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The document is linked to the following blog link:[My blog](https://www.jianshu.com/p/f6516d5a71ed) 6 | -------------------------------------------------------------------------------- /code/extract_basic_time_domain_features.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import pandas as pd 4 | import scipy.stats 5 | import numpy as np 6 | from sklearn.metrics import mean_squared_error 7 | 8 | 9 | class ExtractTimeFeatures: 10 | def __init__(self, z, name='z'): 11 | if type(z) is type(pd.DataFrame([1])): 12 | z = z.values 13 | if type(z) is not type(np.array([1])): 14 | try: 15 | z = np.array(z) 16 | except TypeError: 17 | print("The type of z must be 'numpy.ndarray' or 'pandas.core.frame.DataFrame'") 18 | 19 | self.c = [] # all feature names 20 | self.z = z # input dataset whose type is 'np.array' 21 | self.name = '_' + name # dataset name 22 | self._df = [] # return new dataset which includes every feature name 23 | 24 | def extracttimefeatures(self): 25 | max_z = self.get_max() 26 | min_z = self.get_min() 27 | range_z = self.get_range() 28 | mean_z = self.get_mean() 29 | median_z = self.get_median() 30 | mode_z = self.get_mode() 31 | std_z = self.get_std() 32 | rms_z = self.get_rms() 33 | ms_z = self.get_ms() 34 | k_center_z = self.get_k_order_moment(is_center=True) 35 | k_origin_z = self.get_k_order_moment(is_origin=True) 36 | skew_z = self.get_skew() 37 | kurt_z = self.get_kurt() 38 | kurt_factor_z = self.get_kurt_factor() 39 | wave_factor = self.get_wave_factor() 40 | pulse_factor_z = self.get_pulse_factor() 41 | margin_factor_z = self.get_margin_factor() 42 | 43 | self._df = pd.DataFrame([ 44 | max_z, min_z, range_z, mean_z, median_z, mode_z, std_z, 45 | rms_z, ms_z, k_center_z, k_origin_z, skew_z, kurt_z, 46 | kurt_factor_z, wave_factor, pulse_factor_z, margin_factor_z 47 | ], self.c).transpose() 48 | return self._df 49 | 50 | # ==============Dimensional time domain feature===================== 51 | def get_max(self): 52 | self.max_z = np.max(self.z, axis=1) 53 | self.c.append('max'+self.name) 54 | return self.max_z 55 | 56 | def get_min(self): 57 | self.min_z = np.min(self.z, axis=1) 58 | self.c.append('min' + self.name) 59 | return self.min_z 60 | 61 | def get_range(self): 62 | self.range_z = self.max_z-self.min_z 63 | self.c.append('range' + self.name) 64 | return self.range_z 65 | 66 | def get_mean(self): 67 | self.mean_z = np.mean(self.z, axis=1) 68 | self.c.append('mean' + self.name) 69 | return self.mean_z 70 | 71 | def get_median(self): 72 | self.median_z = np.median(self.z, axis=1) 73 | self.c.append('median' + self.name) 74 | return self.median_z 75 | 76 | def get_mode(self): 77 | self.mode_z = scipy.stats.mode(self.z, axis=1)[0].reshape([-1]) 78 | self.c.append('mode' + self.name) 79 | return self.mode_z 80 | 81 | def get_std(self): 82 | self.std_z = np.std(self.z ,axis=1) 83 | self.c.append('std' + self.name) 84 | return self.std_z 85 | 86 | def get_rms(self): 87 | rms_z = [np.sqrt(mean_squared_error(zi, np.zeros(len(zi)))) for zi in self.z] 88 | self.rms_z = np.array(rms_z) 89 | self.c.append('rms' + self.name) 90 | return self.rms_z 91 | 92 | def get_ms(self): 93 | ms_z = [mean_squared_error(zi, np.zeros(len(zi))) for zi in self.z] 94 | self.ms_z = np.array(ms_z) 95 | self.c.append('ms' + self.name) 96 | return self.ms_z 97 | 98 | def get_k_order_moment(self, k=3, is_center=False, is_origin=False): 99 | moment_name, self.moment_z = self.k_order_moment(self.z, k, 100 | is_center, is_origin) 101 | self.c.append(moment_name + self.name) 102 | return self.moment_z 103 | 104 | @staticmethod 105 | def k_order_moment(z, k, is_center, is_origin): 106 | """ 107 | Calculate k-order center moment and k-order origin moment of z 108 | :param z: array_like 109 | :param k: int 110 | :param is_center: bool; whether calculate k-order center moment 111 | :param is_origin: bool; whether calculate k-order origin moment 112 | :return: tuple; return k-order center moment or k-order origin moment 113 | """ 114 | if (is_center is False) and (is_origin is False): 115 | raise ValueError("At least one of is_center and is_origin is True") 116 | if (is_center is True) and (is_origin is True): 117 | raise ValueError("At most one of is_center and is_origin is True") 118 | if (type(k) is not int) or (k < 0): 119 | raise TypeError("k must be a integrate and more than 0") 120 | if type(z) is list: 121 | z = np.array(z) 122 | 123 | mean_z = np.mean(z, axis=1) 124 | if is_origin is False: 125 | k_center = np.mean([(z[i] - mean_z[i]) ** k for i in range(z.shape[0])], axis=1) 126 | return str(k)+'_order_center', k_center 127 | if is_center is False: 128 | k_origin = np.mean([z[i] ** k for i in range(z.shape[0])], axis=1) 129 | return str(k)+'_order_origin', k_origin 130 | # =========================END========================================= 131 | 132 | # ===============Dimensionless time domain feature===================== 133 | def get_skew(self): 134 | self.skew_z = pd.DataFrame(self.z.transpose()).skew().values 135 | self.c.append('skew' + self.name) 136 | return self.skew_z 137 | 138 | def get_kurt(self): 139 | self.kurt_z = pd.DataFrame(self.z.transpose()).kurt().values 140 | self.c.append('kurt' + self.name) 141 | return self.kurt_z 142 | 143 | def get_kurt_factor(self): 144 | self.kurt_factor_z = self.max_z/self.rms_z 145 | self.c.append('kurt_factor' + self.name) 146 | return self.kurt_factor_z 147 | 148 | def get_wave_factor(self): 149 | self.wave_factor_z = self.rms_z/self.mean_z 150 | self.c.append('wave_factor' + self.name) 151 | return self.wave_factor_z 152 | 153 | def get_pulse_factor(self): 154 | self.pulse_factor_z = self.max_z/abs(self.mean_z) 155 | self.c.append('pulse_factor' + self.name) 156 | return self.pulse_factor_z 157 | 158 | def get_margin_factor(self): 159 | self.margin_factor_z = self.max_z/self.ms_z 160 | self.c.append('margin_factor' + self.name) 161 | return self.margin_factor_z 162 | # =========================END========================================= 163 | 164 | 165 | if __name__ == '__main__': 166 | # =====Example======= 167 | z_ = [[.1, 2, .3, 4, 5, .6, 7], [.1, 12, .3, 41, 15, .6, .7]] 168 | c = ExtractTimeFeatures(z_) 169 | print(c.extracttimefeatures()) 170 | -------------------------------------------------------------------------------- /code/feature_extract.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import numpy as np 4 | import scipy.stats as sts 5 | from tqdm import tqdm 6 | import os 7 | # from pywt import WaveletPacket 8 | epsn = 1e-8 9 | 10 | # =================== STATISTICAL FEATURES IN TIME DOMAIN ===================== 11 | def mean_fea(a): 12 | return np.mean(a) 13 | 14 | def rms_fea(a): 15 | return np.sqrt(np.mean(np.square(a))) 16 | 17 | def sr_fea(a): 18 | return np.square(np.mean(np.sqrt(np.abs(a)))) 19 | 20 | def am_fea(a): 21 | return np.mean(np.abs(a)) 22 | 23 | def skew_fea(a): 24 | return np.mean((a-mean_fea(a))**3) 25 | 26 | def kurt_fea(a): 27 | return np.mean((a-mean_fea(a))**4) 28 | 29 | def max_fea(a): 30 | return np.max(a) 31 | 32 | def min_fea(a): 33 | return np.min(a) 34 | 35 | def pp_fea(a): 36 | return max_fea(a)-min_fea(a) 37 | 38 | def var_fea(a): 39 | n = len(a) 40 | return np.sum((a-mean_fea(a))**2)/(n-1) 41 | 42 | def waveform_index(a): 43 | return rms_fea(a)/(am_fea(a)+epsn) 44 | 45 | def peak_index(a): 46 | return max_fea(a)/(rms_fea(a)+epsn) 47 | 48 | def impluse_factor(a): 49 | return max_fea(a)/(am_fea(a)+epsn) 50 | 51 | def tolerance_index(a): 52 | return max_fea(a)/(sr_fea(a)+epsn) 53 | 54 | def skew_index(a): 55 | n = len(a) 56 | temp1 = np.sum((a-mean_fea(a))**3) 57 | temp2 = (np.sqrt(var_fea(a)))**3 58 | return temp1/((n-1)*temp2) 59 | 60 | def kurt_index(a): 61 | n = len(a) 62 | temp1 = np.sum((a-mean_fea(a))**4) 63 | temp2 = (np.sqrt(var_fea(a)))**4 64 | return temp1/((n-1)*temp2) 65 | # ============================= END ====================================== 66 | # def wave_fea(a): 67 | # wp = WaveletPacket(a,'db1', maxlevel=8) 68 | # nodes = wp.get_level(8, "freq") 69 | # return np.linalg.norm(np.array([n.data for n in nodes]), 2) 70 | 71 | # =============== STATISTICAL FEATURES IN TIME DOMAIN ======================= 72 | # def fft_fft(sequence_data): 73 | # fft_trans = np.abs(np.fft.fft(sequence_data)) 74 | # dc = fft_trans[0] 75 | # freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 76 | # freq_sum_ = np.sum(freq_spectrum) 77 | # return dc, freq_spectrum, freq_sum_ 78 | 79 | def fft_mean(sequence_data): 80 | def fft_fft(sequence_data): 81 | fft_trans = np.abs(np.fft.fft(sequence_data)) 82 | # dc = fft_trans[0] 83 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 84 | _freq_sum_ = np.sum(freq_spectrum) 85 | return freq_spectrum, _freq_sum_ 86 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 87 | return np.mean(freq_spectrum) 88 | 89 | def fft_var(sequence_data): 90 | def fft_fft(sequence_data): 91 | fft_trans = np.abs(np.fft.fft(sequence_data)) 92 | # dc = fft_trans[0] 93 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 94 | _freq_sum_ = np.sum(freq_spectrum) 95 | return freq_spectrum, _freq_sum_ 96 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 97 | return np.var(freq_spectrum) 98 | 99 | # def fft_std(sequence_data): 100 | # def fft_fft(sequence_data): 101 | # fft_trans = np.abs(np.fft.fft(sequence_data)) 102 | # # dc = fft_trans[0] 103 | # freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 104 | # _freq_sum_ = np.sum(freq_spectrum) 105 | # return freq_spectrum, _freq_sum_ 106 | # freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 107 | # return np.std(freq_spectrum) 108 | 109 | def fft_entropy(sequence_data): 110 | def fft_fft(sequence_data): 111 | fft_trans = np.abs(np.fft.fft(sequence_data)) 112 | # dc = fft_trans[0] 113 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 114 | _freq_sum_ = np.sum(freq_spectrum) 115 | return freq_spectrum, _freq_sum_ 116 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 117 | pr_freq = freq_spectrum * 1.0 / _freq_sum_ 118 | entropy = -1 * np.sum([np.log2(p+1e-5) * p for p in pr_freq]) 119 | return entropy 120 | 121 | def fft_energy(sequence_data): 122 | def fft_fft(sequence_data): 123 | fft_trans = np.abs(np.fft.fft(sequence_data)) 124 | # dc = fft_trans[0] 125 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 126 | _freq_sum_ = np.sum(freq_spectrum) 127 | return freq_spectrum, _freq_sum_ 128 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 129 | return np.sum(freq_spectrum ** 2) / len(freq_spectrum) 130 | 131 | def fft_skew(sequence_data): 132 | def fft_fft(sequence_data): 133 | fft_trans = np.abs(np.fft.fft(sequence_data)) 134 | # dc = fft_trans[0] 135 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 136 | _freq_sum_ = np.sum(freq_spectrum) 137 | return freq_spectrum, _freq_sum_ 138 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 139 | _fft_mean, _fft_std = fft_mean(sequence_data), fft_std(sequence_data) 140 | return np.mean([0 if _fft_std < epsn else np.power((x - _fft_mean) / _fft_std, 3) 141 | for x in freq_spectrum]) 142 | 143 | def fft_kurt(sequence_data): 144 | def fft_fft(sequence_data): 145 | fft_trans = np.abs(np.fft.fft(sequence_data)) 146 | # dc = fft_trans[0] 147 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 148 | _freq_sum_ = np.sum(freq_spectrum) 149 | return freq_spectrum, _freq_sum_ 150 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 151 | _fft_mean, _fft_std = fft_mean(sequence_data), fft_std(sequence_data) 152 | return np.mean([0 if _fft_std < epsn else np.power((x - _fft_mean) / _fft_std, 4) 153 | for x in freq_spectrum]) 154 | 155 | def fft_shape_mean(sequence_data): 156 | def fft_fft(sequence_data): 157 | fft_trans = np.abs(np.fft.fft(sequence_data)) 158 | # dc = fft_trans[0] 159 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 160 | _freq_sum_ = np.sum(freq_spectrum) 161 | return freq_spectrum, _freq_sum_ 162 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 163 | shape_sum = np.sum([x * freq_spectrum[x] 164 | for x in range(len(freq_spectrum))]) 165 | return 0 if _freq_sum_ < epsn else shape_sum * 1.0 / _freq_sum_ 166 | 167 | def fft_shape_std(sequence_data): 168 | def fft_fft(sequence_data): 169 | fft_trans = np.abs(np.fft.fft(sequence_data)) 170 | # dc = fft_trans[0] 171 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 172 | _freq_sum_ = np.sum(freq_spectrum) 173 | return freq_spectrum, _freq_sum_ 174 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 175 | shape_mean = fft_shape_mean(sequence_data) 176 | var = np.sum([0 if _freq_sum_ < epsn else np.power((x - shape_mean), 2) * freq_spectrum[x] 177 | for x in range(len(freq_spectrum))]) / _freq_sum_ 178 | return np.sqrt(var) 179 | 180 | def fft_shape_skew(sequence_data): 181 | def fft_fft(sequence_data): 182 | fft_trans = np.abs(np.fft.fft(sequence_data)) 183 | # dc = fft_trans[0] 184 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 185 | _freq_sum_ = np.sum(freq_spectrum) 186 | return freq_spectrum, _freq_sum_ 187 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 188 | shape_mean = fft_shape_mean(sequence_data) 189 | return np.sum([np.power((x - shape_mean), 3) * freq_spectrum[x] 190 | for x in range(len(freq_spectrum))]) / _freq_sum_ 191 | 192 | def fft_shape_kurt(sequence_data): 193 | def fft_fft(sequence_data): 194 | fft_trans = np.abs(np.fft.fft(sequence_data)) 195 | # dc = fft_trans[0] 196 | freq_spectrum = fft_trans[1:int(np.floor(len(sequence_data) * 1.0 / 2)) + 1] 197 | _freq_sum_ = np.sum(freq_spectrum) 198 | return freq_spectrum, _freq_sum_ 199 | freq_spectrum, _freq_sum_ = fft_fft(sequence_data) 200 | shape_mean = fft_shape_mean(sequence_data) 201 | return np.sum([np.power((x - shape_mean), 4) * freq_spectrum[x] - 3 202 | for x in range(len(freq_spectrum))]) / _freq_sum_ 203 | # =================================== END ===================================== --------------------------------------------------------------------------------