-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathconvert_traffic.py
64 lines (56 loc) · 3.82 KB
/
convert_traffic.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
from utils import *
import pandas as pd
def convert_traffic(input_temp):
# Define input features
# Distance features: 'distance_haversine', 'distance_dummy_manhattan'
# Speed features：'avg_speed_h'，'avg_speed_m', unit:m/s
# Time features: 'pick_up_h', 'pick_up_m', 'weekday', (0 represents Sunday)
# Zone features: 'pickup_lat_label', 'pickup_long_label', 'dropoff_lat_label', 'dropoff_long_label'
# Define distance features
input_temp.loc[:, 'distance_haversine'] = haversine_array(input_temp['pickup_latitude'].values,
input_temp['pickup_longitude'].values,
input_temp['dropoff_latitude'].values,
input_temp['dropoff_longitude'].values)
input_temp.loc[:, 'distance_dummy_manhattan'] = dummy_manhattan_distance(input_temp['pickup_latitude'].values,
input_temp['pickup_longitude'].values,
input_temp['dropoff_latitude'].values,
input_temp['dropoff_longitude'].values)
# Define speed features
input_temp.loc[:, 'avg_speed_h'] = 1000 * input_temp['distance_haversine'] / input_temp['trip_duration']
input_temp.loc[:, 'avg_speed_m'] = 1000 * input_temp['distance_dummy_manhattan'] / input_temp['trip_duration']
# Define time features
input_temp['pickup_datetime'] = pd.to_datetime(input_temp.pickup_datetime)
input_temp.loc[:, 'pickup_weekday'] = input_temp['pickup_datetime'].dt.weekday
input_temp.loc[:, 'pickup_hour'] = input_temp['pickup_datetime'].dt.hour
input_temp.loc[:, 'pickup_minute'] = input_temp['pickup_datetime'].dt.minute
# Define zone features
input_df, lat_min, long_min, lat_max, long_max = get_zone_features(input_temp)
input_df.loc[:, 'pickup_lat_label'] = np.round((input_df.pickup_latitude - lat_min) / 0.01)
input_df.loc[:, 'pickup_long_label'] = np.round((input_df.pickup_longitude - long_min) / 0.01)
input_df.loc[:, 'dropoff_lat_label'] = np.round((input_df.dropoff_latitude - lat_min) / 0.01)
input_df.loc[:, 'dropoff_long_label'] = np.round((input_df.dropoff_longitude - long_min) / 0.01)
# Standardize and one-hot encode the input data
x_data = pd.DataFrame(
columns=['id', 'vendor_id', 'distance_haversine', 'distance_dummy_manhattan', 'avg_speed_h', 'avg_speed_m',
'pickup_weekday', 'pickup_hour', 'pickup_minute', 'pickup_lat_label', 'pickup_long_label',
'dropoff_lat_label', 'dropoff_long_label'])
x_data_onehot = get_standard_onehot_x(x_data, input_df)
# One-hot encode the output data
y_data = output_preprocess(input_df)
y_data = y_data.drop('id', axis=1)
yyy_dict = y_dict()
y_data_list = [yyy_dict[int(x)] if x in yyy_dict else x for x in y_data['trip_duration'].values]
y_data_onehot = np.array(y_data_list)
y_row = np.size(y_data_onehot, 0)
y_col = np.size(y_data_onehot, 1)
# Split the data into training set, test set and validation set
x_train_tmp, x_test, y_train_tmp, y_test = train_test_split(x_data_onehot, y_data_onehot, test_size=0.2,
random_state=42)
x_train, x_val, y_train, y_val = train_test_split(x_train_tmp, y_train_tmp, test_size=0.2, random_state=55)
outputDict = {'x_train': x_train.tolist(),
'x_val': x_val.tolist(),
'x_test': x_test.tolist(),
'y_train': y_train.tolist(),
'y_val': y_val.tolist(),
'y_test': y_test.tolist()}
return outputDict