机器学习|暴力特征工程函数汇总，附代码|np|sum|beyond|compute|variance

简介近期一些朋友询问我关于如何做特征工程的问题，有没有什么适合初学者的有效操作。特征工程的问题往往需要具体问题具体分析，当然也有一些暴力的策略，可以在竞赛初赛前期可以带来较大提升，而很多竞赛往往依赖这些信息就可以拿到非常好的效果，剩余的则需要结合业务逻辑以及很多其他的技巧，此处我们将平时用得最多的聚合操作罗列在下方。最近刚好看到一篇文章汇总了非常多的聚合函数，就摘录在下方，供许多初入竞赛的朋友参考。聚合特征汇总01pandas自带的聚合函数mean(): Compute mean of groupssum(): Compute sum of group valuessize(): Compute group sizescount(): Compute count of groupstd(): Standard deviation of groupsvar(): Compute variance of groupssem(): Standard error of the mean of groupsfirst(): Compute first of group valueslast(): Compute last of group valuesnth() : Take nth value, or a subset if n is a listmin(): Compute min of group valuesmax(): Compute max of group values02其它重要聚合函数其它重要聚合函数&分类分别如下。def median(x):return np.median(x)def variation_coefficient(x):mean = np.mean(x)if mean != 0:return np.std(x) / meanelse:return np.nandef variance(x):return np.var(x)def skewness(x):if not isinstance(x, pd.Series):x = pd.Series(x)return pd.Series.skew(x)def kurtosis(x):if not isinstance(x, pd.Series):x = pd.Series(x)return pd.Series.kurtosis(x)def standard_deviation(x):return np.std(x)def large_standard_deviation(x):if (np.max(x)-np.min(x)) == 0:return np.nanelse:return np.std(x)/(np.max(x)-np.min(x))def variation_coefficient(x):mean = np.mean(x)if mean != 0:return np.std(x) / meanelse:return np.nandef variance_std_ratio(x):y = np.var(x)if y != 0:return y/np.sqrt(y)else:return np.nandef ratio_beyond_r_sigma(x, r):if x.size == 0:return np.nanelse:return np.sum(np.abs(x - np.mean(x)) > r * np.asarray(np.std(x))) / x.sizedef range_ratio(x):mean_median_difference = np.abs(np.mean(x) - np.median(x))max_min_difference = np.max(x) - np.min(x)if max_min_difference == 0:return np.nanelse:return mean_median_difference / max_min_differencedef has_duplicate_max(x):return np.sum(x == np.max(x)) >= 2def has_duplicate_min(x):return np.sum(x == np.min(x)) >= 2def has_duplicate(x):return x.size != np.unique(x).sizedef count_duplicate_max(x):return np.sum(x == np.max(x))def count_duplicate_min(x):return np.sum(x == np.min(x))def count_duplicate(x):return x.size - np.unique(x).sizedef sum_values(x):if len(x) == 0:return 0return np.sum(x)def log_return(list_stock_prices):return np.log(list_stock_prices).diff()def realized_volatility(series):return np.sqrt(np.sum(series**2))def realized_abs_skew(series):return np.power(np.abs(np.sum(series**3)),1/3)def realized_skew(series):return np.sign(np.sum(series**3))*np.power(np.abs(np.sum(series**3)),1/3)def realized_vol_skew(series):return np.power(np.abs(np.sum(series**6)),1/6)def realized_quarticity(series):return np.power(np.sum(series**4),1/4)def count_unique(series):return len(np.unique(series))def count(series):return series.size#drawdons functions are minedef maximum_drawdown(series):series = np.asarray(series)if len(series)<2:return 0k = series[np.argmax(np.maximum.accumulate(series) - series)]i = np.argmax(np.maximum.accumulate(series) - series)if len(series[:i])<1:return np.NaNelse:j = np.max(series[:i])return j-kdef maximum_drawup(series):series = np.asarray(series)if len(series)<2:return 0series = - seriesk = series[np.argmax(np.maximum.accumulate(series) - series)]i = np.argmax(np.maximum.accumulate(series) - series)if len(series[:i])<1:return np.NaNelse:j = np.max(series[:i])return j-kdef drawdown_duration(series):series = np.asarray(series)if len(series)<2:return 0k = np.argmax(np.maximum.accumulate(series) - series)i = np.argmax(np.maximum.accumulate(series) - series)if len(series[:i]) == 0:j=kelse:j = np.argmax(series[:i])return k-jdef drawup_duration(series):series = np.asarray(series)if len(series)<2:return 0series=-seriesk = np.argmax(np.maximum.accumulate(series) - series)i = np.argmax(np.maximum.accumulate(series) - series)if len(series[:i]) == 0:j=kelse:j = np.argmax(series[:i])return k-jdef max_over_min(series):if len(series)<2:return 0if np.min(series) == 0:return np.nanreturn np.max(series)/np.min(series)def mean_n_absolute_max(x, number_of_maxima = 1):""" Calculates the arithmetic mean of the n absolute maximum values of the time series."""assert (number_of_maxima > 0), f" number_of_maxima={number_of_maxima} which is not greater than 1"n_absolute_maximum_values = np.sort(np.absolute(x))[-number_of_maxima:]return np.mean(n_absolute_maximum_values) if len(x) > number_of_maxima else np.NaNdef count_above(x, t):if len(x)==0:return np.nanelse:return np.sum(x >= t) / len(x)def count_below(x, t):if len(x)==0:return np.nanelse:return np.sum(x <= t) / len(x)#number of valleys = number_peaks(-x, n)def number_peaks(x, n):"""Calculates the number of peaks of at least support n in the time series x. A peak of support n is defined as asubsequence of x where a value occurs, which is bigger than its n neighbours to the left and to the right."""x_reduced = x[n:-n]res = Nonefor i in range(1, n + 1):result_first = x_reduced > _roll(x, i)[n:-n]if res is None:res = result_firstelse:res &= result_firstres &= x_reduced > _roll(x, -i)[n:-n]return np.sum(res)def mean_abs_change(x):return np.mean(np.abs(np.diff(x)))def mean_change(x):x = np.asarray(x)return (x[-1] - x[0]) / (len(x) - 1) if len(x) > 1 else np.NaNdef mean_second_derivative_central(x):x = np.asarray(x)return (x[-1] - x[-2] - x[1] + x[0]) / (2 * (len(x) - 2)) if len(x) > 2 else np.NaNdef root_mean_square(x):return np.sqrt(np.mean(np.square(x))) if len(x) > 0 else np.NaNdef absolute_sum_of_changes(x):return np.sum(np.abs(np.diff(x)))def longest_strike_below_mean(x):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)return np.max(_get_length_sequences_where(x < np.mean(x))) if x.size > 0 else 0def longest_strike_above_mean(x):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)return np.max(_get_length_sequences_where(x > np.mean(x))) if x.size > 0 else 0def count_above_mean(x):m = np.mean(x)return np.where(x > m)[0].sizedef count_below_mean(x):m = np.mean(x)return np.where(x < m)[0].sizedef last_location_of_maximum(x):x = np.asarray(x)return 1.0 - np.argmax(x[::-1]) / len(x) if len(x) > 0 else np.NaNdef first_location_of_maximum(x):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)return np.argmax(x) / len(x) if len(x) > 0 else np.NaNdef last_location_of_minimum(x):x = np.asarray(x)return 1.0 - np.argmin(x[::-1]) / len(x) if len(x) > 0 else np.NaNdef first_location_of_minimum(x):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)return np.argmin(x) / len(x) if len(x) > 0 else np.NaN# Test non-consecutive non-reoccuring values ?def percentage_of_reoccurring_values_to_all_values(x):if len(x) == 0:return np.nanunique, counts = np.unique(x, return_counts=True)if counts.shape[0] == 0:return 0return np.sum(counts > 1) / float(counts.shape[0])def percentage_of_reoccurring_datapoints_to_all_datapoints(x):if len(x) == 0:return np.nanif not isinstance(x, pd.Series):x = pd.Series(x)value_counts = x.value_counts()reoccuring_values = value_counts[value_counts > 1].sum()if np.isnan(reoccuring_values):return 0return reoccuring_values / x.sizedef sum_of_reoccurring_values(x):unique, counts = np.unique(x, return_counts=True)counts[counts < 2] = 0counts[counts > 1] = 1return np.sum(counts * unique)def sum_of_reoccurring_data_points(x):unique, counts = np.unique(x, return_counts=True)counts[counts < 2] = 0return np.sum(counts * unique)def ratio_value_number_to_time_series_length(x):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)if x.size == 0:return np.nanreturn np.unique(x).size / x.sizedef abs_energy(x):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)return np.dot(x, x)def quantile(x, q):if len(x) == 0:return np.NaNreturn np.quantile(x, q)# crossing the mean ? other levels ?def number_crossing_m(x, m):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)# From https://stackoverflow.com/questions/3843017/efficiently-detect-sign-changes-in-pythonpositive = x > mreturn np.where(np.diff(positive))[0].sizedef absolute_maximum(x):return np.max(np.absolute(x)) if len(x) > 0 else np.NaNdef value_count(x, value):if not isinstance(x, (np.ndarray, pd.Series)):x = np.asarray(x)if np.isnan(value):return np.isnan(x).sum()else:return x[x == value].sizedef range_count(x, min, max):return np.sum((x >= min) & (x < max))def mean_diff(x):return np.nanmean(np.diff(x.values))base_stats = ['mean','sum','size','count','std','first','last','min','max',median,skewness,kurtosis]higher_order_stats = [abs_energy,root_mean_square,sum_values,realized_volatility,realized_abs_skew,realized_skew,realized_vol_skew,realized_quarticity]additional_quantiles = [quantile_01,quantile_025,quantile_075,quantile_09]other_min_max = [absolute_maximum,max_over_min]min_max_positions = [last_location_of_maximum,first_location_of_maximum,last_location_of_minimum,first_location_of_minimum]peaks = [number_peaks_2, mean_n_absolute_max_2, number_peaks_5, mean_n_absolute_max_5, number_peaks_10, mean_n_absolute_max_10]counts = [count_unique,count,count_above_0,count_below_0,value_count_0,count_near_0]reoccuring_values = [count_above_mean,count_below_mean,percentage_of_reoccurring_values_to_all_values,percentage_of_reoccurring_datapoints_to_all_datapoints,sum_of_reoccurring_values,sum_of_reoccurring_data_points,ratio_value_number_to_time_series_length]count_duplicate = [count_duplicate,count_duplicate_min,count_duplicate_max]variations = [mean_diff,mean_abs_change,mean_change,mean_second_derivative_central,absolute_sum_of_changes,number_crossing_0]ranges = [variance_std_ratio,ratio_beyond_01_sigma,ratio_beyond_02_sigma,ratio_beyond_03_sigma,large_standard_deviation,range_ratio]参考文献https://www.kaggle.com/code/lucasmorin/amex-feature-engineering-2-aggreg-functions