功能包括：
- 异常值处理
- 空置处理
- 训练集和测试集划分
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import gc
from tqdm import tqdm
import time
from contextlib import contextmanager
from sklearn.preprocessing import StandardScaler
@contextmanager
def timer(name):
t0 = time.time()
yield
print(f'[{name}] done in {time.time() - t0:.0f} s')
def preprocess(data: pd.DataFrame):
""" 对数据进行预处理
"""
def fill_outliers(col: pd.Series):
""" Remove outliers of each col
"""
mean = col.mean()
std = col.std()
upper = mean + 3 * std
lower = mean - 3 * std
col[col > upper] = np.floor(upper)
col[col < lower] = np.floor(lower)
return col.values
# 处理离散值 & 填充空值(使用均值填充)
columns = data.columns
for col_name in tqdm(columns):
data[col_name] = data[col_name].fillna(data[col_name].mean())
#标准化
return data
with timer("split train and test dataset!!!"):
# 读取训练集和测试集
X_train = pd.read_csv('./dataset/atec_anti_fraud_train.csv', encoding='utf-8',
low_memory=False, parse_dates=['date'],index_col='id')
X_test = pd.read_csv('./dataset/atec_anti_fraud_test_b.csv', encoding='utf-8',
low_memory=False, parse_dates=['date'],index_col='id')
col_train_num, col_test_num = X_train.columns, X_test.columns
X_train, X_test = X_train[col_train_num], X_test[col_test_num]
X_train_label,X_train_date=X_train.pop('label'),X_train.pop('date')
X_test_date=X_test.pop('date')
print(X_train.shape, X_test.shape)
print("Start filter features!!!")
# 筛选缺失率小于0.6的特征
col_train, col_test = [], []
for item in X_train.columns:
tmp = np.sum(X_train[item].isnull()) / len(X_train)
if tmp < 1:
col_train.append(item)
for item in X_test.columns:
tmp = np.sum(X_test[item].isnull()) / len(X_test)
if tmp <1:
col_test.append(item)
# 选择训练集和测试集的交集
col = [item for item in col_train if item in col_test]
print('len(col):', len(col))
X_train, X_test = X_train[col], X_test[col]
X_train, X_test = preprocess(X_train), preprocess(X_test)
X_train, X_test = pd.DataFrame(X_train),pd.DataFrame(X_test)
X_train=pd.concat([X_train_label,X_train_date,X_train],axis=1)
X_test=pd.concat([X_test_date,X_test],axis=1)
X_train_col,X_test_col=col.copy(),col.copy()
X_train_col.insert(0,'label')
X_train_col.insert(1,'date')
X_test_col.insert(0,'date')
print(X_train.shape, X_test.shape)
print("Start writing")
X_train.to_csv("./dataset/x_train.csv", encoding='utf-8',header=X_train_col)
X_test.to_csv("./dataset/x_test_b.csv", encoding='utf-8',header=X_test_col)
del X_train,X_test
gc.collect()
工具函数
import gc
import os
import sys
import time
import pickle
import datetime
import numpy as np
import pandas as pd
from tqdm import tqdm
import multiprocessing
from functools import partial
from dateutil.parser import parse
from lightgbm import LGBMClassifier
from collections import defaultdict
from datetime import date, timedelta
from contextlib import contextmanager
from joblib import dump, load, Parallel, delayed
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import NMF, PCA, TruncatedSVD
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation as LDA
# 求解rmse的均值和标准差
def get_ave_std(c1,c2,f1,f2):
'''
:param c1: 提交的常数1
:param c2: 提交的常数2
:param f1: 得分1
:param f2: 得分2
:return: 均值和标准差
'''
f1 = f1**2; f2 = f2**2;
a = 2; b = 2*(c1+c2); c = c1**2+c2**2-(f1-f2);
ave = (f1 - f2 + c2 ** 2 - c1 ** 2) / 2 / (c2 - c1)
std = (f1 - (c1 - ave) ** 2) ** 0.5
return ave,std
# 求解rmse的均值
def get_sub_ave_std(c1,c2,f1,f2,n1,n2):
'''
:param c1: 提交1的常数
:param c2: 提交2有差异的部分的常数
:param f1: 提交1的分数
:param f2: 提交2的分数
:param n1: 提交总个数
:param n2: 提交2有差异部分的个数
:return: 提交2有差异部分的均值
'''
result = ((c1+c2)-((f1**2-f2**2)*n1/n2/(c1-c2)))/2
return result
# 抽样函数
def make_sample(n,n_sub=2,seed=None):
import random
if seed is not None:
random.seed(seed)
if type(n) is int:
l = list(range(n))
s = int(n / n_sub)
else:
l = list(n)
s = int(len(n) / n_sub)
random.shuffle(l)
result = []
for i in range(n_sub):
if i == n_sub:
result.append(l[i*s:])
else:
result.append(l[i*s: (i+1)*s])
return result
# 统计list的value_counts
def value_counts(l):
s = set(l)
d = dict([(x,0) for x in s])
for i in l:
d[i] += 1
result = pd.Series(d)
result.sort_values(ascending=False,inplace=True)
return result
# 统计转化率
def convert(data,stat,key):
key = key if type(key) == list else [key]
stat_temp = stat[key + ['label']].copy()
rate = stat_temp.groupby(key,as_index=False)['label'].agg({'sum':'sum', 'count':'count'})
rate['_'.join(key)+'_convert'] = ((rate['sum']+4)/(rate['count']+46))
data = data.merge(rate, on=key, how='left')
return data['_'.join(key)+'_convert']
# One-hot encoding for categorical columns with get_dummies
def one_hot_encoder(df, nan_as_category=True, min_count=100,inplace=True):
original_columns = list(df.columns)
categorical_columns = [col for col in df.columns if df[col].dtype == 'object']
result = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category)
new_columns = [c for c in result.columns if c not in original_columns]
cat_columns = [c for c in original_columns if c not in result.columns]
if not inplace:
for c in cat_columns:
result[c] = df[c]
for c in new_columns:
if (result[c].sum()<100) or ((result.shape[0]-result[c].sum())<100):
del result[c]
new_columns.remove(c)
return result, new_columns
# 分类特征转化率
def analyse(data,name,label='label'):
result = data.groupby(name)[label].agg({'count':'count',
'sum':'sum'})
result['rate'] = result['sum']/result['count']
return result
# 连续特征转化率，等距分隔
def analyse2(data,name='id',label='label', factor=10):
grouping = pd.cut(data[name],factor)
rate = data.groupby(grouping)[label].agg({'sum':'sum',
'count':'count'})
rate['rate'] = rate['sum']/rate['count']
return rate
# 连续特征转化率，等数分隔
def analyse3(data,name='id',label='label', factor=10):
grouping = pd.qcut(data[name],factor)
rate = data.groupby(grouping)[label].agg({'sum':'sum',
'count':'count'})
rate['rate'] = rate['sum']/rate['count']
return rate
# 分组标准化
def grp_standard(data,key,names,replace=False):
for name in names:
new_name = name + '_' + key + '_' + 'standardize' if replace else name
mean_std = data.groupby(key, as_index=False)[name].agg({'mean': 'mean',
'std': 'std'})
data = data.merge(mean_std, on=key, how='left')
data[new_name] = ((data[name]-data['mean'])/data['std']).fillna(0).astype(np.float32)
data[new_name] = data[new_name].replace(-np.inf, 0).fillna(0)
data.drop(['mean','std'],axis=1,inplace=True)
return data
# 分组归一化
def grp_normalize(data,key,names,start=0,replace=False):
for name in names:
new_name = name + '_' + key + '_' + 'normalize' if replace else name
max_min = data.groupby(key,as_index=False)[name].agg({'max':'max',
'min':'min'})
data = data.merge(max_min, on=key, how='left')
data[new_name] = (data[name]-data['min'])/(data['max']-data['min'])
data[new_name] = data[new_name].replace(-np.inf, start).fillna(start).astype(np.float32)
data.drop(['max','min'],axis=1,inplace=True)
return data
# 分组排序
def grp_rank(data,key,names,ascending=True):
for name in names:
data.sort_values([key, name], inplace=True, ascending=ascending)
data['rank'] = range(data.shape[0])
min_rank = data.groupby(key, as_index=False)['rank'].agg({'min_rank': 'min'})
data = pd.merge(data, min_rank, on=key, how='left')
data['rank'] = data['rank'] - data['min_rank']
data[names] = data['rank']
data.drop(['rank'],axis=1,inplace=True)
return data
# 合并节约内存
def concat(L):
result = None
for l in L:
if result is None:
result = l
else:
result[l.columns.tolist()] = l
return result
# 分组排序函数
def group_rank(data, key, values, ascending=True):
if type(key)==list:
data_temp = data[key + [values]].copy()
data_temp.sort_values(key + [values], inplace=True, ascending=ascending)
data_temp['rank'] = range(data_temp.shape[0])
min_rank = data_temp.groupby(key,as_index=False)['rank'].agg({'min_rank':'min'})
index = data_temp.index
data_temp = data_temp.merge(min_rank,on=key,how='left')
data_temp.index = index
else:
data_temp = data[[key,values]].copy()
data_temp.sort_values(key + [values], inplace=True, ascending=ascending)
data_temp['rank'] = range(data_temp.shape[0])
data_temp['min_rank'] = data_temp[key].map(data_temp.groupby(key)['rank'].min())
data_temp['rank'] = data_temp['rank'] - data_temp['min_rank']
return data_temp['rank']
def nunique(x):
return len(set(x))
# 前后时间差的函数：
def group_diff_time(data,key,value,n):
data_temp = data[key+[value]].copy()
shift_value = data_temp.groupby(key)[value].shift(n)
data_temp['shift_value'] = data_temp[value] - shift_value
return data_temp['shift_value']
# smape
def smape(y_true,y_pred):
y_true = np.array(y_true)
y_pred = np.array(y_pred)
y_diff = np.abs(y_true-y_pred)
y_sum = y_true+y_pred
return np.mean(y_diff/y_sum)*2
# groupby 直接拼接
def groupby(data,stat,key,value,func):
key = key if type(key)==list else [key]
data_temp = data[key].copy()
feat = stat.groupby(key,as_index=False)[value].agg({'feat':func})
data_temp = data_temp.merge(feat,on=key,how='left')
return data_temp['feat']
# 计算关系紧密程度指数
def tfidf2(df,key1,key2):
key = key1 + key2
tfidf2 = '_'.join(key) + '_tfidf2'
df1 = df.groupby(key,as_index=False)[key[0]].agg({'key_count': 'size'})
df2 = df1.groupby(key1,as_index=False)['count'].agg({'key1_count': 'sum'})
df3 = df1.groupby(key2, as_index=False)['count'].agg({'key2_count': 'sum'})
df1 = df1.merge(df2,on=key1,how='left').merge(df3,on=key2,how='left')
df1[tfidf2] = df1['key_count'] / df['key2_count'] / df['key1_count']
# 相差的日期数
def diff_of_days(day1, day2):
days = (parse(day1[:10]) - parse(day2[:10])).days
return days
# 相差的分钟数
def diff_of_minutes(time1,time2):
minutes = (parse(time1) - parse(time2)).total_seconds()//60
return abs(minutes)
# 相差的小时数
def diff_of_hours(time1,time2):
hours = (parse(time1) - parse(time2)).total_seconds()//3600
return abs(hours)
# 日期的加减
def date_add_days(start_date, days):
end_date = parse(start_date[:10]) + timedelta(days=days)
end_date = end_date.strftime('%Y-%m-%d')
return end_date
# 日期的加减
def date_add_hours(start_date, hours):
end_date = parse(start_date) + timedelta(hours=hours)
end_date = end_date.strftime('%Y-%m-%d %H:%M:%S')
return end_date
# 获取某个类型里面第n次的值
def get_last_values(data, stat, key, sort_value, value, shift, sort=None):
key = key if type(key)==list else [key]
if sort == 'ascending':
stat_temp = stat.sort_values(sort_value, ascending=True)
elif sort == 'descending':
stat_temp = stat.sort_values(sort_value, ascending=False)
else:
stat_temp = stat.copy()
stat_temp['value'] = stat_temp.groupby(key)[value].shift(shift)
stat_temp.drop_duplicates(key,keep='last',inplace=True)
data_temp = data[key].copy()
data_temp = data_temp.merge(stat_temp,on=key,how='left')
return data_temp['value']
# 获取某个类型里面第n次的值
def get_first_values(data, stat, key, sort_value, value, shift, sort=None):
key = key if type(key)==list else [key]
if sort == 'ascending':
stat_temp = stat.sort_values(sort_value, ascending=True)
elif sort == 'descending':
stat_temp = stat.sort_values(sort_value, ascending=False)
else:
stat_temp = stat.copy()
stat_temp['value'] = stat_temp.groupby(key)[value].shift(-shift)
stat_temp.drop_duplicates(key,keep='first',inplace=True)
data_temp = data[key].copy()
data_temp = data_temp.merge(stat_temp,on=key,how='left')
return data_temp['value']
# 压缩数据
def compress(data):
size = sys.getsizeof(data)/2**20
def intcp(series):
ma = max(series)
mi = min(series)
if (ma<128) & (mi>=-128):
return 'int8'
elif (ma<32768) & (mi>=-32768):
return 'int16'
elif (ma<2147483648) & (mi>=-2147483648):
return 'int32'
else:
return None
def floatcp(series):
ma = max(series)
mi = min(series)
if (ma<32770) & (mi>-32770):
return 'float16'
elif (ma<2147483600) & (mi>-2147483600):
return 'float32'
else:
return None
for c in data.columns:
ctype = None
dtypes = data[c].dtypes
if dtypes == np.int64:
ctype = intcp(data[c])
if dtypes == np.int32:
ctype = intcp(data[c])
if dtypes == np.int16:
ctype = intcp(data[c])
if dtypes == np.float64:
ctype = floatcp(data[c])
if dtypes == np.float32:
ctype = floatcp(data[c])
if ctype is None:
continue
try:
data[c] = data[c].astype(ctype)
print('{} convet to {}, done! {}'.format(dtypes,ctype,c))
except:
print('特征{}的类型为：{}，转化出线问题！！！'.format(c,dtypes))
print('原始数据大小为： {}M'.format(round(size, 2)))
print('新数据大小为： {}M'.format(round(sys.getsizeof(data) / 2 ** 20,2)))
return data
def trend(y):
try:
x = np.arange(0, len(y)).reshape(-1, 1)
lr = LinearRegression()
lr.fit(x, y)
trend = lr.coef_[0]
except:
trend = np.nan
return trend
@contextmanager
def timer(title):
t0 = time.time()
yield
print("{} - done in {:.0f}s".format(title, time.time() - t0))
def jiangwei(stat,data, id, feature):
print('lda ...')
mapping = {}
for sample in stat[[id, feature]].values:
mapping.setdefault(sample[0], []).append(str(sample[1]))
ids = list(mapping.keys())
sentences = [' '.join(mapping[cate_]) for cate_ in ids]
stat_sentences_matrix = CountVectorizer(token_pattern='(?u)\\b\\w+\\b', min_df=2).fit_transform(sentences)
mapping = {}
for sample in data[[id, feature]].values:
mapping.setdefault(sample[0], []).append(str(sample[1]))
ids = list(mapping.keys())
sentences = [' '.join(mapping[cate_]) for cate_ in ids]
data_sentences_matrix = CountVectorizer(token_pattern='(?u)\\b\\w+\\b', min_df=2).fit_transform(sentences)
lda = LDA(n_components=5,
learning_method='online',
batch_size=1000,
n_jobs=40,
random_state=520)
lda.fit(stat_sentences_matrix)
lda_matrix = lda.transform(data_sentences_matrix)
lda_matrix = pd.DataFrame(lda_matrix,columns=['lda_{}_{}'.format(feature, i) for i in range(5)]).astype('float16')
nmf = NMF(n_components=5,
random_state=520,
beta_loss='kullback-leibler',
solver='mu',
max_iter=1000,
alpha=.1,
l1_ratio=.5)
nmf.fit(stat_sentences_matrix)
nmf_matrix = nmf.transform(stat_sentences_matrix)
nmf_matrix = pd.DataFrame(nmf_matrix,columns=['nmf_{}_{}'.format(feature, i) for i in range(5)]).astype('float16')
pca = TruncatedSVD(5)
pca.fit(stat_sentences_matrix)
pca_matrix = pca.transform(stat_sentences_matrix)
pca_matrix = pd.DataFrame(pca_matrix,
columns=["%s_%s_svd_action" % ('user_sku', i) for i in range(5)]).astype('float32')
matrix = concat([lda_matrix,nmf_matrix,pca_matrix])
matrix[id] = ids
return matrix