40
1
29.30
40
2016-07-19 00:40:00
B
1
0
40
1
13.44
40
2016-07-19 00:40:00
B
3
0
40
1
167.55
40
2016-07-19 00:40:00
C
1
0
40
1
20.74
40
2016-07-19 00:40:00
C
3
0
40
1
7.62
1
0
2016-07-19 01:00:00
A
2
1
0
1
39.12
0
2016-07-19 01:00:00
A
3
1
0
1
93.09
0
2016-07-19 01:00:00
B
1
1
0
1
17.06
0
2016-07-19 01:00:00
B
3
1
0
1
36.47
0
2016-07-19 01:00:00
C
1
1
0
1
14.96
0
2016-07-19 01:00:00
C
3
1
0
1
17.62
20
2016-07-19 01:20:00
A
2
1
20
1
41.92
20
2016-07-19 01:20:00
A
3
1
20
1
32.12
20
2016-07-19 01:20:00
B
1
1
20
1
11.06
20
2016-07-19 01:20:00
B
3
1
20
1
31.36
20
2016-07-19 01:20:00
C
1
1
20
1
13.87
20
2016-07-19 01:20:00
C
3
1
20
1
11.76
...
...
...
...
...
...
...
...
...
...
2016-10-17
20
20
2016-10-17 20:20:00
A
2
20
20
0
50.18
20
2016-10-17 20:20:00
A
3
20
20
0
71.97
20
2016-10-17 20:20:00
B
1
20
20
0
136.78
20
2016-10-17 20:20:00
B
3
20
20
0
115.60
20
2016-10-17 20:20:00
C
1
20
20
0
115.16
20
2016-10-17 20:20:00
C
3
20
20
0
185.25
40
2016-10-17 20:40:00
A
2
20
40
0
55.83
40
2016-10-17 20:40:00
A
3
20
40
0
83.76
40
2016-10-17 20:40:00
B
1
20
40
0
28.32
40
2016-10-17 20:40:00
B
3
20
40
0
60.92
40
2016-10-17 20:40:00
C
1
20
40
0
65.62
40
2016-10-17 20:40:00
C
3
20
40
0
150.53
21
0
2016-10-17 21:00:00
A
2
21
0
0
50.90
0
2016-10-17 21:00:00
A
3
21
0
0
103.69
0
2016-10-17 21:00:00
B
1
21
0
0
25.26
0
2016-10-17 21:00:00
B
3
21
0
0
60.49
0
2016-10-17 21:00:00
C
1
21
0
0
194.33
0
2016-10-17 21:00:00
C
3
21
0
0
35.69
20
2016-10-17 21:20:00
A
2
21
20
0
79.21
20
2016-10-17 21:20:00
A
3
21
20
0
157.23
20
2016-10-17 21:20:00
B
1
21
20
0
97.54
20
2016-10-17 21:20:00
B
3
21
20
0
131.68
20
2016-10-17 21:20:00
C
1
21
20
0
159.78
20
2016-10-17 21:20:00
C
3
21
20
0
34.71
40
2016-10-17 21:40:00
A
2
21
40
0
42.27
40
2016-10-17 21:40:00
A
3
21
40
0
113.94
40
2016-10-17 21:40:00
B
1
21
40
0
23.50
40
2016-10-17 21:40:00
B
3
21
40
0
39.47
40
2016-10-17 21:40:00
C
1
21
40
0
35.92
40
2016-10-17 21:40:00
C
3
21
40
0
21.77
39276 rows × 7 columns
In [31]:
df[['intersection_id', 'tollgate_id', 'hour', 'minute', 'dayofweek', 'avg_travel_time']]
Out[31]:
intersection_id
tollgate_id
hour
minute
dayofweek
avg_travel_time
date
hour_index
minute_index
2016-07-19
0
0
A
2
0
0
1
37.09
0
A
3
0
0
1
35.27
0
B
1
0
0
1
15.58
0
B
3
0
0
1
67.81
0
C
1
0
0
1
8.36
0
C
3
0
0
1
17.12
20
A
2
0
20
1
42.64
20
A
3
0
20
1
77.61
20
B
1
0
20
1
10.38
20
B
3
0
20
1
25.51
20
C
1
0
20
1
24.81
20
C
3
0
20
1
11.66
40
A
2
0
40
1
40.17
40
A
3
0
40
1
29.30
40
B
1
0
40
1
13.44
40
B
3
0
40
1
167.55
40
C
1
0
40
1
20.74
40
C
3
0
40
1
7.62
1
0
A
2
1
0
1
39.12
0
A
3
1
0
1
93.09
0
B
1
1
0
1
17.06
0
B
3
1
0
1
36.47
0
C
1
1
0
1
14.96
0
C
3
1
0
1
17.62
20
A
2
1
20
1
41.92
20
A
3
1
20
1
32.12
20
B
1
1
20
1
11.06
20
B
3
1
20
1
31.36
20
C
1
1
20
1
13.87
20
C
3
1
20
1
11.76
...
...
...
...
...
...
...
...
...
2016-10-17
20
20
A
2
20
20
0
50.18
20
A
3
20
20
0
71.97
20
B
1
20
20
0
136.78
20
B
3
20
20
0
115.60
20
C
1
20
20
0
115.16
20
C
3
20
20
0
185.25
40
A
2
20
40
0
55.83
40
A
3
20
40
0
83.76
40
B
1
20
40
0
28.32
40
B
3
20
40
0
60.92
40
C
1
20
40
0
65.62
40
C
3
20
40
0
150.53
21
0
A
2
21
0
0
50.90
0
A
3
21
0
0
103.69
0
B
1
21
0
0
25.26
0
B
3
21
0
0
60.49
0
C
1
21
0
0
194.33
0
C
3
21
0
0
35.69
20
A
2
21
20
0
79.21
20
A
3
21
20
0
157.23
20
B
1
21
20
0
97.54
20
B
3
21
20
0
131.68
20
C
1
21
20
0
159.78
20
C
3
21
20
0
34.71
40
A
2
21
40
0
42.27
40
A
3
21
40
0
113.94
40
B
1
21
40
0
23.50
40
B
3
21
40
0
39.47
40
C
1
21
40
0
35.92
40
C
3
21
40
0
21.77
39276 rows × 6 columns
In [57]:
sns.pairplot(df, x_vars=feature_cols, y_vars=['avg_travel_time'], size=6, aspect=1.3, kind='reg')
Out[57]:
<seaborn.axisgrid.PairGrid at 0x1f93e78eb00>
In [43]:
# not working!?!?!
import src.misc.split_train_valid as split
#training, validation, testing = split.split_dataset(x_df, 0.8, 0)
In [32]:
from sklearn.model_selection import train_test_split
feature_cols = ['hour', 'minute', 'dayofweek']
x_train, x_test, y_train, y_test = train_test_split(df[feature_cols], df['avg_travel_time'], test_size=0.2, random_state=42)
# k-fold cross validation
# 13 weeks
In [36]:
from sklearn import linear_model
# Linear
linreg = linear_model.LinearRegression()
linreg.fit(x_train, y_train)
print(linreg.intercept_)
print(linreg.coef_)
print(list(zip(feature_cols, linreg.coef_)))
90.4505541547
[ 0.5378391 -0.01347068 0.37377118]
[('hour', 0.53783909947222686), ('minute', -0.013470675331621032), ('dayofweek', 0.37377118090272132)]
In [40]:
y_pred = linreg.predict(x_test)
y_pred
Out[40]:
array([ 91.52524444, 99.57974395, 99.32440534, ..., 100.22194072,
103.77711115, 98.02494898])
In [39]:
from sklearn import metrics
print(metrics.mean_squared_error(y_test, y_pred))
4054.16945765
In [47]:
results = []
for i in range(10):
x_train, x_test, y_train, y_test = train_test_split(df[feature_cols], df['avg_travel_time'], test_size=0.2)
# Linear
linreg = linear_model.LinearRegression()
linreg.fit(x_train, y_train)
y_pred = linreg.predict(x_test)
rmse = metrics.mean_squared_error(y_test, y_pred)
res = [linreg.intercept_, linreg.coef_, rmse]
results.append(res)
pd.DataFrame(results, columns=['intercept_', 'coef_', 'rmse'])
Out[47]:
intercept_
coef_
rmse
0
89.900135
[0.553162415068, -0.0109416038171, 0.449450448...
4293.120405
1
90.406761
[0.544382755867, -0.0430806912813, 0.534094441...
3753.681408
2
90.564282
[0.537184781523, -0.0250889884854, 0.430037729...
4126.072963
3
90.796235
[0.508025303115, -0.0101932285518, 0.296625425...
3893.368448
4
90.548492
[0.558682850336, -0.0364820538643, 0.378925613...
3913.769467
5
89.772504
[0.544020384939, -0.0123248005422, 0.529592140...
4002.785057
6
90.489136
[0.560705266648, -0.0279377386041, 0.46599922408]
3636.617045
7
90.522800
[0.52685064344, -0.0234596604812, 0.455429051707]
3745.868425
8
90.779581
[0.499510477393, -0.0129419222283, 0.371548692...
4643.868031
9
90.035448
[0.523470833998, -0.00737534488841, 0.43789263...
3878.608227
In [71]:
# using k=13 -> 13 weeks
from sklearn.model_selection import cross_val_score
for i in range(2,42):
linreg = linear_model.LinearRegression()
scores = cross_val_score(linreg, df[feature_cols], df['avg_travel_time'], cv=i)
print(i, scores.mean())
2 -0.0136550778135
3 -0.00451698571477
4 -0.0172182283394
5 -0.0115886937688
6 -0.0128614033172
7 -0.0192007563099
8 -0.0125629832363
9 -0.0205520178453
10 -0.0121324560931
11 -0.0206496967275
12 -0.0178696265426
13 -0.0161331753932
14 -0.0187423881844
15 -0.015802700029
16 -0.0214825039539
17 -0.0194334498526
18 -0.0209669981238
19 -0.0190266464005
20 -0.0232651993636
21 -0.0192234925935
22 -0.0214332542837
23 -0.0222366779215
24 -0.0209497753024
25 -0.0231583992857
26 -0.0206221891042
27 -0.0256733750917
28 -0.0205605843793
29 -0.0240059211337
30 -0.0204546432879
31 -0.0231928180191
32 -0.0221112534617
33 -0.024614620124
34 -0.0258157286425
35 -0.0231218872723
36 -0.0294662828878
37 -0.0266213378535
38 -0.0258434982667
39 -0.0256765723216
40 -0.0265690909442
41 -0.0245131508336
Content source: Superchicken1/SambaFlow
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