-
Notifications
You must be signed in to change notification settings - Fork 12
/
Copy pathgetFeature.py
162 lines (117 loc) · 5.22 KB
/
getFeature.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
# coding: utf-8
# ### 引入相关包
# 版本说明：
# python3.6.5
# conda 4.5.4
# In[1]:
import zipfile
import pandas as pd
# ### 路径说明 windows
# 引入文件路径
# 当前文件夹下面的data文件夹存放了训练集和测试集以及标注文件
# In[2]:
"""
trainZip 使用zipfile,将训练集读入
testZip 使用zipfile,将测试集读入
columnsNames 文件列名
"""
trainZip = zipfile.ZipFile('./data/train.zip')
testZip = zipfile.ZipFile('./data/test.zip')
columnsNames = ['轮毂转速', '轮毂角度', '叶片1角度', '叶片2角度', '叶片3角度', '变桨电机1电流', '变桨电机2电流',
'变桨电机3电流', '超速传感器转速检测值', '5秒偏航对风平均值', 'x方向振动值', 'y方向振动值', '液压制动压力',
'机舱气象站风速', '风向绝对值', '大气压力', '无功功率控制状态', '变频器电网侧电流', '变频器电网侧电压',
'变频器电网侧有功功率', '变频器电网侧无功功率', '变频器发电机侧功率', '发电机运行频率', '发电机电流', '发电机转矩',
'变频器入口温度', '变频器出口温度', '变频器入口压力', '变频器出口压力', '发电机功率限幅值', '无功功率设定值',
'额定的轮毂转速', '测风塔环境温度', '发电机定子温度1', '发电机定子温度2', '发电机定子温度3', '发电机定子温度4',
'发电机定子温度5', '发电机定子温度6', '发电机空气温度1', '发电机空气温度2', '主轴承温度1', '主轴承温度2',
'轮毂温度', '轮毂控制柜温度', '机舱温度', '机舱控制柜温度', '变频器INU温度', '变频器ISU温度',
'变频器INU RMIO温度', '变桨电机1功率估算', '变桨电机2功率估算', '变桨电机3功率估算', '风机当前状态值',
'轮毂当前状态值', '偏航状态值', '偏航要求值', '叶片1电池箱温度', '叶片2电池箱温度', '叶片3电池箱温度',
'叶片1变桨电机温度', '叶片2变桨电机温度', '叶片3变桨电机温度', '叶片1变频器箱温度', '叶片2变频器箱温度',
'叶片3变频器箱温度', '叶片1超级电容电压', '叶片2超级电容电压', '叶片3超级电容电压', '驱动1晶闸管温度',
'驱动2晶闸管温度', '驱动3晶闸管温度', '驱动1输出扭矩', '驱动2输出扭矩', '驱动3输出扭矩']
# In[3]:
"""
获取压缩包里面的文件路径
"""
trainfileNameList = trainZip.namelist()
testfileNameList = testZip.namelist()
# In[4]:
"""
定义两个list 分别存放解析后的train和test的feature
"""
trainDFList = []
testDFList = []
# ### 构造训练集特征
# In[5]:
"""
循环遍历zip包里面的文件路径，解析到csv文件，然后读取里面的内容，构建相关特征
"""
for fname in trainfileNameList:
if fname.endswith('.csv'):
dict_f = {}
dict_f['file_name'] = fname.split('/')[-1]
f_info = trainZip.getinfo(fname)
fo = trainZip.open(f_info)
fDF = pd.read_csv(fo)
dfmean=fDF.mean()
dfmin = fDF.min()
dfmax = fDF.max()
dfstd = fDF.std()
fDF_diff = fDF.diff(1).drop(0,0)
df_diffmean= fDF_diff.mean()
df_diffmin = fDF_diff.min()
df_diffmax = fDF_diff.max()
df_diffstd = fDF_diff.std()
for col in columnsNames:
dict_f[col+'_mean'] = dfmean[col]
dict_f[col+'_diff_mean'] = df_diffmean[col]
dict_f[col+'_min'] = dfmin[col]
dict_f[col+'_diff_min'] = df_diffmin[col]
dict_f[col+'_max'] = dfmax[col]
dict_f[col+'_diff_max'] = df_diffmax[col]
dict_f[col+'_std'] = dfstd[col]
dict_f[col+'_diff_std'] = df_diffstd[col]
trainDFList.append(dict_f)
else:
continue
# ### 构造测试集特征
# In[6]:
for fname in testfileNameList:
if fname.endswith('.csv'):
dict_f = {}
dict_f['file_name'] = fname.split('/')[-1]
f_info = testZip.getinfo(fname)
fo = testZip.open(f_info)
fDF = pd.read_csv(fo)
dfmean=fDF.mean()
dfmin = fDF.min()
dfmax = fDF.max()
dfstd = fDF.std()
fDF_diff = fDF.diff(1).drop(0,0)
df_diffmean= fDF_diff.mean()
df_diffmin = fDF_diff.min()
df_diffmax = fDF_diff.max()
df_diffstd = fDF_diff.std()
for col in columnsNames:
dict_f[col+'_mean'] = dfmean[col]
dict_f[col+'_diff_mean'] = df_diffmean[col]
dict_f[col+'_min'] = dfmin[col]
dict_f[col+'_diff_min'] = df_diffmin[col]
dict_f[col+'_max'] = dfmax[col]
dict_f[col+'_diff_max'] = df_diffmax[col]
dict_f[col+'_std'] = dfstd[col]
dict_f[col+'_diff_std'] = df_diffstd[col]
testDFList.append(dict_f)
else:
continue
# In[7]:
"""
将list转换为DataFrame
"""
testDF = pd.DataFrame(testDFList)
trainDF = pd.DataFrame(trainDFList)
# ### 保存特征集
# In[8]:
trainDF.to_csv('./data/train_feature.csv',header=True,index=False)
testDF.to_csv('./data/test_feature.csv',header=True,index=False)