def get_global_feature(self): """ 获取时域全局特征,包含最大值、标准差、平均值 :param hadcropped: :return: """ square_data, square_energy, square_azrate = self.pre_process(method='hanning', ifcrop=True) func = lambda x: [ # feature_calc.autocorrelation(norm(x), 5), np.std(x), feature_calc.approximate_entropy(norm(x), 5, 1), feature_calc.cid_ce(x, normalize=True), feature_calc.count_above_mean(x), feature_calc.first_location_of_minimum(x), feature_calc.first_location_of_maximum(x), feature_calc.last_location_of_maximum(x), feature_calc.last_location_of_minimum(x), feature_calc.longest_strike_above_mean(x), feature_calc.number_crossing_m(x, 0.8*np.max(x)), feature_calc.skewness(x), feature_calc.time_reversal_asymmetry_statistic(x, 5) ] # global features I want to get upper_rate = self.get_upper_rate(square_energy) feature = np.hstack([ [np.mean(norm(square_energy))], [upper_rate], func(square_azrate), func(square_energy) ]) return feature
def time_reversal_asymmetry(mag): """Derives a feature introduced by Fulcher. See: Fulcher, B.D., Jones, N.S. (2014). Highly comparative feature-based time-series classification. Knowledge and Data Engineering, IEEE Transactions on 26, 3026–3037. rtype: float """ val = ts.time_reversal_asymmetry_statistic(mag, 1) return val
def get_mfcc_feature(self, hadcropped=False): ''' calculate Mel-frequency cepstral coefficients in frequency domain and extract features from MFCC :return: numpy array ''' assert self.frame_per_second not in [32, 64, 128, 256], \ Exception("Cannot operate butterfly computation ," "frame per second should in [32, 64, 128, 256]") hanning_kernel = self.get_window(method='hanning') windowed = self._add_window(hanning_kernel, self.meta_audio_data) # [num_frame, kernel_size] hanning_energy = self.get_energy(self.meta_audio_data, hanning_kernel) if not hadcropped: boundary = self.get_boundary(hanning_energy) cropped = windowed[boundary[0]: boundary[1] + 1, :] frequency = np.vstack([fft.fft(frame.squeeze()) for frame in np.vsplit(cropped, len(cropped))]) else: frequency = np.vstack([fft.fft(windowed)]) frequency = np.abs(frequency) frequency_energy = frequency ** 2 low_freq = self.sr / self.num_per_frame high_freq = self.sr H = self._mfcc_filter(self.mfcc_cof, low_freq, high_freq) S = np.dot(frequency_energy, H.transpose()) # (F, M) cos_ary = self._discrete_cosine_transform() mfcc_raw_features = np.sqrt(2 / self.mfcc_cof) * np.dot(S, cos_ary) # (F,N) upper = [self.get_upper_rate(fea) for fea in mfcc_raw_features.transpose()] assert len(upper) == mfcc_raw_features.shape[1] func = lambda x: [ # feature_calc.autocorrelation(norm(x), 5), np.std(x), feature_calc.approximate_entropy(norm(x), 5, 1), feature_calc.cid_ce(x, normalize=True), feature_calc.count_above_mean(x), feature_calc.first_location_of_minimum(x), feature_calc.first_location_of_maximum(x), feature_calc.last_location_of_maximum(x), feature_calc.last_location_of_minimum(x), feature_calc.longest_strike_above_mean(x), feature_calc.number_crossing_m(x, 0.8*np.max(x)), feature_calc.skewness(x), feature_calc.time_reversal_asymmetry_statistic(x, 5) ] mfcc_features = np.hstack( [func(col) for col in mfcc_raw_features.transpose()] ) return mfcc_features
def compute_standard_features_block(xc, seg_id, X, fs, prefix=''): # Generic stats X.loc[seg_id, prefix + 'mean'] = xc.mean() X.loc[seg_id, prefix + 'std'] = xc.std() X.loc[seg_id, prefix + 'max'] = xc.max() X.loc[seg_id, prefix + 'min'] = xc.min() X.loc[seg_id, prefix + 'hmean'] = stats.hmean(np.abs(xc[np.nonzero(xc)[0]])) X.loc[seg_id, prefix + 'gmean'] = stats.gmean(np.abs(xc[np.nonzero(xc)[0]])) X.loc[seg_id, prefix + 'mad'] = xc.mad() X.loc[seg_id, prefix + 'kurt'] = xc.kurtosis() X.loc[seg_id, prefix + 'skew'] = xc.skew() X.loc[seg_id, prefix + 'med'] = xc.median() for p in [1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99]: X.loc[seg_id, prefix + f'percentile_{p}'] = np.percentile(xc, p) X.loc[seg_id, prefix + f'abs_percentile_{p}'] = np.percentile(np.abs(xc), p) X.loc[seg_id, prefix + 'num_crossing_0'] = feature_calculators.number_crossing_m(xc, 0) for p in [95,99]: X.loc[seg_id, prefix + f'binned_entropy_{p}'] = feature_calculators.binned_entropy(xc, p) # Andrew stats X.loc[seg_id, prefix + 'mean_diff'] = np.mean(np.diff(xc)) X.loc[seg_id, prefix + 'mean_abs_diff'] = np.mean(np.abs(np.diff(xc))) X.loc[seg_id, prefix + 'mean_change_rate'] = change_rate(xc, method='original') X.loc[seg_id, prefix + 'mean_change_rate_v2'] = change_rate(xc, method='modified') X.loc[seg_id, prefix + 'abs_max'] = np.abs(xc).max() X.loc[seg_id, prefix + 'abs_min'] = np.abs(xc).min() X.loc[seg_id, prefix + 'mean_change_abs'] = np.mean(np.diff(xc)) # Classical stats by segment for agg_type, slice_length, direction in product(['std', 'min', 'max', 'mean'], [1000, 10000, 50000], ['first', 'last']): if direction == 'first': X.loc[seg_id, prefix + f'{agg_type}_{direction}_{slice_length}'] = xc[:slice_length].agg(agg_type) elif direction == 'last': X.loc[seg_id, prefix + f'{agg_type}_{direction}_{slice_length}'] = xc[-slice_length:].agg(agg_type) X.loc[seg_id, prefix + 'avg_first_50000'] = xc[:50000].mean() X.loc[seg_id, prefix + 'avg_last_50000'] = xc[-50000:].mean() X.loc[seg_id, prefix + 'avg_first_10000'] = xc[:10000].mean() X.loc[seg_id, prefix + 'avg_last_10000'] = xc[-10000:].mean() # k-statistic and moments for i in range(1, 5): X.loc[seg_id, prefix + f'kstat_{i}'] = stats.kstat(xc, i) X.loc[seg_id, prefix + f'moment_{i}'] = stats.moment(xc, i) for i in [1, 2]: X.loc[seg_id, prefix + f'kstatvar_{i}'] = stats.kstatvar(xc, i) X.loc[seg_id, prefix + 'range_minf_m4000'] = feature_calculators.range_count(xc, -np.inf, -4000) X.loc[seg_id, prefix + 'range_p4000_pinf'] = feature_calculators.range_count(xc, 4000, np.inf) for i, j in zip(borders, borders[1:]): X.loc[seg_id, prefix + f'range_{i}_{j}'] = feature_calculators.range_count(xc, i, j) X.loc[seg_id, prefix + 'ratio_unique_values'] = feature_calculators.ratio_value_number_to_time_series_length(xc) X.loc[seg_id, prefix + 'max_to_min'] = xc.max() / np.abs(xc.min()) X.loc[seg_id, prefix + 'max_to_min_diff'] = xc.max() - np.abs(xc.min()) X.loc[seg_id, prefix + 'count_big'] = len(xc[np.abs(xc) > 500]) X.loc[seg_id, prefix + 'sum'] = xc.sum() # calc_change_rate on slices of data for slice_length, direction in product([1000, 10000, 50000], ['first', 'last']): if direction == 'first': X.loc[seg_id, prefix + f'mean_change_rate_{direction}_{slice_length}'] = change_rate(xc[:slice_length], method='original') X.loc[seg_id, prefix + f'mean_change_rate_{direction}_{slice_length}_v2'] = change_rate(xc[:slice_length], method='modified') elif direction == 'last': X.loc[seg_id, prefix + f'mean_change_rate_{direction}_{slice_length}'] = change_rate(xc[-slice_length:], method='original') X.loc[seg_id, prefix + f'mean_change_rate_{direction}_{slice_length}_v2'] = change_rate(xc[-slice_length:], method='modified') X.loc[seg_id, prefix + 'q95'] = np.quantile(xc, 0.95) X.loc[seg_id, prefix + 'q99'] = np.quantile(xc, 0.99) X.loc[seg_id, prefix + 'q05'] = np.quantile(xc, 0.05) X.loc[seg_id, prefix + 'q01'] = np.quantile(xc, 0.01) X.loc[seg_id, prefix + 'abs_q95'] = np.quantile(np.abs(xc), 0.95) X.loc[seg_id, prefix + 'abs_q99'] = np.quantile(np.abs(xc), 0.99) X.loc[seg_id, prefix + 'abs_q05'] = np.quantile(np.abs(xc), 0.05) X.loc[seg_id, prefix + 'abs_q01'] = np.quantile(np.abs(xc), 0.01) X.loc[seg_id, prefix + 'trend'] = add_trend_feature(xc) X.loc[seg_id, prefix + 'abs_trend'] = add_trend_feature(xc, abs_values=True) X.loc[seg_id, prefix + 'abs_mean'] = np.abs(xc).mean() X.loc[seg_id, prefix + 'abs_std'] = np.abs(xc).std() X.loc[seg_id, prefix + 'Hilbert_mean'] = np.abs(hilbert(xc)).mean() X.loc[seg_id, prefix + 'Hann_window_mean'] = (convolve(xc, hann(150), mode='same') / sum(hann(150))).mean() for hw in [50, 150, 1500, 15000]: X.loc[seg_id, prefix + f'Hann_window_mean_{hw}'] = (convolve(xc, hann(hw), mode='same') / sum(hann(hw))).mean() sta_lta_method = 'original' classic_sta_lta1 = sta_lta_ratio(xc, 500, 10000, method=sta_lta_method) classic_sta_lta2 = sta_lta_ratio(xc, 5000, 100000, method=sta_lta_method) classic_sta_lta3 = sta_lta_ratio(xc, 3333, 6666, method=sta_lta_method) classic_sta_lta4 = sta_lta_ratio(xc, 10000, 25000, method=sta_lta_method) classic_sta_lta5 = sta_lta_ratio(xc, 50, 1000, method=sta_lta_method) classic_sta_lta6 = sta_lta_ratio(xc, 100, 5000, method=sta_lta_method) classic_sta_lta7 = sta_lta_ratio(xc, 333, 666, method=sta_lta_method) classic_sta_lta8 = sta_lta_ratio(xc, 4000, 10000, method=sta_lta_method) X.loc[seg_id, prefix + 'classic_sta_lta1_mean'] = classic_sta_lta1.mean() X.loc[seg_id, prefix + 'classic_sta_lta2_mean'] = classic_sta_lta2.mean() X.loc[seg_id, prefix + 'classic_sta_lta3_mean'] = classic_sta_lta3.mean() X.loc[seg_id, prefix + 'classic_sta_lta4_mean'] = classic_sta_lta4.mean() X.loc[seg_id, prefix + 'classic_sta_lta5_mean'] = classic_sta_lta5.mean() X.loc[seg_id, prefix + 'classic_sta_lta6_mean'] = classic_sta_lta6.mean() X.loc[seg_id, prefix + 'classic_sta_lta7_mean'] = classic_sta_lta7.mean() X.loc[seg_id, prefix + 'classic_sta_lta8_mean'] = classic_sta_lta8.mean() X.loc[seg_id, prefix + 'classic_sta_lta1_q95'] = np.quantile(classic_sta_lta1, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta2_q95'] = np.quantile(classic_sta_lta2, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta3_q95'] = np.quantile(classic_sta_lta3, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta4_q95'] = np.quantile(classic_sta_lta4, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta5_q95'] = np.quantile(classic_sta_lta5, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta6_q95'] = np.quantile(classic_sta_lta6, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta7_q95'] = np.quantile(classic_sta_lta7, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta8_q95'] = np.quantile(classic_sta_lta8, 0.95) X.loc[seg_id, prefix + 'classic_sta_lta1_q05'] = np.quantile(classic_sta_lta1, 0.05) X.loc[seg_id, prefix + 'classic_sta_lta2_q05'] = np.quantile(classic_sta_lta2, 0.05) X.loc[seg_id, prefix + 'classic_sta_lta3_q05'] = np.quantile(classic_sta_lta3, 0.05) X.loc[seg_id, prefix + 'classic_sta_lta4_q05'] = np.quantile(classic_sta_lta4, 0.05) X.loc[seg_id, prefix + 'classic_sta_lta5_q05'] = np.quantile(classic_sta_lta5, 0.05) X.loc[seg_id, prefix + 'classic_sta_lta6_q05'] = np.quantile(classic_sta_lta6, 0.05) X.loc[seg_id, prefix + 'classic_sta_lta7_q05'] = np.quantile(classic_sta_lta7, 0.05) X.loc[seg_id, prefix + 'classic_sta_lta8_q05'] = np.quantile(classic_sta_lta8, 0.05) sta_lta_method = 'modified' classic_sta_lta1 = sta_lta_ratio(xc, 500, 10000, method=sta_lta_method) classic_sta_lta2 = sta_lta_ratio(xc, 5000, 100000, method=sta_lta_method) classic_sta_lta3 = sta_lta_ratio(xc, 3333, 6666, method=sta_lta_method) classic_sta_lta4 = sta_lta_ratio(xc, 10000, 25000, method=sta_lta_method) classic_sta_lta5 = sta_lta_ratio(xc, 50, 1000, method=sta_lta_method) classic_sta_lta6 = sta_lta_ratio(xc, 100, 5000, method=sta_lta_method) classic_sta_lta7 = sta_lta_ratio(xc, 333, 666, method=sta_lta_method) classic_sta_lta8 = sta_lta_ratio(xc, 4000, 10000, method=sta_lta_method) X.loc[seg_id, prefix + 'modified_sta_lta1_mean'] = classic_sta_lta1.mean() X.loc[seg_id, prefix + 'modified_sta_lta2_mean'] = classic_sta_lta2.mean() X.loc[seg_id, prefix + 'modified_sta_lta3_mean'] = classic_sta_lta3.mean() X.loc[seg_id, prefix + 'modified_sta_lta4_mean'] = classic_sta_lta4.mean() X.loc[seg_id, prefix + 'modified_sta_lta5_mean'] = classic_sta_lta5.mean() X.loc[seg_id, prefix + 'modified_sta_lta6_mean'] = classic_sta_lta6.mean() X.loc[seg_id, prefix + 'modified_sta_lta7_mean'] = classic_sta_lta7.mean() X.loc[seg_id, prefix + 'modified_sta_lta8_mean'] = classic_sta_lta8.mean() X.loc[seg_id, prefix + 'modified_sta_lta1_q95'] = np.quantile(classic_sta_lta1, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta2_q95'] = np.quantile(classic_sta_lta2, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta3_q95'] = np.quantile(classic_sta_lta3, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta4_q95'] = np.quantile(classic_sta_lta4, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta5_q95'] = np.quantile(classic_sta_lta5, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta6_q95'] = np.quantile(classic_sta_lta6, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta7_q95'] = np.quantile(classic_sta_lta7, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta8_q95'] = np.quantile(classic_sta_lta8, 0.95) X.loc[seg_id, prefix + 'modified_sta_lta1_q05'] = np.quantile(classic_sta_lta1, 0.05) X.loc[seg_id, prefix + 'modified_sta_lta2_q05'] = np.quantile(classic_sta_lta2, 0.05) X.loc[seg_id, prefix + 'modified_sta_lta3_q05'] = np.quantile(classic_sta_lta3, 0.05) X.loc[seg_id, prefix + 'modified_sta_lta4_q05'] = np.quantile(classic_sta_lta4, 0.05) X.loc[seg_id, prefix + 'modified_sta_lta5_q05'] = np.quantile(classic_sta_lta5, 0.05) X.loc[seg_id, prefix + 'modified_sta_lta6_q05'] = np.quantile(classic_sta_lta6, 0.05) X.loc[seg_id, prefix + 'modified_sta_lta7_q05'] = np.quantile(classic_sta_lta7, 0.05) X.loc[seg_id, prefix + 'modified_sta_lta8_q05'] = np.quantile(classic_sta_lta8, 0.05) X.loc[seg_id, prefix + 'Moving_average_700_mean'] = xc.rolling(window=700).mean().mean(skipna=True) X.loc[seg_id, prefix + 'Moving_average_1500_mean'] = xc.rolling(window=1500).mean().mean(skipna=True) X.loc[seg_id, prefix + 'Moving_average_3000_mean'] = xc.rolling(window=3000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'Moving_average_6000_mean'] = xc.rolling(window=6000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'Moving_average_30000_mean'] = xc.rolling(window=30000).mean().mean(skipna=True) ewma = pd.Series.ewm X.loc[seg_id, prefix + 'exp_Moving_average_300_mean'] = ewma(xc, span=300).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_3000_mean'] = ewma(xc, span=3000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_6000_mean'] = ewma(xc, span=6000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_30000_mean'] = ewma(xc, span=30000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_50000_mean'] = ewma(xc, span=50000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_300_std'] = ewma(xc, span=300).mean().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_3000_std'] = ewma(xc, span=3000).mean().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_6000_std'] = ewma(xc, span=6000).mean().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_30000_std'] = ewma(xc, span=30000).mean().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_average_50000_std'] = ewma(xc, span=50000).mean().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_300_mean'] = ewma(xc, span=300).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_3000_mean'] = ewma(xc, span=3000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_6000_mean'] = ewma(xc, span=6000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_30000_mean'] = ewma(xc, span=30000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_50000_mean'] = ewma(xc, span=50000).mean().mean(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_300_std'] = ewma(xc, span=300).std().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_3000_std'] = ewma(xc, span=3000).std().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_6000_std'] = ewma(xc, span=6000).std().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_30000_std'] = ewma(xc, span=30000).std().std(skipna=True) X.loc[seg_id, prefix + 'exp_Moving_std_50000_std'] = ewma(xc, span=50000).std().std(skipna=True) no_of_std = 2 X.loc[seg_id, prefix + 'MA_700MA_std_mean'] = xc.rolling(window=700).std().mean() X.loc[seg_id, prefix + 'MA_700MA_BB_high_mean'] = (X.loc[seg_id, prefix + 'Moving_average_700_mean'] + no_of_std * X.loc[seg_id, prefix + 'MA_700MA_std_mean']).mean() X.loc[seg_id, prefix + 'MA_700MA_BB_low_mean'] = (X.loc[seg_id, prefix + 'Moving_average_700_mean'] - no_of_std * X.loc[seg_id, prefix + 'MA_700MA_std_mean']).mean() X.loc[seg_id, prefix + 'MA_400MA_std_mean'] = xc.rolling(window=400).std().mean() X.loc[seg_id, prefix + 'MA_400MA_BB_high_mean'] = (X.loc[seg_id, prefix + 'Moving_average_700_mean'] + no_of_std * X.loc[seg_id, prefix + 'MA_400MA_std_mean']).mean() X.loc[seg_id, prefix + 'MA_400MA_BB_low_mean'] = (X.loc[seg_id, prefix + 'Moving_average_700_mean'] - no_of_std * X.loc[seg_id, prefix + 'MA_400MA_std_mean']).mean() X.loc[seg_id, prefix + 'MA_1000MA_std_mean'] = xc.rolling(window=1000).std().mean() X.loc[seg_id, prefix + 'iqr'] = np.subtract(*np.percentile(xc, [75, 25])) X.loc[seg_id, prefix + 'iqr1'] = np.subtract(*np.percentile(xc, [95, 5])) X.loc[seg_id, prefix + 'q999'] = np.quantile(xc, 0.999) X.loc[seg_id, prefix + 'q001'] = np.quantile(xc, 0.001) X.loc[seg_id, prefix + 'ave10'] = stats.trim_mean(xc, 0.1) X.loc[seg_id, prefix + 'freq_cross_first_50000'] = freq_from_crossings(xc.values[:50000], fs) X.loc[seg_id, prefix + 'freq_cross_last_50000'] = freq_from_crossings(xc.values[-50000:], fs) X.loc[seg_id, prefix + 'freq_cross_first_10000'] = freq_from_crossings(xc.values[:10000], fs) X.loc[seg_id, prefix + 'freq_cross_last_10000'] = freq_from_crossings(xc.values[-10000:], fs) for peak in [10, 20, 50, 100]: X.loc[seg_id, prefix + f'num_peaks_{peak}'] = feature_calculators.number_peaks(xc, peak) for c in [1, 5, 10, 50, 100]: X.loc[seg_id, prefix + f'spkt_welch_density_{c}'] = list(feature_calculators.spkt_welch_density(xc, [{'coeff': c}]))[0][1] X.loc[seg_id, prefix + f'time_rev_asym_stat_{c}'] = feature_calculators.time_reversal_asymmetry_statistic(xc, c) for autocorr_lag in [5, 10, 50, 100, 500, 1000, 5000, 10000]: X.loc[seg_id, prefix + f'autocorrelation_{autocorr_lag}'] = feature_calculators.autocorrelation(xc, autocorr_lag) X.loc[seg_id, prefix + f'c3_{autocorr_lag}'] = feature_calculators.c3(xc, autocorr_lag) for windows in [10, 50, 100, 500, 1000, 10000]: x_roll_std = xc.rolling(windows).std().dropna().values x_roll_mean = xc.rolling(windows).mean().dropna().values for p in [1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99]: X.loc[seg_id, prefix + f'percentile_roll_std_{p}_window_{windows}'] = np.percentile(x_roll_std, p) X.loc[seg_id, prefix + f'percentile_roll_mean_{p}_window_{windows}'] = np.percentile(x_roll_mean, p) X.loc[seg_id, prefix + 'ave_roll_std_' + str(windows)] = x_roll_std.mean() X.loc[seg_id, prefix + 'std_roll_std_' + str(windows)] = x_roll_std.std() X.loc[seg_id, prefix + 'max_roll_std_' + str(windows)] = x_roll_std.max() X.loc[seg_id, prefix + 'min_roll_std_' + str(windows)] = x_roll_std.min() X.loc[seg_id, prefix + 'q01_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.01) X.loc[seg_id, prefix + 'q05_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.05) X.loc[seg_id, prefix + 'q95_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.95) X.loc[seg_id, prefix + 'q99_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.99) X.loc[seg_id, prefix + 'av_change_abs_roll_std_' + str(windows)] = np.mean(np.abs(np.diff(x_roll_std))) X.loc[seg_id, prefix + 'av_change_rate_roll_std_' + str(windows)] = change_rate(pd.Series(x_roll_std), method='original') X.loc[seg_id, prefix + 'av_change_rate_roll_std_' + str(windows) + 'v2'] = change_rate(pd.Series(x_roll_std), method='modified') X.loc[seg_id, prefix + 'abs_max_roll_std_' + str(windows)] = np.abs(x_roll_std).max() X.loc[seg_id, prefix + 'ave_roll_mean_' + str(windows)] = x_roll_mean.mean() X.loc[seg_id, prefix + 'std_roll_mean_' + str(windows)] = x_roll_mean.std() X.loc[seg_id, prefix + 'max_roll_mean_' + str(windows)] = x_roll_mean.max() X.loc[seg_id, prefix + 'min_roll_mean_' + str(windows)] = x_roll_mean.min() X.loc[seg_id, prefix + 'q01_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.01) X.loc[seg_id, prefix + 'q05_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.05) X.loc[seg_id, prefix + 'q95_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.95) X.loc[seg_id, prefix + 'q99_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.99) X.loc[seg_id, prefix + 'av_change_abs_roll_mean_' + str(windows)] = np.mean(np.abs(np.diff(x_roll_mean))) X.loc[seg_id, prefix + 'av_change_rate_roll_mean_' + str(windows)] = change_rate(pd.Series(x_roll_mean), method='original') X.loc[seg_id, prefix + 'av_change_rate_roll_mean_' + str(windows) + '_v2'] = change_rate(pd.Series(x_roll_mean), method='modified') X.loc[seg_id, prefix + 'abs_max_roll_mean_' + str(windows)] = np.abs(x_roll_mean).max() for p in [1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99]: X.loc[seg_id, prefix + f'percentile_roll_std_{p}'] = X.loc[seg_id, prefix + f'percentile_roll_std_{p}_window_10000'] X.loc[seg_id, prefix + f'percentile_roll_mean_{p}'] = X.loc[seg_id, prefix + f'percentile_roll_mean_{p}_window_10000']
def features(self, x, y, seg_id): feature_dict = dict() feature_dict['target'] = y feature_dict['seg_id'] = seg_id # lists with parameters to iterate over them percentiles = [ 1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99 ] hann_windows = [50, 150, 1500, 15000] spans = [300, 3000, 30000, 50000] windows = [10, 50, 100, 500, 1000, 10000] borders = list(range(-4000, 4001, 1000)) peaks = [10, 20, 50, 100] coefs = [1, 5, 10, 50, 100] autocorr_lags = [5, 10, 50, 100, 500, 1000, 5000, 10000] # basic stats feature_dict['mean'] = x.mean() feature_dict['std'] = x.std() feature_dict['max'] = x.max() feature_dict['min'] = x.min() # basic stats on absolute values feature_dict['mean_change_abs'] = np.mean(np.diff(x)) feature_dict['abs_max'] = np.abs(x).max() feature_dict['abs_mean'] = np.abs(x).mean() feature_dict['abs_std'] = np.abs(x).std() # geometric and harmonic means feature_dict['hmean'] = stats.hmean(np.abs(x[np.nonzero(x)[0]])) feature_dict['gmean'] = stats.gmean(np.abs(x[np.nonzero(x)[0]])) # k-statistic and moments for i in range(1, 5): feature_dict[f'kstat_{i}'] = stats.kstat(x, i) feature_dict[f'moment_{i}'] = stats.moment(x, i) for i in [1, 2]: feature_dict[f'kstatvar_{i}'] = stats.kstatvar(x, i) # aggregations on various slices of data for agg_type, slice_length, direction in product( ['std', 'min', 'max', 'mean'], [1000, 10000, 50000], ['first', 'last']): if direction == 'first': feature_dict[ f'{agg_type}_{direction}_{slice_length}'] = x[: slice_length].agg( agg_type) elif direction == 'last': feature_dict[f'{agg_type}_{direction}_{slice_length}'] = x[ -slice_length:].agg(agg_type) feature_dict['max_to_min'] = x.max() / np.abs(x.min()) feature_dict['max_to_min_diff'] = x.max() - np.abs(x.min()) feature_dict['count_big'] = len(x[np.abs(x) > 500]) feature_dict['sum'] = x.sum() feature_dict['mean_change_rate'] = self.calc_change_rate(x) # calc_change_rate on slices of data for slice_length, direction in product([1000, 10000, 50000], ['first', 'last']): if direction == 'first': feature_dict[ f'mean_change_rate_{direction}_{slice_length}'] = self.calc_change_rate( x[:slice_length]) elif direction == 'last': feature_dict[ f'mean_change_rate_{direction}_{slice_length}'] = self.calc_change_rate( x[-slice_length:]) # percentiles on original and absolute values for p in percentiles: feature_dict[f'percentile_{p}'] = np.percentile(x, p) feature_dict[f'abs_percentile_{p}'] = np.percentile(np.abs(x), p) feature_dict['trend'] = self.add_trend_feature(x) feature_dict['abs_trend'] = self.add_trend_feature(x, abs_values=True) feature_dict['mad'] = x.mad() feature_dict['kurt'] = x.kurtosis() feature_dict['skew'] = x.skew() feature_dict['med'] = x.median() feature_dict['Hilbert_mean'] = np.abs(signal.hilbert(x)).mean() for hw in hann_windows: feature_dict[f'Hann_window_mean_{hw}'] = ( signal.convolve(x, signal.hann(hw), mode='same') / sum(signal.hann(hw))).mean() feature_dict['classic_sta_lta1_mean'] = self.classic_sta_lta( x, 500, 10000).mean() feature_dict['classic_sta_lta2_mean'] = self.classic_sta_lta( x, 5000, 100000).mean() feature_dict['classic_sta_lta3_mean'] = self.classic_sta_lta( x, 3333, 6666).mean() feature_dict['classic_sta_lta4_mean'] = self.classic_sta_lta( x, 10000, 25000).mean() feature_dict['classic_sta_lta5_mean'] = self.classic_sta_lta( x, 50, 1000).mean() feature_dict['classic_sta_lta6_mean'] = self.classic_sta_lta( x, 100, 5000).mean() feature_dict['classic_sta_lta7_mean'] = self.classic_sta_lta( x, 333, 666).mean() feature_dict['classic_sta_lta8_mean'] = self.classic_sta_lta( x, 4000, 10000).mean() # exponential rolling statistics ewma = pd.Series.ewm for s in spans: feature_dict[f'exp_Moving_average_{s}_mean'] = (ewma( x, span=s).mean(skipna=True)).mean(skipna=True) feature_dict[f'exp_Moving_average_{s}_std'] = (ewma( x, span=s).mean(skipna=True)).std(skipna=True) feature_dict[f'exp_Moving_std_{s}_mean'] = (ewma( x, span=s).std(skipna=True)).mean(skipna=True) feature_dict[f'exp_Moving_std_{s}_std'] = (ewma( x, span=s).std(skipna=True)).std(skipna=True) feature_dict['iqr'] = np.subtract(*np.percentile(x, [75, 25])) feature_dict['iqr1'] = np.subtract(*np.percentile(x, [95, 5])) feature_dict['ave10'] = stats.trim_mean(x, 0.1) for slice_length, threshold in product([50000, 100000, 150000], [5, 10, 20, 50, 100]): feature_dict[f'count_big_{slice_length}_threshold_{threshold}'] = ( np.abs(x[-slice_length:]) > threshold).sum() feature_dict[ f'count_big_{slice_length}_less_threshold_{threshold}'] = ( np.abs(x[-slice_length:]) < threshold).sum() feature_dict['range_minf_m4000'] = feature_calculators.range_count( x, -np.inf, -4000) feature_dict['range_p4000_pinf'] = feature_calculators.range_count( x, 4000, np.inf) for i, j in zip(borders, borders[1:]): feature_dict[f'range_{i}_{j}'] = feature_calculators.range_count( x, i, j) for autocorr_lag in autocorr_lags: feature_dict[ f'autocorrelation_{autocorr_lag}'] = feature_calculators.autocorrelation( x, autocorr_lag) feature_dict[f'c3_{autocorr_lag}'] = feature_calculators.c3( x, autocorr_lag) for p in percentiles: feature_dict[ f'binned_entropy_{p}'] = feature_calculators.binned_entropy( x, p) feature_dict['num_crossing_0'] = feature_calculators.number_crossing_m( x, 0) for peak in peaks: feature_dict[ f'num_peaks_{peak}'] = feature_calculators.number_peaks( x, peak) for c in coefs: feature_dict[f'spkt_welch_density_{c}'] = \ list(feature_calculators.spkt_welch_density(x, [{'coeff': c}]))[0][1] feature_dict[ f'time_rev_asym_stat_{c}'] = feature_calculators.time_reversal_asymmetry_statistic( x, c) for w in windows: x_roll_std = x.rolling(w).std().dropna().values x_roll_mean = x.rolling(w).mean().dropna().values feature_dict[f'ave_roll_std_{w}'] = x_roll_std.mean() feature_dict[f'std_roll_std_{w}'] = x_roll_std.std() feature_dict[f'max_roll_std_{w}'] = x_roll_std.max() feature_dict[f'min_roll_std_{w}'] = x_roll_std.min() for p in percentiles: feature_dict[ f'percentile_roll_std_{p}_window_{w}'] = np.percentile( x_roll_std, p) feature_dict[f'av_change_abs_roll_std_{w}'] = np.mean( np.diff(x_roll_std)) feature_dict[f'av_change_rate_roll_std_{w}'] = np.mean( np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0]) feature_dict[f'abs_max_roll_std_{w}'] = np.abs(x_roll_std).max() feature_dict[f'ave_roll_mean_{w}'] = x_roll_mean.mean() feature_dict[f'std_roll_mean_{w}'] = x_roll_mean.std() feature_dict[f'max_roll_mean_{w}'] = x_roll_mean.max() feature_dict[f'min_roll_mean_{w}'] = x_roll_mean.min() for p in percentiles: feature_dict[ f'percentile_roll_mean_{p}_window_{w}'] = np.percentile( x_roll_mean, p) feature_dict[f'av_change_abs_roll_mean_{w}'] = np.mean( np.diff(x_roll_mean)) feature_dict[f'av_change_rate_roll_mean_{w}'] = np.mean( np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0]) feature_dict[f'abs_max_roll_mean_{w}'] = np.abs(x_roll_mean).max() # Mel-frequency cepstral coefficients (MFCCs) x = x.values.astype('float32') mfcc = librosa.feature.mfcc(y=x) for i in range(len(mfcc)): feature_dict[f'mfcc_{i}_avg'] = np.mean(np.abs(mfcc[i])) # spectral features feature_dict['spectral_centroid'] = np.mean( np.abs(librosa.feature.spectral_centroid(y=x)[0])) feature_dict['zero_crossing_rate'] = np.mean( np.abs(librosa.feature.zero_crossing_rate(y=x)[0])) feature_dict['spectral_flatness'] = np.mean( np.abs(librosa.feature.spectral_flatness(y=x)[0])) feature_dict['spectral_contrast'] = np.mean( np.abs( librosa.feature.spectral_contrast( S=np.abs(librosa.stft(x)))[0])) feature_dict['spectral_bandwidth'] = np.mean( np.abs(librosa.feature.spectral_bandwidth(y=x)[0])) return feature_dict
def function(x): return time_reversal_asymmetry_statistic(x, lag=self.lag)
def features(self, x, prefix): feature_dict = dict() # create features here # numpy feature_dict[prefix + '_' + 'mean'] = np.mean(x) feature_dict[prefix + '_' + 'max'] = np.max(x) feature_dict[prefix + '_' + 'min'] = np.min(x) feature_dict[prefix + '_' + 'std'] = np.std(x) feature_dict[prefix + '_' + 'var'] = np.var(x) feature_dict[prefix + '_' + 'ptp'] = np.ptp(x) feature_dict[prefix + '_' + 'percentile_10'] = np.percentile(x, 10) feature_dict[prefix + '_' + 'percentile_20'] = np.percentile(x, 20) feature_dict[prefix + '_' + 'percentile_30'] = np.percentile(x, 30) feature_dict[prefix + '_' + 'percentile_40'] = np.percentile(x, 40) feature_dict[prefix + '_' + 'percentile_50'] = np.percentile(x, 50) feature_dict[prefix + '_' + 'percentile_60'] = np.percentile(x, 60) feature_dict[prefix + '_' + 'percentile_70'] = np.percentile(x, 70) feature_dict[prefix + '_' + 'percentile_80'] = np.percentile(x, 80) feature_dict[prefix + '_' + 'percentile_90'] = np.percentile(x, 90) # scipy feature_dict[prefix + '_' + 'skew'] = sp.stats.skew(x) feature_dict[prefix + '_' + 'kurtosis'] = sp.stats.kurtosis(x) feature_dict[prefix + '_' + 'kstat_1'] = sp.stats.kstat(x, 1) feature_dict[prefix + '_' + 'kstat_2'] = sp.stats.kstat(x, 2) feature_dict[prefix + '_' + 'kstat_3'] = sp.stats.kstat(x, 3) feature_dict[prefix + '_' + 'kstat_4'] = sp.stats.kstat(x, 4) feature_dict[prefix + '_' + 'moment_1'] = sp.stats.moment(x, 1) feature_dict[prefix + '_' + 'moment_2'] = sp.stats.moment(x, 2) feature_dict[prefix + '_' + 'moment_3'] = sp.stats.moment(x, 3) feature_dict[prefix + '_' + 'moment_4'] = sp.stats.moment(x, 4) # tsfresh feature_dict[prefix + '_' + 'abs_energy'] = feature_calculators.abs_energy(x) feature_dict[ prefix + '_' + 'abs_sum_of_changes'] = feature_calculators.absolute_sum_of_changes( x) feature_dict[ prefix + '_' + 'count_above_mean'] = feature_calculators.count_above_mean(x) feature_dict[ prefix + '_' + 'count_below_mean'] = feature_calculators.count_below_mean(x) feature_dict[prefix + '_' + 'mean_abs_change'] = feature_calculators.mean_abs_change( x) feature_dict[prefix + '_' + 'mean_change'] = feature_calculators.mean_change(x) feature_dict[ prefix + '_' + 'var_larger_than_std_dev'] = feature_calculators.variance_larger_than_standard_deviation( x) feature_dict[prefix + '_' + 'range_minf_m4000'] = feature_calculators.range_count( x, -np.inf, -4000) feature_dict[prefix + '_' + 'range_m4000_m3000'] = feature_calculators.range_count( x, -4000, -3000) feature_dict[prefix + '_' + 'range_m3000_m2000'] = feature_calculators.range_count( x, -3000, -2000) feature_dict[prefix + '_' + 'range_m2000_m1000'] = feature_calculators.range_count( x, -2000, -1000) feature_dict[prefix + '_' + 'range_m1000_0'] = feature_calculators.range_count( x, -1000, 0) feature_dict[prefix + '_' + 'range_0_p1000'] = feature_calculators.range_count( x, 0, 1000) feature_dict[prefix + '_' + 'range_p1000_p2000'] = feature_calculators.range_count( x, 1000, 2000) feature_dict[prefix + '_' + 'range_p2000_p3000'] = feature_calculators.range_count( x, 2000, 3000) feature_dict[prefix + '_' + 'range_p3000_p4000'] = feature_calculators.range_count( x, 3000, 4000) feature_dict[prefix + '_' + 'range_p4000_pinf'] = feature_calculators.range_count( x, 4000, np.inf) feature_dict[ prefix + '_' + 'ratio_unique_values'] = feature_calculators.ratio_value_number_to_time_series_length( x) feature_dict[ prefix + '_' + 'first_loc_min'] = feature_calculators.first_location_of_minimum(x) feature_dict[ prefix + '_' + 'first_loc_max'] = feature_calculators.first_location_of_maximum(x) feature_dict[ prefix + '_' + 'last_loc_min'] = feature_calculators.last_location_of_minimum(x) feature_dict[ prefix + '_' + 'last_loc_max'] = feature_calculators.last_location_of_maximum(x) feature_dict[ prefix + '_' + 'time_rev_asym_stat_10'] = feature_calculators.time_reversal_asymmetry_statistic( x, 10) feature_dict[ prefix + '_' + 'time_rev_asym_stat_100'] = feature_calculators.time_reversal_asymmetry_statistic( x, 100) feature_dict[ prefix + '_' + 'time_rev_asym_stat_1000'] = feature_calculators.time_reversal_asymmetry_statistic( x, 1000) feature_dict[ prefix + '_' + 'autocorrelation_1'] = feature_calculators.autocorrelation(x, 1) feature_dict[ prefix + '_' + 'autocorrelation_2'] = feature_calculators.autocorrelation(x, 2) feature_dict[ prefix + '_' + 'autocorrelation_3'] = feature_calculators.autocorrelation(x, 3) feature_dict[ prefix + '_' + 'autocorrelation_4'] = feature_calculators.autocorrelation(x, 4) feature_dict[ prefix + '_' + 'autocorrelation_5'] = feature_calculators.autocorrelation(x, 5) feature_dict[ prefix + '_' + 'autocorrelation_6'] = feature_calculators.autocorrelation(x, 6) feature_dict[ prefix + '_' + 'autocorrelation_7'] = feature_calculators.autocorrelation(x, 7) feature_dict[ prefix + '_' + 'autocorrelation_8'] = feature_calculators.autocorrelation(x, 8) feature_dict[ prefix + '_' + 'autocorrelation_9'] = feature_calculators.autocorrelation(x, 9) feature_dict[ prefix + '_' + 'autocorrelation_10'] = feature_calculators.autocorrelation(x, 10) feature_dict[ prefix + '_' + 'autocorrelation_50'] = feature_calculators.autocorrelation(x, 50) feature_dict[ prefix + '_' + 'autocorrelation_100'] = feature_calculators.autocorrelation( x, 100) feature_dict[ prefix + '_' + 'autocorrelation_1000'] = feature_calculators.autocorrelation( x, 1000) feature_dict[prefix + '_' + 'c3_1'] = feature_calculators.c3(x, 1) feature_dict[prefix + '_' + 'c3_2'] = feature_calculators.c3(x, 2) feature_dict[prefix + '_' + 'c3_3'] = feature_calculators.c3(x, 3) feature_dict[prefix + '_' + 'c3_4'] = feature_calculators.c3(x, 4) feature_dict[prefix + '_' + 'c3_5'] = feature_calculators.c3(x, 5) feature_dict[prefix + '_' + 'c3_10'] = feature_calculators.c3(x, 10) feature_dict[prefix + '_' + 'c3_100'] = feature_calculators.c3(x, 100) for c in range(1, 34): feature_dict[prefix + '_' + 'fft_{0}_real'.format(c)] = list( feature_calculators.fft_coefficient(x, [{ 'coeff': c, 'attr': 'real' }]))[0][1] feature_dict[prefix + '_' + 'fft_{0}_imag'.format(c)] = list( feature_calculators.fft_coefficient(x, [{ 'coeff': c, 'attr': 'imag' }]))[0][1] feature_dict[prefix + '_' + 'fft_{0}_ang'.format(c)] = list( feature_calculators.fft_coefficient(x, [{ 'coeff': c, 'attr': 'angle' }]))[0][1] feature_dict[ prefix + '_' + 'long_strk_above_mean'] = feature_calculators.longest_strike_above_mean( x) feature_dict[ prefix + '_' + 'long_strk_below_mean'] = feature_calculators.longest_strike_below_mean( x) feature_dict[prefix + '_' + 'cid_ce_0'] = feature_calculators.cid_ce( x, 0) feature_dict[prefix + '_' + 'cid_ce_1'] = feature_calculators.cid_ce( x, 1) feature_dict[prefix + '_' + 'binned_entropy_5'] = feature_calculators.binned_entropy( x, 5) feature_dict[prefix + '_' + 'binned_entropy_10'] = feature_calculators.binned_entropy( x, 10) feature_dict[prefix + '_' + 'binned_entropy_20'] = feature_calculators.binned_entropy( x, 20) feature_dict[prefix + '_' + 'binned_entropy_50'] = feature_calculators.binned_entropy( x, 50) feature_dict[prefix + '_' + 'binned_entropy_80'] = feature_calculators.binned_entropy( x, 80) feature_dict[ prefix + '_' + 'binned_entropy_100'] = feature_calculators.binned_entropy(x, 100) feature_dict[prefix + '_' + 'num_crossing_0'] = feature_calculators.number_crossing_m( x, 0) feature_dict[prefix + '_' + 'num_peaks_1'] = feature_calculators.number_peaks(x, 1) feature_dict[prefix + '_' + 'num_peaks_3'] = feature_calculators.number_peaks(x, 3) feature_dict[prefix + '_' + 'num_peaks_5'] = feature_calculators.number_peaks(x, 5) feature_dict[prefix + '_' + 'num_peaks_10'] = feature_calculators.number_peaks(x, 10) feature_dict[prefix + '_' + 'num_peaks_50'] = feature_calculators.number_peaks(x, 50) feature_dict[prefix + '_' + 'num_peaks_100'] = feature_calculators.number_peaks( x, 100) feature_dict[prefix + '_' + 'num_peaks_500'] = feature_calculators.number_peaks( x, 500) feature_dict[prefix + '_' + 'spkt_welch_density_1'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 1 }]))[0][1] feature_dict[prefix + '_' + 'spkt_welch_density_2'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 2 }]))[0][1] feature_dict[prefix + '_' + 'spkt_welch_density_5'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 5 }]))[0][1] feature_dict[prefix + '_' + 'spkt_welch_density_8'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 8 }]))[0][1] feature_dict[prefix + '_' + 'spkt_welch_density_10'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 10 }]))[0][1] feature_dict[prefix + '_' + 'spkt_welch_density_50'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 50 }]))[0][1] feature_dict[prefix + '_' + 'spkt_welch_density_100'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 100 }]))[0][1] feature_dict[ prefix + '_' + 'time_rev_asym_stat_1'] = feature_calculators.time_reversal_asymmetry_statistic( x, 1) feature_dict[ prefix + '_' + 'time_rev_asym_stat_2'] = feature_calculators.time_reversal_asymmetry_statistic( x, 2) feature_dict[ prefix + '_' + 'time_rev_asym_stat_3'] = feature_calculators.time_reversal_asymmetry_statistic( x, 3) feature_dict[ prefix + '_' + 'time_rev_asym_stat_4'] = feature_calculators.time_reversal_asymmetry_statistic( x, 4) feature_dict[ prefix + '_' + 'time_rev_asym_stat_10'] = feature_calculators.time_reversal_asymmetry_statistic( x, 10) feature_dict[ prefix + '_' + 'time_rev_asym_stat_100'] = feature_calculators.time_reversal_asymmetry_statistic( x, 100) for r in range(20): feature_dict[prefix + '_' + 'symmetry_looking_' + str(r)] = feature_calculators.symmetry_looking( x, [{ 'r': r * 0.05 }])[0][1] for r in range(1, 20): feature_dict[ prefix + '_' + 'large_standard_deviation_' + str(r)] = feature_calculators.large_standard_deviation( x, r * 0.05) for r in range(1, 10): feature_dict[prefix + '_' + 'quantile_' + str(r)] = feature_calculators.quantile(x, r * 0.1) for r in ['mean', 'median', 'var']: feature_dict[prefix + '_' + 'agg_autocorr_' + r] = feature_calculators.agg_autocorrelation( x, [{ 'f_agg': r, 'maxlag': 40 }])[0][-1] #for r in range(1, 6): # feature_dict[prefix+'_'+'number_cwt_peaks_'+str(r)] = feature_calculators.number_cwt_peaks(x, r) for r in range(1, 10): feature_dict[prefix + '_' + 'index_mass_quantile_' + str(r)] = feature_calculators.index_mass_quantile( x, [{ 'q': r }])[0][1] #for ql in [0., .2, .4, .6, .8]: # for qh in [.2, .4, .6, .8, 1.]: # if ql < qh: # for b in [False, True]: # for f in ["mean", "var"]: # feature_dict[prefix+'_'+'change_quantiles_'+str(ql)+'_'+str(qh)+'_'+str(b)+'_'+str(f)] = feature_calculators.change_quantiles(x, ql, qh, b, f) #for r in [.1, .3, .5, .7, .9]: # feature_dict[prefix+'_'+'approximate_entropy_'+str(r)] = feature_calculators.approximate_entropy(x, 2, r) feature_dict[ prefix + '_' + 'max_langevin_fixed_point'] = feature_calculators.max_langevin_fixed_point( x, 3, 30) for r in ['pvalue', 'rvalue', 'intercept', 'slope', 'stderr']: feature_dict[prefix + '_' + 'linear_trend_' + str(r)] = feature_calculators.linear_trend( x, [{ 'attr': r }])[0][1] for r in ['pvalue', 'teststat', 'usedlag']: feature_dict[prefix + '_' + 'augmented_dickey_fuller_' + r] = feature_calculators.augmented_dickey_fuller( x, [{ 'attr': r }])[0][1] for r in [0.5, 1, 1.5, 2, 2.5, 3, 5, 6, 7, 10]: feature_dict[prefix + '_' + 'ratio_beyond_r_sigma_' + str(r)] = feature_calculators.ratio_beyond_r_sigma( x, r) #for attr in ["pvalue", "rvalue", "intercept", "slope", "stderr"]: # feature_dict[prefix+'_'+'linear_trend_timewise_'+attr] = feature_calculators.linear_trend_timewise(x, [{'attr': attr}])[0][1] #for attr in ["rvalue", "intercept", "slope", "stderr"]: # for i in [5, 10, 50]: # for f in ["max", "min", "mean", "var"]: # feature_dict[prefix+'_'+'agg_linear_trend_'+attr+'_'+str(i)+'_'+f] = feature_calculators.agg_linear_trend(x, [{'attr': attr, 'chunk_len': i, 'f_agg': f}])[0][-1] #for width in [2, 5, 10, 20]: # for coeff in range(15): # for w in [2, 5, 10, 20]: # feature_dict[prefix+'_'+'cwt_coefficients_'+str(width)+'_'+str(coeff)+'_'+str(w)] = list(feature_calculators.cwt_coefficients(x, [{'widths': width, 'coeff': coeff, 'w': w}]))[0][1] #for r in range(10): # feature_dict[prefix+'_'+'partial_autocorr_'+str(r)] = feature_calculators.partial_autocorrelation(x, [{'lag': r}])[0][1] # "ar_coefficient": [{"coeff": coeff, "k": k} for coeff in range(5) for k in [10]], # "fft_coefficient": [{"coeff": k, "attr": a} for a, k in product(["real", "imag", "abs", "angle"], range(100))], # "fft_aggregated": [{"aggtype": s} for s in ["centroid", "variance", "skew", "kurtosis"]], # "value_count": [{"value": value} for value in [0, 1, -1]], # "range_count": [{"min": -1, "max": 1}, {"min": 1e12, "max": 0}, {"min": 0, "max": 1e12}], # "friedrich_coefficients": (lambda m: [{"coeff": coeff, "m": m, "r": 30} for coeff in range(m + 1)])(3), # "energy_ratio_by_chunks": [{"num_segments": 10, "segment_focus": i} for i in range(10)], return feature_dict
def get_time_reversal(arr): res = np.array([time_reversal_asymmetry_statistic(arr, lag=1)]) res = np.nan_to_num(res) return res
def features(self, x, y, seg_id): feature_dict = dict() feature_dict['target'] = y feature_dict['seg_id'] = seg_id # create features here # numpy feature_dict['mean'] = np.mean(x) feature_dict['max'] = np.max(x) feature_dict['min'] = np.min(x) feature_dict['std'] = np.std(x) feature_dict['var'] = np.var(x) feature_dict['ptp'] = np.ptp(x) feature_dict['percentile_10'] = np.percentile(x, 10) feature_dict['percentile_20'] = np.percentile(x, 20) feature_dict['percentile_30'] = np.percentile(x, 30) feature_dict['percentile_40'] = np.percentile(x, 40) feature_dict['percentile_50'] = np.percentile(x, 50) feature_dict['percentile_60'] = np.percentile(x, 60) feature_dict['percentile_70'] = np.percentile(x, 70) feature_dict['percentile_80'] = np.percentile(x, 80) feature_dict['percentile_90'] = np.percentile(x, 90) # scipy feature_dict['skew'] = sp.stats.skew(x) feature_dict['kurtosis'] = sp.stats.kurtosis(x) feature_dict['kstat_1'] = sp.stats.kstat(x, 1) feature_dict['kstat_2'] = sp.stats.kstat(x, 2) feature_dict['kstat_3'] = sp.stats.kstat(x, 3) feature_dict['kstat_4'] = sp.stats.kstat(x, 4) feature_dict['moment_1'] = sp.stats.moment(x, 1) feature_dict['moment_2'] = sp.stats.moment(x, 2) feature_dict['moment_3'] = sp.stats.moment(x, 3) feature_dict['moment_4'] = sp.stats.moment(x, 4) feature_dict['abs_energy'] = feature_calculators.abs_energy(x) feature_dict['abs_sum_of_changes'] = feature_calculators.absolute_sum_of_changes(x) feature_dict['count_above_mean'] = feature_calculators.count_above_mean(x) feature_dict['count_below_mean'] = feature_calculators.count_below_mean(x) feature_dict['mean_abs_change'] = feature_calculators.mean_abs_change(x) feature_dict['mean_change'] = feature_calculators.mean_change(x) feature_dict['var_larger_than_std_dev'] = feature_calculators.variance_larger_than_standard_deviation(x) feature_dict['range_minf_m4000'] = feature_calculators.range_count(x, -np.inf, -4000) feature_dict['range_m4000_m3000'] = feature_calculators.range_count(x, -4000, -3000) feature_dict['range_m3000_m2000'] = feature_calculators.range_count(x, -3000, -2000) feature_dict['range_m2000_m1000'] = feature_calculators.range_count(x, -2000, -1000) feature_dict['range_m1000_0'] = feature_calculators.range_count(x, -1000, 0) feature_dict['range_0_p1000'] = feature_calculators.range_count(x, 0, 1000) feature_dict['range_p1000_p2000'] = feature_calculators.range_count(x, 1000, 2000) feature_dict['range_p2000_p3000'] = feature_calculators.range_count(x, 2000, 3000) feature_dict['range_p3000_p4000'] = feature_calculators.range_count(x, 3000, 4000) feature_dict['range_p4000_pinf'] = feature_calculators.range_count(x, 4000, np.inf) feature_dict['ratio_unique_values'] = feature_calculators.ratio_value_number_to_time_series_length(x) feature_dict['first_loc_min'] = feature_calculators.first_location_of_minimum(x) feature_dict['first_loc_max'] = feature_calculators.first_location_of_maximum(x) feature_dict['last_loc_min'] = feature_calculators.last_location_of_minimum(x) feature_dict['last_loc_max'] = feature_calculators.last_location_of_maximum(x) feature_dict['time_rev_asym_stat_10'] = feature_calculators.time_reversal_asymmetry_statistic(x, 10) feature_dict['time_rev_asym_stat_100'] = feature_calculators.time_reversal_asymmetry_statistic(x, 100) feature_dict['time_rev_asym_stat_1000'] = feature_calculators.time_reversal_asymmetry_statistic(x, 1000) feature_dict['autocorrelation_5'] = feature_calculators.autocorrelation(x, 5) feature_dict['autocorrelation_10'] = feature_calculators.autocorrelation(x, 10) feature_dict['autocorrelation_50'] = feature_calculators.autocorrelation(x, 50) feature_dict['autocorrelation_100'] = feature_calculators.autocorrelation(x, 100) feature_dict['autocorrelation_1000'] = feature_calculators.autocorrelation(x, 1000) feature_dict['c3_5'] = feature_calculators.c3(x, 5) feature_dict['c3_10'] = feature_calculators.c3(x, 10) feature_dict['c3_100'] = feature_calculators.c3(x, 100) feature_dict['fft_1_real'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 1, 'attr': 'real'}]))[0][1] feature_dict['fft_1_imag'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 1, 'attr': 'imag'}]))[0][1] feature_dict['fft_1_ang'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 1, 'attr': 'angle'}]))[0][1] feature_dict['fft_2_real'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 2, 'attr': 'real'}]))[0][1] feature_dict['fft_2_imag'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 2, 'attr': 'imag'}]))[0][1] feature_dict['fft_2_ang'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 2, 'attr': 'angle'}]))[0][1] feature_dict['fft_3_real'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 3, 'attr': 'real'}]))[0][1] feature_dict['fft_3_imag'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 3, 'attr': 'imag'}]))[0][1] feature_dict['fft_3_ang'] = list(feature_calculators.fft_coefficient(x, [{'coeff': 3, 'attr': 'angle'}]))[0][1] feature_dict['long_strk_above_mean'] = feature_calculators.longest_strike_above_mean(x) feature_dict['long_strk_below_mean'] = feature_calculators.longest_strike_below_mean(x) feature_dict['cid_ce_0'] = feature_calculators.cid_ce(x, 0) feature_dict['cid_ce_1'] = feature_calculators.cid_ce(x, 1) feature_dict['binned_entropy_5'] = feature_calculators.binned_entropy(x, 5) feature_dict['binned_entropy_10'] = feature_calculators.binned_entropy(x, 10) feature_dict['binned_entropy_20'] = feature_calculators.binned_entropy(x, 20) feature_dict['binned_entropy_50'] = feature_calculators.binned_entropy(x, 50) feature_dict['binned_entropy_80'] = feature_calculators.binned_entropy(x, 80) feature_dict['binned_entropy_100'] = feature_calculators.binned_entropy(x, 100) feature_dict['num_crossing_0'] = feature_calculators.number_crossing_m(x, 0) feature_dict['num_peaks_10'] = feature_calculators.number_peaks(x, 10) feature_dict['num_peaks_50'] = feature_calculators.number_peaks(x, 50) feature_dict['num_peaks_100'] = feature_calculators.number_peaks(x, 100) feature_dict['num_peaks_500'] = feature_calculators.number_peaks(x, 500) feature_dict['spkt_welch_density_1'] = list(feature_calculators.spkt_welch_density(x, [{'coeff': 1}]))[0][1] feature_dict['spkt_welch_density_10'] = list(feature_calculators.spkt_welch_density(x, [{'coeff': 10}]))[0][1] feature_dict['spkt_welch_density_50'] = list(feature_calculators.spkt_welch_density(x, [{'coeff': 50}]))[0][1] feature_dict['spkt_welch_density_100'] = list(feature_calculators.spkt_welch_density(x, [{'coeff': 100}]))[0][1] feature_dict['time_rev_asym_stat_1'] = feature_calculators.time_reversal_asymmetry_statistic(x, 1) feature_dict['time_rev_asym_stat_10'] = feature_calculators.time_reversal_asymmetry_statistic(x, 10) feature_dict['time_rev_asym_stat_100'] = feature_calculators.time_reversal_asymmetry_statistic(x, 100) return feature_dict
def features(self, x, y, seg_id): feature_dict = dict() feature_dict['target'] = y feature_dict['seg_id'] = seg_id # create features here # lists with parameters to iterate over them percentiles = [ 1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99 ] hann_windows = [50, 150, 1500, 15000] spans = [300, 3000, 30000, 50000] windows = [10, 50, 100, 500, 1000, 10000] borders = list(range(-4000, 4001, 1000)) peaks = [10, 20, 50, 100] coefs = [1, 5, 10, 50, 100] lags = [10, 100, 1000, 10000] autocorr_lags = [5, 10, 50, 100, 500, 1000, 5000, 10000] # basic stats feature_dict['mean'] = x.mean() feature_dict['std'] = x.std() feature_dict['max'] = x.max() feature_dict['min'] = x.min() # basic stats on absolute values feature_dict['mean_change_abs'] = np.mean(np.diff(x)) feature_dict['abs_max'] = np.abs(x).max() feature_dict['abs_mean'] = np.abs(x).mean() feature_dict['abs_std'] = np.abs(x).std() # geometric and harminic means feature_dict['hmean'] = stats.hmean(np.abs(x[np.nonzero(x)[0]])) feature_dict['gmean'] = stats.gmean(np.abs(x[np.nonzero(x)[0]])) # k-statistic and moments for i in range(1, 5): feature_dict[f'kstat_{i}'] = stats.kstat(x, i) feature_dict[f'moment_{i}'] = stats.moment(x, i) for i in [1, 2]: feature_dict[f'kstatvar_{i}'] = stats.kstatvar(x, i) # aggregations on various slices of data for agg_type, slice_length, direction in product( ['std', 'min', 'max', 'mean'], [1000, 10000, 50000], ['first', 'last']): if direction == 'first': feature_dict[ f'{agg_type}_{direction}_{slice_length}'] = x[: slice_length].agg( agg_type) elif direction == 'last': feature_dict[f'{agg_type}_{direction}_{slice_length}'] = x[ -slice_length:].agg(agg_type) feature_dict['max_to_min'] = x.max() / np.abs(x.min()) feature_dict['max_to_min_diff'] = x.max() - np.abs(x.min()) feature_dict['count_big'] = len(x[np.abs(x) > 500]) feature_dict['sum'] = x.sum() feature_dict['mean_change_rate'] = calc_change_rate(x) # calc_change_rate on slices of data for slice_length, direction in product([1000, 10000, 50000], ['first', 'last']): if direction == 'first': feature_dict[ f'mean_change_rate_{direction}_{slice_length}'] = calc_change_rate( x[:slice_length]) elif direction == 'last': feature_dict[ f'mean_change_rate_{direction}_{slice_length}'] = calc_change_rate( x[-slice_length:]) # percentiles on original and absolute values for p in percentiles: feature_dict[f'percentile_{p}'] = np.percentile(x, p) feature_dict[f'abs_percentile_{p}'] = np.percentile(np.abs(x), p) feature_dict['trend'] = add_trend_feature(x) feature_dict['abs_trend'] = add_trend_feature(x, abs_values=True) feature_dict['mad'] = x.mad() feature_dict['kurt'] = x.kurtosis() feature_dict['skew'] = x.skew() feature_dict['med'] = x.median() feature_dict['Hilbert_mean'] = np.abs(hilbert(x)).mean() for hw in hann_windows: feature_dict[f'Hann_window_mean_{hw}'] = ( convolve(x, hann(hw), mode='same') / sum(hann(hw))).mean() feature_dict['classic_sta_lta1_mean'] = classic_sta_lta(x, 500, 10000).mean() feature_dict['classic_sta_lta2_mean'] = classic_sta_lta( x, 5000, 100000).mean() feature_dict['classic_sta_lta3_mean'] = classic_sta_lta(x, 3333, 6666).mean() feature_dict['classic_sta_lta4_mean'] = classic_sta_lta( x, 10000, 25000).mean() feature_dict['classic_sta_lta5_mean'] = classic_sta_lta(x, 50, 1000).mean() feature_dict['classic_sta_lta6_mean'] = classic_sta_lta(x, 100, 5000).mean() feature_dict['classic_sta_lta7_mean'] = classic_sta_lta(x, 333, 666).mean() feature_dict['classic_sta_lta8_mean'] = classic_sta_lta( x, 4000, 10000).mean() # exponential rolling statistics ewma = pd.Series.ewm for s in spans: feature_dict[f'exp_Moving_average_{s}_mean'] = (ewma( x, span=s).mean(skipna=True)).mean(skipna=True) feature_dict[f'exp_Moving_average_{s}_std'] = (ewma( x, span=s).mean(skipna=True)).std(skipna=True) feature_dict[f'exp_Moving_std_{s}_mean'] = (ewma( x, span=s).std(skipna=True)).mean(skipna=True) feature_dict[f'exp_Moving_std_{s}_std'] = (ewma( x, span=s).std(skipna=True)).std(skipna=True) feature_dict['iqr'] = np.subtract(*np.percentile(x, [75, 25])) feature_dict['iqr1'] = np.subtract(*np.percentile(x, [95, 5])) feature_dict['ave10'] = stats.trim_mean(x, 0.1) for slice_length, threshold in product([50000, 100000, 150000], [5, 10, 20, 50, 100]): feature_dict[f'count_big_{slice_length}_threshold_{threshold}'] = ( np.abs(x[-slice_length:]) > threshold).sum() feature_dict[ f'count_big_{slice_length}_less_threshold_{threshold}'] = ( np.abs(x[-slice_length:]) < threshold).sum() # tfresh features take too long to calculate, so I comment them for now # feature_dict['abs_energy'] = feature_calculators.abs_energy(x) # feature_dict['abs_sum_of_changes'] = feature_calculators.absolute_sum_of_changes(x) # feature_dict['count_above_mean'] = feature_calculators.count_above_mean(x) # feature_dict['count_below_mean'] = feature_calculators.count_below_mean(x) # feature_dict['mean_abs_change'] = feature_calculators.mean_abs_change(x) # feature_dict['mean_change'] = feature_calculators.mean_change(x) # feature_dict['var_larger_than_std_dev'] = feature_calculators.variance_larger_than_standard_deviation(x) feature_dict['range_minf_m4000'] = feature_calculators.range_count( x, -np.inf, -4000) feature_dict['range_p4000_pinf'] = feature_calculators.range_count( x, 4000, np.inf) for i, j in zip(borders, borders[1:]): feature_dict[f'range_{i}_{j}'] = feature_calculators.range_count( x, i, j) # feature_dict['ratio_unique_values'] = feature_calculators.ratio_value_number_to_time_series_length(x) # feature_dict['first_loc_min'] = feature_calculators.first_location_of_minimum(x) # feature_dict['first_loc_max'] = feature_calculators.first_location_of_maximum(x) # feature_dict['last_loc_min'] = feature_calculators.last_location_of_minimum(x) # feature_dict['last_loc_max'] = feature_calculators.last_location_of_maximum(x) # for lag in lags: # feature_dict[f'time_rev_asym_stat_{lag}'] = feature_calculators.time_reversal_asymmetry_statistic(x, lag) for autocorr_lag in autocorr_lags: feature_dict[ f'autocorrelation_{autocorr_lag}'] = feature_calculators.autocorrelation( x, autocorr_lag) feature_dict[f'c3_{autocorr_lag}'] = feature_calculators.c3( x, autocorr_lag) # for coeff, attr in product([1, 2, 3, 4, 5], ['real', 'imag', 'angle']): # feature_dict[f'fft_{coeff}_{attr}'] = list(feature_calculators.fft_coefficient(x, [{'coeff': coeff, 'attr': attr}]))[0][1] # feature_dict['long_strk_above_mean'] = feature_calculators.longest_strike_above_mean(x) # feature_dict['long_strk_below_mean'] = feature_calculators.longest_strike_below_mean(x) # feature_dict['cid_ce_0'] = feature_calculators.cid_ce(x, 0) # feature_dict['cid_ce_1'] = feature_calculators.cid_ce(x, 1) for p in percentiles: feature_dict[ f'binned_entropy_{p}'] = feature_calculators.binned_entropy( x, p) feature_dict['num_crossing_0'] = feature_calculators.number_crossing_m( x, 0) for peak in peaks: feature_dict[ f'num_peaks_{peak}'] = feature_calculators.number_peaks( x, peak) for c in coefs: feature_dict[f'spkt_welch_density_{c}'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': c }]))[0][1] feature_dict[ f'time_rev_asym_stat_{c}'] = feature_calculators.time_reversal_asymmetry_statistic( x, c) # statistics on rolling windows of various sizes for w in windows: x_roll_std = x.rolling(w).std().dropna().values x_roll_mean = x.rolling(w).mean().dropna().values feature_dict[f'ave_roll_std_{w}'] = x_roll_std.mean() feature_dict[f'std_roll_std_{w}'] = x_roll_std.std() feature_dict[f'max_roll_std_{w}'] = x_roll_std.max() feature_dict[f'min_roll_std_{w}'] = x_roll_std.min() for p in percentiles: feature_dict[ f'percentile_roll_std_{p}_window_{w}'] = np.percentile( x_roll_std, p) feature_dict[f'av_change_abs_roll_std_{w}'] = np.mean( np.diff(x_roll_std)) feature_dict[f'av_change_rate_roll_std_{w}'] = np.mean( np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0]) feature_dict[f'abs_max_roll_std_{w}'] = np.abs(x_roll_std).max() feature_dict[f'ave_roll_mean_{w}'] = x_roll_mean.mean() feature_dict[f'std_roll_mean_{w}'] = x_roll_mean.std() feature_dict[f'max_roll_mean_{w}'] = x_roll_mean.max() feature_dict[f'min_roll_mean_{w}'] = x_roll_mean.min() for p in percentiles: feature_dict[ f'percentile_roll_mean_{p}_window_{w}'] = np.percentile( x_roll_mean, p) feature_dict[f'av_change_abs_roll_mean_{w}'] = np.mean( np.diff(x_roll_mean)) feature_dict[f'av_change_rate_roll_mean_{w}'] = np.mean( np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0]) feature_dict[f'abs_max_roll_mean_{w}'] = np.abs(x_roll_mean).max() return feature_dict
def transform_pack3(df): """ augment X from tsfresh features""" x = df.values output = {} output['kstat_1'] = stats.kstat(x, 1) output['kstat_2'] = stats.kstat(x, 2) output['kstat_3'] = stats.kstat(x, 3) output['kstat_4'] = stats.kstat(x, 4) output['abs_energy'] = feature_calculators.abs_energy(x) output['abs_sum_of_changes'] = feature_calculators.absolute_sum_of_changes( x) output['count_above_mean'] = feature_calculators.count_above_mean(x) output['count_below_mean'] = feature_calculators.count_below_mean(x) output['range_minf_m4000'] = feature_calculators.range_count( x, -np.inf, -4000) output['range_m4000_m3000'] = feature_calculators.range_count( x, -4000, -3000) output['range_m3000_m2000'] = feature_calculators.range_count( x, -3000, -2000) output['range_m2000_m1000'] = feature_calculators.range_count( x, -2000, -1000) output['range_m1000_0'] = feature_calculators.range_count(x, -1000, 0) output['range_0_p1000'] = feature_calculators.range_count(x, 0, 1000) output['range_p1000_p2000'] = feature_calculators.range_count( x, 1000, 2000) output['range_p2000_p3000'] = feature_calculators.range_count( x, 2000, 3000) output['range_p3000_p4000'] = feature_calculators.range_count( x, 3000, 4000) output['range_p4000_pinf'] = feature_calculators.range_count( x, 4000, np.inf) output[ 'ratio_unique_values'] = feature_calculators.ratio_value_number_to_time_series_length( x) output['first_loc_min'] = feature_calculators.first_location_of_minimum(x) output['first_loc_max'] = feature_calculators.first_location_of_maximum(x) output['last_loc_min'] = feature_calculators.last_location_of_minimum(x) output['last_loc_max'] = feature_calculators.last_location_of_maximum(x) output[ 'time_rev_asym_stat_10'] = feature_calculators.time_reversal_asymmetry_statistic( x, 10) output[ 'time_rev_asym_stat_100'] = feature_calculators.time_reversal_asymmetry_statistic( x, 100) output[ 'time_rev_asym_stat_1000'] = feature_calculators.time_reversal_asymmetry_statistic( x, 1000) output['autocorrelation_10'] = feature_calculators.autocorrelation(x, 10) output['autocorrelation_100'] = feature_calculators.autocorrelation(x, 100) output['autocorrelation_1000'] = feature_calculators.autocorrelation( x, 1000) output['autocorrelation_5000'] = feature_calculators.autocorrelation( x, 5000) output['c3_5'] = feature_calculators.c3(x, 5) output['c3_10'] = feature_calculators.c3(x, 10) output['c3_100'] = feature_calculators.c3(x, 100) output[ 'long_strk_above_mean'] = feature_calculators.longest_strike_above_mean( x) output[ 'long_strk_below_mean'] = feature_calculators.longest_strike_below_mean( x) output['cid_ce_0'] = feature_calculators.cid_ce(x, 0) output['cid_ce_1'] = feature_calculators.cid_ce(x, 1) output['binned_entropy_10'] = feature_calculators.binned_entropy(x, 10) output['binned_entropy_50'] = feature_calculators.binned_entropy(x, 50) output['binned_entropy_80'] = feature_calculators.binned_entropy(x, 80) output['binned_entropy_100'] = feature_calculators.binned_entropy(x, 100) tmp = np.abs(x) output['num_crossing_0'] = feature_calculators.number_crossing_m(tmp, 0) output['num_crossing_10'] = feature_calculators.number_crossing_m(tmp, 10) output['num_crossing_100'] = feature_calculators.number_crossing_m( tmp, 100) output['num_peaks_10'] = feature_calculators.number_peaks(tmp, 10) output['num_peaks_50'] = feature_calculators.number_peaks(tmp, 50) output['num_peaks_100'] = feature_calculators.number_peaks(tmp, 100) output['num_peaks_500'] = feature_calculators.number_peaks(tmp, 500) output['spkt_welch_density_1'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 1 }]))[0][1] output['spkt_welch_density_10'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 10 }]))[0][1] output['spkt_welch_density_50'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 50 }]))[0][1] output['spkt_welch_density_100'] = list( feature_calculators.spkt_welch_density(x, [{ 'coeff': 100 }]))[0][1] output[ 'time_rev_asym_stat_1'] = feature_calculators.time_reversal_asymmetry_statistic( x, 1) output[ 'time_rev_asym_stat_10'] = feature_calculators.time_reversal_asymmetry_statistic( x, 10) output[ 'time_rev_asym_stat_100'] = feature_calculators.time_reversal_asymmetry_statistic( x, 100) return output
def create_features2(seg, ): data_row = {} xcz = des_filter(seg, high=CUTOFF) zc = np.fft.fft(xcz) zc = zc[:MAX_FREQ] # FFT transform values realFFT = np.real(zc) imagFFT = np.imag(zc) freq_bands = list(range(0, MAX_FREQ, FREQ_STEP)) magFFT = np.abs(zc) phzFFT = np.angle(zc) phzFFT[phzFFT == -np.inf] = -np.pi / 2.0 phzFFT[phzFFT == np.inf] = np.pi / 2.0 phzFFT = np.nan_to_num(phzFFT) for freq in freq_bands: data_row['FFT_Mag_01q%d' % freq] = np.quantile(magFFT[freq: freq + FREQ_STEP], 0.01) data_row['FFT_Mag_10q%d' % freq] = np.quantile(magFFT[freq: freq + FREQ_STEP], 0.1) data_row['FFT_Mag_90q%d' % freq] = np.quantile(magFFT[freq: freq + FREQ_STEP], 0.9) data_row['FFT_Mag_99q%d' % freq] = np.quantile(magFFT[freq: freq + FREQ_STEP], 0.99) data_row['FFT_Mag_mean%d' % freq] = np.mean(magFFT[freq: freq + FREQ_STEP]) data_row['FFT_Mag_std%d' % freq] = np.std(magFFT[freq: freq + FREQ_STEP]) data_row['FFT_Mag_max%d' % freq] = np.max(magFFT[freq: freq + FREQ_STEP]) data_row['FFT_Mag_min%d' % freq] = np.min(magFFT[freq: freq + FREQ_STEP]) data_row['FFT_Phz_mean%d' % freq] = np.mean(phzFFT[freq: freq + FREQ_STEP]) data_row['FFT_Phz_std%d' % freq] = np.std(phzFFT[freq: freq + FREQ_STEP]) data_row['FFT_Phz_max%d' % freq] = np.max(phzFFT[freq: freq + FREQ_STEP]) data_row['FFT_Phz_min%d' % freq] = np.min(phzFFT[freq: freq + FREQ_STEP]) data_row['FFT_Rmean'] = realFFT.mean() data_row['FFT_Rstd'] = realFFT.std() data_row['FFT_Rmax'] = realFFT.max() data_row['FFT_Rmin'] = realFFT.min() data_row['FFT_Imean'] = imagFFT.mean() data_row['FFT_Istd'] = imagFFT.std() data_row['FFT_Imax'] = imagFFT.max() data_row['FFT_Imin'] = imagFFT.min() data_row['FFT_Rmean_first_6000'] = realFFT[:6000].mean() data_row['FFT_Rstd__first_6000'] = realFFT[:6000].std() data_row['FFT_Rmax_first_6000'] = realFFT[:6000].max() data_row['FFT_Rmin_first_6000'] = realFFT[:6000].min() data_row['FFT_Rmean_first_18000'] = realFFT[:18000].mean() data_row['FFT_Rstd_first_18000'] = realFFT[:18000].std() data_row['FFT_Rmax_first_18000'] = realFFT[:18000].max() data_row['FFT_Rmin_first_18000'] = realFFT[:18000].min() del xcz del zc # gc.collect() sigs = [seg] for freq in range(0, MAX_FREQ + FREQ_STEP, FREQ_STEP): if freq == 0: xc_ = des_filter(seg, high=FREQ_STEP) elif freq == MAX_FREQ: xc_ = des_filter(seg, low=freq) else: xc_ = des_filter(seg, low=freq, high=freq + FREQ_STEP) sigs.append(pd.Series(xc_)) for window in [50, 200, 1000]: roll_mean = seg.rolling(window).mean().dropna() roll_std = seg.rolling(window).std().dropna() sigs.append(pd.Series(roll_mean)) sigs.append(pd.Series(roll_std)) for span in [30, 300, 3000]: exp_mean = seg.ewm(span).mean().dropna() exp_std = seg.ewm(span).std().dropna() sigs.append(pd.Series(exp_mean)) sigs.append(pd.Series(exp_std)) for i, sig in enumerate(sigs): data_row['mean_%d' % i] = sig.mean() data_row['std_%d' % i] = sig.std() data_row['max_%d' % i] = sig.max() data_row['min_%d' % i] = sig.min() data_row['mean_change_abs_%d' % i] = np.mean(np.diff(sig)) data_row['mean_change_rate_%d' % i] = np.mean(np.nonzero((np.diff(sig) / sig[:-1]))[0]) data_row['abs_max_%d' % i] = np.abs(sig).max() data_row['abs_min_%d' % i] = np.abs(sig).min() data_row['std_first_50000_%d' % i] = sig[:50000].std() data_row['std_last_50000_%d' % i] = sig[-50000:].std() data_row['std_first_10000_%d' % i] = sig[:10000].std() data_row['std_last_10000_%d' % i] = sig[-10000:].std() data_row['avg_first_50000_%d' % i] = sig[:50000].mean() data_row['avg_last_50000_%d' % i] = sig[-50000:].mean() data_row['avg_first_10000_%d' % i] = sig[:10000].mean() data_row['avg_last_10000_%d' % i] = sig[-10000:].mean() data_row['min_first_50000_%d' % i] = sig[:50000].min() data_row['min_last_50000_%d' % i] = sig[-50000:].min() data_row['min_first_10000_%d' % i] = sig[:10000].min() data_row['min_last_10000_%d' % i] = sig[-10000:].min() data_row['max_first_50000_%d' % i] = sig[:50000].max() data_row['max_last_50000_%d' % i] = sig[-50000:].max() data_row['max_first_10000_%d' % i] = sig[:10000].max() data_row['max_last_10000_%d' % i] = sig[-10000:].max() data_row['max_to_min_%d' % i] = sig.max() / np.abs(sig.min()) data_row['max_to_min_diff_%d' % i] = sig.max() - np.abs(sig.min()) data_row['count_big_%d' % i] = len(sig[np.abs(sig) > 500]) data_row['sum_%d' % i] = sig.sum() data_row['mean_change_rate_first_50000_%d' % i] = np.mean( np.nonzero((np.diff(sig[:50000]) / sig[:50000][:-1]))[0]) data_row['mean_change_rate_last_50000_%d' % i] = np.mean( np.nonzero((np.diff(sig[-50000:]) / sig[-50000:][:-1]))[0]) data_row['mean_change_rate_first_10000_%d' % i] = np.mean( np.nonzero((np.diff(sig[:10000]) / sig[:10000][:-1]))[0]) data_row['mean_change_rate_last_10000_%d' % i] = np.mean( np.nonzero((np.diff(sig[-10000:]) / sig[-10000:][:-1]))[0]) for p in [1, 5, 10, 25, 50, 75, 90, 95, 99]: data_row['percentile_p{}_{}'.format(p, i)] = np.percentile(sig, p) data_row['abd_percentile_p{}_{}'.format(p, i)] = np.percentile(np.abs(sig), p) data_row['trend_%d' % i] = add_trend_feature(sig) data_row['abs_trend_%d' % i] = add_trend_feature(sig, abs_values=True) data_row['abs_mean_%d' % i] = np.abs(sig).mean() data_row['abs_std_%d' % i] = np.abs(sig).std() data_row['mad_%d' % i] = sig.mad() data_row['kurt_%d' % i] = sig.kurtosis() data_row['skew_%d' % i] = sig.skew() data_row['med_%d' % i] = sig.median() # data_row['Hilbert_mean_%d' % i] = np.abs(hilbert(sig)).mean() data_row['Hann_window50_%d' % i] = (convolve(sig, hann(50), mode='same') / sum(hann(50))).mean() data_row['Hann_window500_%d' % i] = (convolve(sig, hann(500), mode='same') / sum(hann(500))).mean() data_row['classic_sta_lta0_mean_%d' % i] = classic_sta_lta(sig, 50, 1000).mean() data_row['classic_sta_lta1_mean_%d' % i] = classic_sta_lta(sig, 500, 10000).mean() data_row['classic_sta_lta2_mean_%d' % i] = classic_sta_lta(sig, 5000, 100000).mean() data_row['classic_sta_lta3_mean_%d' % i] = classic_sta_lta(sig, 3333, 6666).mean() data_row['classic_sta_lta4_mean_%d' % i] = classic_sta_lta(sig, 10000, 25000).mean() no_of_std = 2 for w in [10, 100, 500]: signal_mean = sig.rolling(window=w).mean() signal_std = sig.rolling(window=w).std() data_row['high_bound_mean_win{}_{}'.format(w, i)] = (signal_mean + no_of_std * signal_std).mean() data_row['low_bound_mean_win{}_{}'.format(w, i)] = (signal_mean - no_of_std * signal_std).mean() data_row['range_inf_4000_%d' % i] = feature_calculators.range_count(sig, -np.inf, -4000) data_row['range_4000_inf_%d' % i] = feature_calculators.range_count(sig, 4000, np.inf) for l, h in [[-4000, -2000], [-2000, 0], [0, 2000], [2000, 4000]]: data_row['range_{}_{}_{}'.format(np.abs(l), np.abs(h), i)] = feature_calculators.range_count(sig, l, h) data_row['iqr0_%d' % i] = np.subtract(*np.percentile(sig, [75, 25])) data_row['iqr1_%d' % i] = np.subtract(*np.percentile(sig, [95, 5])) data_row['ave10_%d' % i] = stats.trim_mean(sig, 0.1) data_row['num_cross_0_%d' % i] = feature_calculators.number_crossing_m(sig, 0) data_row['ratio_value_number_%d' % i] = feature_calculators.ratio_value_number_to_time_series_length(sig) # data_row['var_larger_than_std_dev_%d' % i] = feature_calculators.variance_larger_than_standard_deviation(sig) data_row['ratio_unique_values_%d' % i] = feature_calculators.ratio_value_number_to_time_series_length(sig) data_row['abs_energy_%d' % i] = feature_calculators.abs_energy(sig) data_row['abs_sum_of_changes_%d' % i] = feature_calculators.absolute_sum_of_changes(sig) data_row['count_above_mean_%d' % i] = feature_calculators.count_above_mean(sig) data_row['count_below_mean_%d' % i] = feature_calculators.count_below_mean(sig) data_row['mean_abs_change_%d' % i] = feature_calculators.mean_abs_change(sig) data_row['mean_change_%d' % i] = feature_calculators.mean_change(sig) data_row['first_loc_min_%d' % i] = feature_calculators.first_location_of_minimum(sig) data_row['first_loc_max_%d' % i] = feature_calculators.first_location_of_maximum(sig) data_row['last_loc_min_%d' % i] = feature_calculators.last_location_of_minimum(sig) data_row['last_loc_max_%d' % i] = feature_calculators.last_location_of_maximum(sig) data_row['long_strk_above_mean_%d' % i] = feature_calculators.longest_strike_above_mean(sig) data_row['long_strk_below_mean_%d' % i] = feature_calculators.longest_strike_below_mean(sig) # data_row['cid_ce_0_%d' % i] = feature_calculators.cid_ce(sig, 0) # data_row['cid_ce_1_%d' % i] = feature_calculators.cid_ce(sig, 1) for j in [10, 50, ]: data_row['peak_num_p{}_{}'.format(j, i)] = feature_calculators.number_peaks(sig, j) for j in [1, 10, 50, 100]: data_row['spkt_welch_density_coeff{}_{}'.format(j, i)] = \ list(feature_calculators.spkt_welch_density(sig, [{'coeff': j}]))[0][1] for j in [5, 10, 100]: data_row['c3_c{}_{}'.format(j, i)] = feature_calculators.c3(sig, j) for j in [5, 10, 50, 100, 1000]: data_row['autocorrelation_auto{}_{}'.format(j, i)] = feature_calculators.autocorrelation(sig, j) for j in [10, 100, 1000]: data_row['time_rev_asym_stat_t{}_{}'.format(j, i)] = feature_calculators.time_reversal_asymmetry_statistic( sig, j) for j in range(1, 5): data_row['kstat_k{}_{}'.format(j, i)] = stats.kstat(sig, j) data_row['moment_m{}_{}'.format(j, i)] = stats.moment(sig, j) for j in range(1, 3): data_row['kstatvar_k{}_{}'.format(j, i)] = stats.kstatvar(sig, j) for j in [5, 10, 50, 100]: data_row['binned_entropy_b{}_{}'.format(j, i)] = feature_calculators.binned_entropy(sig, j) return data_row