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feature2.py
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# 制作第二个特征集
import pandas as pd
import os
from tqdm import *
path="new_data/"
data_list = os.listdir(path+'train/')
file_name='mydata/data_all_n2_d5_train.csv'
df = pd.read_csv(path+'train/'+ data_list[0])
df.drop(df[df['活塞工作时长']>150].index) ##50
df.drop(df[ (df['发动机转速']>10000) ].index)
df.drop(df[ (df['油泵转速']>15000) ].index)
#df.drop(df[ df['泵送压力']>450 ].index) ##>0
df.drop(df[ df['排量电流']>50000 ].index) ##
df['t1']=df['发动机转速']*df['分配压力'] #
df['t2']=df['发动机转速']*df['泵送压力'] #
df['t3']=df['搅拌超压信号']/df['活塞工作时长'] ##
df['t4']=df['分配压力']/df['泵送压力'] ##
df['t5']=df['油泵转速']*df['泵送压力'] ##
df['t6']=df['排量电流'] /df['流量档位'] ###每?
df['t7']=[ 0 if x<85 and x>0 else 1 for x in df['液压油温'] ] #
df['sample_file_name'] = data_list[0]
df.to_csv(file_name, index=False,encoding='utf-8')
for i in tqdm(range(1, len(data_list))):
if data_list[i].split('.')[-1] == 'csv':
df = pd.read_csv(path+'train/' + data_list[i])
df.drop(df[df['活塞工作时长']>150].index) ##50
df.drop(df[ (df['发动机转速']>10000) ].index)
df.drop(df[ (df['油泵转速']>15000) ].index)
#df.drop(df[ df['泵送压力']>450 ].index) ##>0
df.drop(df[ df['排量电流']>50000 ].index) ##由训练数据图表观察到异常
df['t1']=df['发动机转速']*df['分配压力'] #（暂时为出口流量？）电机的功率也将越大
df['t2']=df['发动机转速']*df['泵送压力'] #？？
df['t3']=df['搅拌超压信号']/df['活塞工作时长'] ##出错机率
df['t4']=df['分配压力']/df['泵送压力'] ##两者什么关系？
df['t5']=df['油泵转速']*df['泵送压力'] ##qt=nV 式中n-一液压油泵的转速;V一一液压油泵的排量
df['t6']=df['排量电流'] /df['流量档位'] ###每?
df['t7']=[ 0 if x<85 and x>0 else 1 for x in df['液压油温'] ] #60度左右，一般不会超过85度，坏
df['sample_file_name'] = data_list[i]
df.to_csv(file_name, index=False, header=False, mode='a+',encoding='utf-8')
else:
continue
# In[5]:
import pandas as pd
import os
from tqdm import *
path="new_data/"
test_data_list = os.listdir(path+'test/')
file_name='mydata/data_all_n2_d5_test.csv'
df = pd.read_csv(path+'test/'+ test_data_list[0])
df.drop(df[df['活塞工作时长']>150].index) ##50
df.drop(df[ (df['发动机转速']>10000) ].index)
df.drop(df[ (df['油泵转速']>15000) ].index)
#df.drop(df[ df['泵送压力']>450 ].index) ##>0
df.drop(df[ df['排量电流']>50000 ].index) ##由训练数据图表观察到异常
df['t1']=df['发动机转速']*df['分配压力'] #（暂时为出口流量？）电机的功率也将越大
df['t2']=df['发动机转速']*df['泵送压力'] #？？
df['t3']=df['搅拌超压信号']/df['活塞工作时长'] ##出错机率
df['t4']=df['分配压力']/df['泵送压力'] ##两者什么关系？
df['t5']=df['油泵转速']*df['泵送压力'] ##qt=nV 式中n-一液压油泵的转速;V一一液压油泵的排量
df['t6']=df['排量电流'] /df['流量档位'] ###每?
df['t7']=[ 0 if x<85 and x>0 else 1 for x in df['液压油温'] ] #60度左右，一般不会超过85度，坏
df['sample_file_name'] = data_list[0]
df.to_csv(file_name, index=False,encoding='utf-8')
for i in tqdm(range(len(test_data_list))):
if test_data_list[i].split('.')[-1] == 'csv':
df = pd.read_csv(path+'test/' + test_data_list[i])
df.drop(df[df['活塞工作时长']>150].index) ##50
df.drop(df[ (df['发动机转速']>10000) ].index)
df.drop(df[ (df['油泵转速']>15000) ].index)
#df.drop(df[ df['泵送压力']>450 ].index) ##>0
df.drop(df[ df['排量电流']>50000 ].index) ##由训练数据图表观察到异常
df['t1']=df['发动机转速']*df['分配压力'] #（暂时为出口流量？）电机的功率也将越大
df['t2']=df['发动机转速']*df['泵送压力'] #？？
df['t3']=df['搅拌超压信号']/df['活塞工作时长'] ##出错机率
df['t4']=df['分配压力']/df['泵送压力'] ##两者什么关系？
df['t5']=df['油泵转速']*df['泵送压力'] ##qt=nV 式中n-一液压油泵的转速;V一一液压油泵的排量
df['t6']=df['排量电流'] /df['流量档位'] ###每?
df['t7']=[ 0 if x<85 and x>0 else 1 for x in df['液压油温'] ] #60度左右，一般不会超过85度，坏
df['sample_file_name'] = test_data_list[i]
df.to_csv(file_name, index=False, header=False, mode='a+',encoding='utf-8')
else:
continue
# In[7]:
dtypes_col=['活塞工作时长',
'发动机转速',
'油泵转速',
'泵送压力',
'液压油温',
'流量档位',
'分配压力',
'排量电流',
'低压开关',
'高压开关',
'搅拌超压信号'
,'正泵'
,'反泵'
# ,'设备类型'
]
dtypes_type = []
for i in range(len(dtypes_col)):
dtypes_type.append('float16')
column_types = dict(zip(dtypes_col, dtypes_type))
# column_types['设备类型']='int16'
# column_types['低压开关']='int8'
# column_types['高压开关']='int8'
# column_types['搅拌超压信号']='int8'
column_types['正泵']='int8'
column_types['反泵']='int8'
train = pd.read_csv('mydata/data_all_n2_d5_train.csv',dtype=column_types)
test = pd.read_csv('mydata/data_all_n2_d5_test.csv',dtype=column_types)
sample = pd.read_csv('new_data/submit_example.csv')
# In[11]:
train.columns
# In[17]:
train['设备类型'] = datashebei_train
test['设备类型'] = datashebei_test
# In[18]:
from sklearn import preprocessing
enc = preprocessing.LabelEncoder()
categorical_columns = ['设备类型']
for f in categorical_columns:
# try:
data =pd.concat([train,test],sort=False)
enc.fit(data[f].values.reshape(-1, 1))
train[f] = enc.fit_transform(train[f])
test[f] = enc.fit_transform(test[f])
"save device "
datashebei_train = train[f]
datashebei_test = test[f]
# In[19]:
def signal_is_1(x):
return np.sum(x == 1)
def signal_is_0(x):
return np.sum(x == 0)
from scipy import signal
def peak_num_1(x):
return len(signal.find_peaks_cwt(x, np.arange(1,50)))
def peak_num_2(x):
return len(signal.find_peaks_cwt(x, np.arange(1,2)))
# In[20]:
def process(data):
data_all = data['sample_file_name'].groupby(data['sample_file_name']).count().rename('长度')
data_all = pd.DataFrame(data_all)
data_shebeileixing = data.groupby(data['sample_file_name']).mean()['设备类型']
data.drop('设备类型',axis=1,inplace=True)
is_01_feature =['低压开关', '高压开关', '搅拌超压信号', '正泵', '反泵','sample_file_name']
need_groupby_fea = [f for f in data.columns if f not in is_01_feature]
print('mean')
data_all = pd.concat([data_all,data.groupby(data['sample_file_name']).mean().rename(columns=lambda x:x+'_mean')],axis=1)
print('max')
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])[need_groupby_fea].max().rename(columns=lambda x:x+'_max')],axis=1)
print('min')
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])[need_groupby_fea].min().rename(columns=lambda x:x+'_min')],axis=1)
print('sum')
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])[need_groupby_fea].sum().rename(columns=lambda x:x+'_sum')],axis=1)
print('std')
data_all = pd.concat([data_all,data.groupby(data['sample_file_name']).std().rename(columns=lambda x:x+'_std')],axis=1)
print('median')
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])[need_groupby_fea].median().rename(columns=lambda x:x+'_median')],axis=1)
"统计信号"
print("统计信号")
print("计算信号==1")
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])['低压开关', '高压开关', '搅拌超压信号', '正泵', '反泵'].agg(signal_is_1).rename(columns=lambda x:x+'_signal_is_1')],axis=1)
print("计算信号==0")
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])['低压开关', '高压开关', '搅拌超压信号', '正泵', '反泵'].agg(signal_is_0).rename(columns=lambda x:x+'_signal_is_0')],axis=1)
print("peak_num_1")
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])['搅拌超压信号'].agg(peak_num_1).rename(columns=lambda x:x+'_peak_num_1')],axis=1)
print("peak_num_2")
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])['正泵','反泵'].agg(lambda x: len(signal.find_peaks_cwt(x, [1,2]))).rename(columns=lambda x:x+'_peak_num_2')],axis=1)
print("peak_num_3")
data_all = pd.concat([data_all,data.groupby(data['sample_file_name'])['发动机转速', '泵送压力', '液压油温', '流量档位', '分配压力', '排量电流',].agg(lambda x: len(signal.find_peaks_cwt(x, [1,2,3,4,5,6,7,8,9,10]))).rename(columns=lambda x:x+'_peak_num_2')],axis=1)
print('设备类型')
data_all = pd.concat([data_all,data_shebeileixing],axis=1)
print('feature over.................')
print("加减乘除特征")
data_all['活塞工作时长/长度'] = data_all['活塞工作时长_mean'] / data_all['长度']
data_all['油泵转速/长度'] = data_all['油泵转速_mean'] / data_all['长度']
data_all["f0"]= data_all["发动机转速_mean"]-data_all["油泵转速_mean"]
data_all["f2"] = data_all['正泵_signal_is_1'] * data_all['长度']
data_all["f5"] = data_all['活塞工作时长_mean'] * data_all['长度']
data_all["f6"] = (data_all['正泵_signal_is_1']+data_all['反泵_signal_is_1'])/data_all['长度']
data_all["f7"]= data_all["f6"]*data_all['活塞工作时长_mean']
return data_all
data_train = process(train)
data_test = process(test)
# In[21]:
data_train.to_csv('mydata/data_train_kaiyaun.csv')
data_test.to_csv('mydata/data_test_kaiyaun.csv',index=None)