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testdata_feature_get.py
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# -*- coding: utf-8 -*-
"""
Created on 2022-04-06
Author ZhengRui
Co-author LongJianghua
"""
#用于特征提取
from cProfile import label
from operator import length_hint
import pandas as pd
import numpy as np
import os
from scipy import stats,signal,fftpack
import math
from pywt import wavedec
import traceback
import sys
params = {}
# testdataA
# params['testApath'] = '../dataset/testdataA.csv'
# params['testAopath'] = '../datafeature/testdataA_feature_select.csv'
#testdataB
params['testBpath'] = '../dataset/testdataB.csv'
params['testBopath'] = '../datafeature/testdataB_feature_select.csv'
argvs = sys.argv
try:
for i in range(len(argvs)):
if i < 1:
continue
if argvs[i].split('=')[1] == 'None':
params[argvs[i].split('=')[0]] = None
else:
Type = type(params[argvs[i].split('=')[0]])
params[argvs[i].split('=')[0]] = Type(argvs[i].split('=')[1])
# columns_list_all = ['time_mean','time_std','time_max','time_min','time_rms', \
# 'time_ptp','time_median','time_iqr','time_pr','time_sknew', \
# 'time_kurtosis','time_var','time_amp','time_smr','time_wavefactor', \
# 'time_peakfactor','time_pulse','time_margin', \
# 'freq_mean','freq_std','freq_max','freq_min','freq_rms','freq_median', \
# 'freq_iqr','freq_pr','freq_f2','freq_f3','freq_f4', \
# 'freq_f5','freq_f6','freq_f7','freq_f8','ener_cA5', \
# 'ener_cD1','ener_cD2','ener_cD3','ener_cD4','ener_cD5', \
# 'ratio_cA5','ratio_cD1','ratio_cD2','ratio_cD3','ratio_cD4','ratio_cD5']
#15 features
#columns_list_all = ['time_mean', 'time_std', 'time_median',\
# 'time_iqr', 'time_pr', 'time_amp',\
# 'freq_max', 'freq_median', 'freq_iqr',\
# 'freq_f5', 'freq_f6', 'freq_f7', \
# 'freq_f8', 'ener_cD1', 'ratio_cD1']
#feature selected
columns_list_all = ['time_mean', 'time_median', 'freq_mean',\
'freq_std', 'freq_median', 'freq_f2', \
'freq_f5', 'freq_f6', 'freq_f7', \
'freq_f8', 'ener_cD1', 'ratio_cD1']
#记录各部分的特征数目
#div = {'time':18, 'freq':15,'ener_ratio':12}
def feature_get(filepath):
dfs = pd.read_csv(filepath)
col_1 = list(dfs.columns)
v = col_1[:-1]
df = dfs.loc[:,v]
data = np.array(df[v])
length_get = np.array(dfs['2558'])
feature_list = [i for i in range(len(length_get))]
for i in range(data.shape[0]):
df_line = data[i,:]
#time-domain feature,18
#时域特征 18个
time_mean = df_line.mean()
time_std = df_line.std()
# time_max = df_line.max()
# time_min = df_line.min()
# time_rms = np.sqrt(np.square(df_line).mean())
# time_ptp = time_max-time_min
time_median = np.median(df_line)
time_iqr = np.percentile(df_line,75)-np.percentile(df_line,25)
time_pr = np.percentile(df_line,90)-np.percentile(df_line,10)
# time_skew = stats.skew(df_line)
# time_kurtosis = stats.kurtosis(df_line)
# time_var = np.var(df_line)
time_amp = np.abs(df_line).mean()
# time_smr = np.square(np.sqrt(np.abs(df_line)).mean())
#下面四个特征需要注意分母为0或接近0问题，可能会发生报错
# time_wavefactor = time_rms/time_amp
# time_peakfactor = time_max/time_rms
# time_pulse = time_max/time_amp
# time_margin = time_max/time_smr
#freq-domain feature,15
#频域特征 15个
#采样频率25600Hz
df_fftline = fftpack.fft(df_line)
freq_fftline = fftpack.fftfreq(len(df_line),1/25600)
df_fftline = abs(df_fftline[freq_fftline>=0])
freq_fftline = freq_fftline[freq_fftline>=0]
#基本特征,依次为均值，标准差，最大值，最小值，均方根，中位数，四分位差，百分位差
freq_mean = df_fftline.mean()
freq_std = df_fftline.std()
freq_max = df_fftline.max()
# freq_min = df_fftline.min()
# freq_rms = np.sqrt(np.square(df_fftline).mean())
freq_median = np.median(df_fftline)
freq_iqr = np.percentile(df_fftline,75)-np.percentile(df_fftline,25)
# freq_pr = np.percentile(df_fftline,90)-np.percentile(df_fftline,10)
#f2 f3 f4反映频谱集中程度
freq_f2 = np.square((df_fftline-freq_mean)).sum()/(len(df_fftline)-1)
# freq_f3 = pow((df_fftline-freq_mean),3).sum()/(len(df_fftline)*pow(freq_f2,1.5))
# freq_f4 = pow((df_fftline-freq_mean),4).sum()/(len(df_fftline)*pow(freq_f2,2))
#f5 f6 f7 f8反映主频带位置
freq_f5 = np.multiply(freq_fftline,df_fftline).sum()/df_fftline.sum()
freq_f6 = np.sqrt(np.multiply(np.square(freq_fftline),df_fftline).sum())/df_fftline.sum()
freq_f7 = np.sqrt(np.multiply(pow(freq_fftline,4),df_fftline).sum())/np.multiply(np.square(freq_fftline),df_fftline).sum()
freq_f8 = np.multiply(np.square(freq_fftline),df_fftline).sum()/np.sqrt(np.multiply(pow(freq_fftline,4),df_fftline).sum()*df_fftline.sum())
#---------- timefreq-domain feature,12
#时频域特征 12个
# 5级小波变换，最后输出6个能量特征和其归一化能量特征
cA5, cD5, cD4, cD3, cD2, cD1 = wavedec(df_line, 'db10', level=5)
ener_cA5 = np.square(cA5).sum()
ener_cD5 = np.square(cD5).sum()
ener_cD4 = np.square(cD4).sum()
ener_cD3 = np.square(cD3).sum()
ener_cD2 = np.square(cD2).sum()
ener_cD1 = np.square(cD1).sum()
ener = ener_cA5 + ener_cD1 + ener_cD2 + ener_cD3 + ener_cD4 + ener_cD5
# ratio_cA5 = ener_cA5/ener
# ratio_cD5 = ener_cD5/ener
# ratio_cD4 = ener_cD4/ener
# ratio_cD3 = ener_cD3/ener
# ratio_cD2 = ener_cD2/ener
ratio_cD1 = ener_cD1/ener
# feature_list[i]=[time_mean,time_std,time_max,time_min,time_rms,time_ptp,time_median,time_iqr,time_pr,time_skew,time_kurtosis,time_var,time_amp,
# time_smr,time_wavefactor,time_peakfactor,time_pulse,time_margin,freq_mean,freq_std,freq_max,freq_min,freq_rms,freq_median,
# freq_iqr,freq_pr,freq_f2,freq_f3,freq_f4,freq_f5,freq_f6,freq_f7,freq_f8,ener_cA5,ener_cD1,ener_cD2,ener_cD3,ener_cD4,ener_cD5,
# ratio_cA5,ratio_cD1,ratio_cD2,ratio_cD3,ratio_cD4,ratio_cD5,]
# feature_list[i] = [ time_mean, time_std, time_median,\
# time_iqr, time_pr, time_amp,\
# freq_max, freq_median, freq_iqr,\
# freq_f5, freq_f6, freq_f7, \
# freq_f8, ener_cD1, ratio_cD1]
feature_list[i] = [ time_mean, time_median, freq_mean, \
freq_std, freq_median, freq_f2, \
freq_f5, freq_f6, freq_f7, \
freq_f8, ener_cD1, ratio_cD1]
print('Feature_get is finished!')
return feature_list
#测试集A特征提取
# file_path = params['testApath']
# features = feature_get(file_path)
# col_lab = columns_list_all
# result = pd.DataFrame(features, columns = col_lab)
# result.to_csv(params['testAopath'], sep=',', header=True, index=False)
#测试集B特征提取
file_path = params['testBpath']
features = feature_get(file_path)
col_lab = columns_list_all
result = pd.DataFrame(features, columns = col_lab)
result.to_csv(params['testBopath'], sep=',', header=True, index=False)
except Exception as e:
traceback.print_exc()
print(e)