-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmyRF_2model_timediff.py
70 lines (53 loc) · 2.77 KB
/
myRF_2model_timediff.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
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
if __name__ == '__main__':
train_data = pd.read_csv('train.csv',skipinitialspace=1,index_col=0,parse_dates=True)
test_data = pd.read_csv('test.csv',skipinitialspace=1,index_col=0,parse_dates=True)
# adding year/month/day/hour as feature
train_data['hour']=train_data.index.hour
test_data['hour']=test_data.index.hour
train_data['month']=train_data.index.month
test_data['month']=test_data.index.month
train_data['day']=train_data.index.day
test_data['day']=test_data.index.day
train_data['year']=train_data.index.year
test_data['year']=test_data.index.year
# add a column of time difference
train_data['time'] = train_data.index
test_data['time'] = test_data.index
train_data['timeSince']=train_data.time-train_data.time[0]
test_data['timeSince']=test_data.time-test_data.time[0]
train_data['timeSince']=train_data['timeSince'].apply(lambda x: x / np.timedelta64(1,'D'))
test_data['timeSince']=test_data['timeSince'].apply(lambda x: x / np.timedelta64(1,'D'))
# separate the data into working or non-working day models
train_wd = train_data[train_data['workingday']==1]
train_nwd = train_data[train_data['workingday']==0]
test_wd = test_data[test_data['workingday']==1]
test_nwd = test_data[test_data['workingday']==0]
# selected column names
selected_cols = [u'season', u'holiday', u'workingday', u'weather', u'temp', u'atemp',u'humidity', u'windspeed',u'hour',u'month',u'year',u'timeSince']
X_train_wd, y_train_wd = train_wd[selected_cols], train_wd['count']
X_train_nwd, y_train_nwd = train_nwd[selected_cols], train_nwd['count']
#params = {'n_estimators': 500, 'max_depth': 3, 'min_samples_split': 1,'learning_rate': 0.01, 'loss': 'ls'}
clf_wd = RandomForestRegressor(n_estimators=200)
clf_nwd = RandomForestRegressor(n_estimators=200)
clf_wd.fit(X_train_wd, y_train_wd)
clf_nwd.fit(X_train_nwd, y_train_nwd)
train_wd['prediction'] = clf_wd.predict(X_train_wd)
train_nwd['prediction'] = clf_nwd.predict(X_train_nwd)
train_data = train_wd.append(train_nwd)
# round to closest integer
train_data['prediction'].apply(round)
if(any(train_data['prediction']<0.0)):
train_data[train_data['prediction']<0.0] = 0.0
mse = mean_squared_error(train_data['count'], train_data['prediction'])
print("MSE: %.4f" % mse)
test_wd['prediction'] = clf_wd.predict(test_wd[selected_cols])
test_nwd['prediction'] = clf_nwd.predict(test_nwd[selected_cols])
test_data = test_wd.append(test_nwd)
test_data['prediction'].apply(round)
if(any(test_data['prediction']<0.0)):
test_data[test_data['prediction']<0.0] = 0.0
test_data['prediction'].to_csv('output.csv',header=['count'])