-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathdata_preprocessing.py
222 lines (168 loc) · 10.8 KB
/
data_preprocessing.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
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
import numpy as np
import pandas as pd
#from numpy import round
class PreProcess:
def __init__(self, df_train, df_test, run_train):
self.df_train = df_train
self.df_test = df_test
self.run_train = run_train
def data_prepro(self):
if self.run_train:
# TRAIN dataset
self.df_train = self.df_train[self.df_train['ghi']!=0]
self.df_train['Kt'] = self.df_train['dw_solar']/self.df_train['ghi']
self.df_train.reset_index(inplace=True)
print("train Kt max: "+str(self.df_train['Kt'].max()))
print("train Kt min: "+str(self.df_train['Kt'].min()))
print("train Kt mean: "+str(self.df_train['Kt'].mean()))
# TEST dataset
self.df_test = self.df_test[self.df_test['ghi']!=0]
self.df_test['Kt'] = self.df_test['dw_solar']/self.df_test['ghi']
self.df_test.reset_index(inplace=True)
print("test Kt max: "+str(self.df_test['Kt'].max()))
print("test Kt min: "+str(self.df_test['Kt'].min()))
print("test Kt mean: "+str(self.df_test['Kt'].mean()))
if self.run_train:
# TRAIN dataset
self.df_train= self.df_train[self.df_train['Kt']< 5000]
self.df_train= self.df_train[self.df_train['Kt']> -1000]
# Test dataset
self.df_test= self.df_test[self.df_test['Kt']< 5000]
self.df_test= self.df_test[self.df_test['Kt']> -1000]
### Making 4 Kt columns
if self.run_train:
# Train dataset
self.df_train['Kt_2'] = self.df_train['Kt']
self.df_train['Kt_3'] = self.df_train['Kt']
self.df_train['Kt_4'] = self.df_train['Kt']
# Test dataset
self.df_test['Kt_2'] = self.df_test['Kt']
self.df_test['Kt_3'] = self.df_test['Kt']
self.df_test['Kt_4'] = self.df_test['Kt']
#### Group the data (train dataframe)
if self.run_train:
zen = self.df_train.groupby(['year','month','day','hour'])['zen'].mean()
dw_solar = self.df_train.groupby(['year','month','day','hour'])['dw_solar'].mean()
uw_solar = self.df_train.groupby(['year','month','day','hour'])['uw_solar'].mean()
direct_n = self.df_train.groupby(['year','month','day','hour'])['direct_n'].mean()
diffuse = self.df_train.groupby(['year','month','day','hour'])['diffuse'].mean()
dw_ir = self.df_train.groupby(['year','month','day','hour'])['dw_ir'].mean()
dw_casetemp = self.df_train.groupby(['year','month','day','hour'])['dw_casetemp'].mean()
dw_dometemp = self.df_train.groupby(['year','month','day','hour'])['dw_dometemp'].mean()
uw_ir = self.df_train.groupby(['year','month','day','hour'])['uw_ir'].mean()
uw_casetemp = self.df_train.groupby(['year','month','day','hour'])['uw_casetemp'].mean()
uw_dometemp = self.df_train.groupby(['year','month','day','hour'])['uw_dometemp'].mean()
uvb = self.df_train.groupby(['year','month','day','hour'])['uvb'].mean()
par = self.df_train.groupby(['year','month','day','hour'])['par'].mean()
netsolar = self.df_train.groupby(['year','month','day','hour'])['netsolar'].mean()
netir = self.df_train.groupby(['year','month','day','hour'])['netir'].mean()
totalnet = self.df_train.groupby(['year','month','day','hour'])['totalnet'].mean()
temp = self.df_train.groupby(['year','month','day','hour'])['temp'].mean()
rh = self.df_train.groupby(['year','month','day','hour'])['rh'].mean()
windspd = self.df_train.groupby(['year','month','day','hour'])['windspd'].mean()
winddir = self.df_train.groupby(['year','month','day','hour'])['winddir'].mean()
pressure = self.df_train.groupby(['year','month','day','hour'])['pressure'].mean()
ghi = self.df_train.groupby(['year','month','day','hour'])['ghi'].mean()
Kt = self.df_train.groupby(['year','month','day','hour'])['Kt'].mean()
Kt_2 = self.df_train.groupby(['year','month','day','hour'])['Kt_2'].mean()
Kt_3 = self.df_train.groupby(['year','month','day','hour'])['Kt_3'].mean()
Kt_4 = self.df_train.groupby(['year','month','day','hour'])['Kt_4'].mean()
df_new_train = pd.concat([zen,dw_solar,uw_solar,direct_n,diffuse,dw_ir,dw_casetemp,dw_dometemp,uw_ir,uw_casetemp,uw_dometemp,
uvb,par,netsolar,netir,totalnet,temp,rh,windspd,winddir,pressure,ghi,Kt,Kt_2,Kt_3,Kt_4], axis=1)
#### Groupdata - test dataframe
test_zen = self.df_test.groupby(['month','day','hour'])['zen'].mean()
test_dw_solar = self.df_test.groupby(['month','day','hour'])['dw_solar'].mean()
test_uw_solar = self.df_test.groupby(['month','day','hour'])['uw_solar'].mean()
test_direct_n = self.df_test.groupby(['month','day','hour'])['direct_n'].mean()
test_diffuse = self.df_test.groupby(['month','day','hour'])['diffuse'].mean()
test_dw_ir = self.df_test.groupby(['month','day','hour'])['dw_ir'].mean()
test_dw_casetemp = self.df_test.groupby(['month','day','hour'])['dw_casetemp'].mean()
test_dw_dometemp = self.df_test.groupby(['month','day','hour'])['dw_dometemp'].mean()
test_uw_ir = self.df_test.groupby(['month','day','hour'])['uw_ir'].mean()
test_uw_casetemp = self.df_test.groupby(['month','day','hour'])['uw_casetemp'].mean()
test_uw_dometemp = self.df_test.groupby(['month','day','hour'])['uw_dometemp'].mean()
test_uvb = self.df_test.groupby(['month','day','hour'])['uvb'].mean()
test_par = self.df_test.groupby(['month','day','hour'])['par'].mean()
test_netsolar = self.df_test.groupby(['month','day','hour'])['netsolar'].mean()
test_netir = self.df_test.groupby(['month','day','hour'])['netir'].mean()
test_totalnet = self.df_test.groupby(['month','day','hour'])['totalnet'].mean()
test_temp = self.df_test.groupby(['month','day','hour'])['temp'].mean()
test_rh = self.df_test.groupby(['month','day','hour'])['rh'].mean()
test_windspd = self.df_test.groupby(['month','day','hour'])['windspd'].mean()
test_winddir = self.df_test.groupby(['month','day','hour'])['winddir'].mean()
test_pressure = self.df_test.groupby(['month','day','hour'])['pressure'].mean()
test_ghi = self.df_test.groupby(['month','day','hour'])['ghi'].mean()
test_Kt = self.df_test.groupby(['month','day','hour'])['Kt'].mean()
test_Kt_2 = self.df_test.groupby(['month','day','hour'])['Kt_2'].mean()
test_Kt_3 = self.df_test.groupby(['month','day','hour'])['Kt_3'].mean()
test_Kt_4 = self.df_test.groupby(['month','day','hour'])['Kt_4'].mean()
df_new_test = pd.concat([test_zen,test_dw_solar,test_uw_solar,test_direct_n,test_diffuse,test_dw_ir,
test_dw_casetemp,test_dw_dometemp,test_uw_ir,test_uw_casetemp,test_uw_dometemp,
test_uvb,test_par,test_netsolar,test_netir,test_totalnet,test_temp,test_rh,
test_windspd,test_winddir,test_pressure,test_ghi,test_Kt,test_Kt_2,test_Kt_3,test_Kt_4], axis=1)
### Shifting Kt values to make 1 hour, 2 hour, 3 hour and 4 hour ahead forecast
#### Train dataset
if self.run_train:
levels_index= []
for m in df_new_train.index.levels:
levels_index.append(m)
for i in levels_index[0]:
for j in levels_index[1]:
df_new_train.loc[i].loc[j]['Kt'] = df_new_train.loc[i].loc[j]['Kt'].shift(-1)
df_new_train.loc[i].loc[j]['Kt_2'] = df_new_train.loc[i].loc[j]['Kt_2'].shift(-2)
df_new_train.loc[i].loc[j]['Kt_3'] = df_new_train.loc[i].loc[j]['Kt_3'].shift(-3)
df_new_train.loc[i].loc[j]['Kt_4'] = df_new_train.loc[i].loc[j]['Kt_4'].shift(-4)
df_new_train = df_new_train[~(df_new_train['Kt_4'].isnull())]
#### Test dataset
levels_index2= []
for m in df_new_test.index.levels:
levels_index2.append(m)
# for i in levels_index2[0]:
# for j in levels_index2[1][:-3]:
# df_new_test.loc[i].loc[j]['Kt'] = df_new_test.loc[i].loc[j]['Kt'].shift(-1)
# df_new_test.loc[i].loc[j]['Kt_2'] = df_new_test.loc[i].loc[j]['Kt_2'].shift(-2)
# df_new_test.loc[i].loc[j]['Kt_3'] = df_new_test.loc[i].loc[j]['Kt_3'].shift(-3)
# df_new_test.loc[i].loc[j]['Kt_4'] = df_new_test.loc[i].loc[j]['Kt_4'].shift(-4)
for i, j in zip(levels_index2[0], levels_index2[1]):
try:
df_new_test.loc[i].loc[j]['Kt'] = df_new_test.loc[i].loc[j]['Kt'].shift(-1)
df_new_test.loc[i].loc[j]['Kt_2'] = df_new_test.loc[i].loc[j]['Kt_2'].shift(-2)
df_new_test.loc[i].loc[j]['Kt_3'] = df_new_test.loc[i].loc[j]['Kt_3'].shift(-3)
df_new_test.loc[i].loc[j]['Kt_4'] = df_new_test.loc[i].loc[j]['Kt_4'].shift(-4)
except KeyError:
continue
df_new_test = df_new_test[~(df_new_test['Kt_4'].isnull())]
### Normalize train and test dataframe
if self.run_train:
# TRAIN set
train_norm = (df_new_train - df_new_train.mean()) / (df_new_train.max() - df_new_train.min())
train_norm.reset_index(inplace=True,drop=True)
# TEST set
test_norm = (df_new_test - df_new_test.mean()) / (df_new_test.max() - df_new_test.min())
test_norm.reset_index(inplace=True,drop=True)
### Making train and test sets with train_norm and test_norm
import math
def roundup(x):
return int(math.ceil(x / 100.0)) * 100
if self.run_train:
# TRAIN set
train_lim = roundup(train_norm.shape[0])
train_random = train_norm.sample(train_lim-train_norm.shape[0])
train_norm = train_norm.append(train_random)
X1 = train_norm.drop(['Kt','Kt_2','Kt_3','Kt_4'],axis=1)
y1 = train_norm[['Kt','Kt_2','Kt_3','Kt_4']]
print("X1_train shape is {}".format(X1.shape))
print("y1_train shape is {}".format(y1.shape))
X_train = np.array(X1)
y_train = np.array(y1)
# TEST set
test_lim = roundup(test_norm.shape[0])
test_random = test_norm.sample(test_lim-test_norm.shape[0])
test_norm = test_norm.append(test_random)
X2 = test_norm.drop(['Kt','Kt_2','Kt_3','Kt_4'],axis=1)
y2 = test_norm[['Kt','Kt_2','Kt_3','Kt_4']]
print("X2_test shape is {}".format(X2.shape))
print("y2_test shape is {}".format(y2.shape))
X_test = np.array(X2)
y_test = np.array(y2)
return X_train, y_train, X_test, y_test, df_new_test