2022雪浪云赛题2总结_雪浪云-汽车全厂排产优化赛题目

记录我一次体验完整的比赛经历，通过datawhale的开源学习以及和众多大佬的比赛交流，收获颇多。

比赛官网：https://www.xuelangyun.com/#/sign-up-statistics?tab=0

比赛排名：55/263

比赛内容：阀体装配线产品合格检测（时序数据二分类、异常检测）

比赛思路如下：

读取csv数据构建特征

统计特征：最小值、最大值、标准差、方差、极差、均值、均方根；

时域特征：总能量、偏度、峭度、波形因子、峰值因子、脉冲因子、裕度因子；

频域特征：经过fft（快速傅里叶变换）后的数据的总能量、最大值、最小值、标准差、方差、极差、均值、偏度、均方根。

这些特征的详细代码参照时频域特征：http://t.csdn.cn/qP8cx

# 定义特征抽取函数
import pandas as pd
import os
import numpy as np
from scipy.linalg import norm
from tqdm import tqdm
import pickle
import json
 
def get_each_sample_feature(ori_path):
    data = {}
    feature_name_list = []
    columns_feature_name = ['sum','min','max','std','var','pk','mean','skew','kurt','rms','boxing','fengzhi','maichong','yudu','25','50','75']
    defult_parameters=[float('nan') for i in range(len(columns_feature_name))]
    columns = ['id']
 
    sample_list = os.listdir(os.path.join(ori_path,'P1000','Report_P1000_Time'))
    for sample in sample_list:
        sample_name = sample.replace('.csv','')
        data[sample_name] = []
    station_list = os.listdir(ori_path)
    for station in station_list:
        for sensor in os.listdir(os.path.join(ori_path,station)):
            feature_name_list.append(ori_path+'/%s/%s'%(station,sensor))
    for feature_name in tqdm(feature_name_list):
        for k in columns_feature_name:
            tmp_name = '_'.join(feature_name.split('/')[-2:])
            columns.append(tmp_name+'_'+k)
        for key in data.keys():
            sample_path = feature_name+'/'+key+'.csv'
            if os.path.exists(sample_path):
                sample_onesensor_pd = pd.read_csv(sample_path)
                if sample_onesensor_pd.shape[0] == 0:
                    sample_ = defult_parameters
                else:
                    sample_ = [sample_onesensor_pd.sum()[0],
                           sample_onesensor_pd.min()[0],
                           sample_onesensor_pd.max()[0],
                           sample_onesensor_pd.std()[0],
                           sample_onesensor_pd.var()[0],
                           sample_onesensor_pd.max()[0]-sample_onesensor_pd.min()[0],
                           sample_onesensor_pd.mean()[0],
                           sample_onesensor_pd.skew()[0],
                           sample_onesensor_pd.kurt()[0],
                           norm(sample_onesensor_pd,2)/np.sqrt(sample_onesensor_pd.shape[-1]),
                           norm(sample_onesensor_pd,2)/np.sqrt(sample_onesensor_pd.shape[-1])/sample_onesensor_pd.mean()[0],
                           ((sample_onesensor_pd.max()-sample_onesensor_pd.min())/norm(sample_onesensor_pd,2)/np.sqrt(sample_onesensor_pd.shape[-1]))[0],
                           ((sample_onesensor_pd.max()-sample_onesensor_pd.min())/sample_onesensor_pd.mean())[0],
                           ((sample_onesensor_pd.max()-sample_onesensor_pd.min())/(np.mean(np.sqrt(abs(sample_onesensor_pd)))**2))[0],
                           np.percentile(sample_onesensor_pd,(25)),
                           np.percentile(sample_onesensor_pd,(50)),
                           np.percentile(sample_onesensor_pd,(75))]
                           
            else:
                sample_ = defult_parameters
            for i in sample_:
                data[key].append(i)
                    
    return data,columns

以上代码根据datawhale开源代码思路进行了自己的特征扩展，datawhale的思路和开源代码地址：

https://datawhaler.feishu.cn/docx/T3Stdh8nFo4FSwxpTX8cFI0rnnd

接下来提取NG，OK样本中的特征数据，并分别保存为OK.csv ，NG.csv到本地方便使用。

OK,columns = get_each_sample_feature('train/OK')
pd.DataFrame(list(OK.values()),columns=columns[1:]).to_csv('OK.csv',index = False)
 
NG,columns = get_each_sample_feature('train/NG')
pd.DataFrame(list(NG.values()),columns=columns[1:]).to_csv('NG.csv',index = False)

通过以上代码可以把一个一个检测台文件夹里的样本csv处理成一个特征csv。

读取特征数据处理后进行特征筛选

读取特征数据

#读取OK特征数据，删除行列全为空的数据，赋予对应的label为0
OK = pd.read_csv('OK.csv')
OK = OK.dropna(axis = 0,how = 'all')
OK = OK.dropna(axis = 1,how = 'all')
OK['label'] = 0
OK
 
NG = pd.read_csv('NG.csv')
NG = NG.dropna(axis = 0,how = 'all')
NG = NG.dropna(axis = 1,how = 'all')
NG['label'] = 1
NG
#拼接ok和ng特征数据
data = pd.concat([OK,NG])
data
#查看缺失值数量
print(data.isna().sum())
print(data.isna().sum().sum())

由于我做的特征有的存在较多的缺失值，所以需要删掉缺失值过多的特征，如缺失50%就可以认为缺失过多。

#查看缺失值在该列的占比
df.isna().mean()
 
#定义查找缺失值超过比例的列的函数
#这里给个参数ratio用于控制缺失值比例，比如给个0.01，意思就是缺失值超过99%这个特征就剔除。
def filter_col_by_nan(df, ratio=0.05):
    cols = []
    for col in df.columns:
        if df[col].isna().mean() >= (1-ratio):
            cols.append(col)
    return cols
 
#比如我想把缺失值比例超过0.02的列删掉
nanfeature = filter_col_by_nan(data, ratio=0.98)
nanfeature
 
data = data.drop(labels = nanfeature,axis = 1)
data

缺失值补全

常见的是用均值，中位数以及众数填充缺失值（甚至可以直接用一个常数填充）

但是针对这一场景：

对于NG样本而言，过检测的传感器发现数据异常，就会判定阀体异常，那么其他的数据就不会去测了，所以有的阀体是有缺失值的，即没有全部的有效特征#针对这种情况，这里选择从OK样本中随机选一个值进行填充，即我们认为没有测量的的特征值默认是能通过阀体检测台的。

#定义缺失值填充函数
def fill_with_random(df, column):
    df[column] = df[column].apply(lambda x: np.random.choice(OK[column].dropna().values) if np.isnan(x) else x)
    return df
 
for i in tqdm(data.columns):
    data=fill_with_random(data,i)
 
#可以再次检查是否还有缺失值
print(data.isna().sum())

特征筛选

方差过滤+皮尔逊系数（只能筛选到线性特征）

train_data=data.drop(labels='label', axis=1)
train_data
 
from sklearn.feature_selection import VarianceThreshold
var = VarianceThreshold(threshold = np.median(train_data.var().values))
# var = VarianceThreshold(threshold = 0)
var.fit_transform(train_data)
is_select = var.get_support()
var_feature = train_data.iloc[:,is_select]
var_feature
 
x_array = np.array(var_feature)
x_array
y_array = np.array(data['label'])
y_array
 
from scipy.stats import pearsonr
def multivariate_pearsonr(X, Y): 
    scores, pvalues = [], []
    for column in range(X.shape[1]):
        cur_score, cur_p = pearsonr(X[:,column], Y)
        scores.append(abs(cur_score))
        pvalues.append(cur_p) 
    return (np.array(scores), np.array(pvalues))
 
from sklearn.feature_selection import SelectKBest
m_pearsonr = SelectKBest(score_func=multivariate_pearsonr, k=50) 
X_pearson = m_pearsonr.fit_transform(x_array, y_array) 
print(m_pearsonr.scores_)
 
pearsonr = pd.DataFrame(m_pearsonr.scores_, columns = ["pearsonr"], index=var_feature.columns)
pearsonr = pearsonr.reset_index()
pearsonr.sort_values('pearsonr',ascending=0)

随机森林特征重要性（负样本少，重要性可能不准）

x = data.iloc[:,:-1]
y = data['label']
 
from sklearn.feature_selection import SelectFromModel
from sklearn.ensemble import RandomForestClassifier as rfc
from sklearn.model_selection import cross_val_score
import numpy as np
import matplotlib.pyplot as plt 
rfc_ = rfc(n_estimators=88,random_state=2023)
rfc_.fit(x,y).feature_importances_
 
max_fi = max(rfc_.fit(x,y).feature_importances_)
max_fi
 
threshold = np.linspace(0,max_fi,20)
threshold
 
scores = []
for i in threshold:
    X_embedded = SelectFromModel(rfc_,threshold=i).fit_transform(x,y)
    score = cross_val_score(rfc_,X_embedded,y,cv=5).mean()
    scores.append(score)
plt.plot(threshold,scores)
plt.show()
print("模型最优时的分数：",max(scores))
print("模型最优时的阈值：",scores.index(max(scores)))
 
a = pd.DataFrame({'feature':x.columns.to_list()})
a
 
b = pd.DataFrame({'feature_importances_':rfc_.fit(x,y).feature_importances_})
b
 
c = pd.concat([a,b],axis=1)
c = c.sort_values(by='feature_importances_',ascending=False)
#c = c[:60]
c

lightgbm特征重要性

from sklearn.model_selection import StratifiedKFold
import lightgbm
 
def select_by_lgb(train_data,train_label,random_state=2023,n_splits=5,metric='auc',num_round=10000,early_stopping_rounds=100):
    kfold = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)  
    feature_importances = pd.DataFrame()
    feature_importances['feature'] = train_data.columns
    fold=0
    for train_idx, val_idx in kfold.split(train_data,target):
        random_state+=1
        train_x = train_data.iloc[train_idx]
        train_y = train_label.iloc[train_idx]
        test_x = train_data.iloc[val_idx]
        test_y = train_label.iloc[val_idx]
        clf=lightgbm
        train_matrix=clf.Dataset(train_x,label=train_y)
        test_matrix=clf.Dataset(test_x,label=test_y)
        params={
                'boosting_type': 'gbdt',  
                'objective': 'binary',
                'learning_rate': 0.1,
                'metric': metric,
                'seed': 2020,
                'nthread':-1 ,
                'verbose': -1}
        model=clf.train(params,train_matrix,num_round,valid_sets=test_matrix,early_stopping_rounds=early_stopping_rounds)
        feature_importances['fold_{}'.format(fold + 1)] = model.feature_importance()
        fold+=1
    feature_importances['averge']=feature_importances[['fold_{}'.format(i) for i in range(1,n_splits+1)]].mean(axis=1)
    return feature_importances
 
train_data = data.drop(labels='label', axis=1)
train_data
target = data['label']
target
 
feature_importances=select_by_lgb(train_data,target)
feature_importances['averge']=feature_importances[['fold_{}'.format(i) for i in range(1,6)]].mean(axis=1)
feature_importances=feature_importances.sort_values(by='averge',ascending=False)
feature_importances

互信息特征筛选（线性和非线性特征都能筛选到）

X = data.iloc[:,:-1]
y = data['label']
 
from sklearn.feature_selection import mutual_info_classif as MIC 
result = MIC(X,y)
k = result.shape[0] - sum(result <= 0)
 
a = pd.DataFrame({'feature':X.columns.to_list()})
a
b = pd.DataFrame({'feature_importances_':result})
b
c = pd.concat([a,b],axis=1)
c = c.sort_values(by='feature_importances_',ascending=False)
c

特征筛选方法简要总结：https://cloud.tencent.com/developer/article/1848218#

由于比赛要求只能使用50个特征，所以还需要写一段代码判断是否超过50个特征

feature_list = []
for feature in list(c['feature'][:100]):
    if 'sum' in feature:
        feature = feature.replace('_sum','')
    if 'min' in feature:
        feature = feature.replace('_min','')
    if 'max' in feature:
        feature = feature.replace('_max','')
    if 'std' in feature:
        feature = feature.replace('_std','')
    if 'var' in feature:
        feature = feature.replace('_var','')
    if 'pk' in feature:
        feature = feature.replace('_pk','')
    if 'mean' in feature:
        feature = feature.replace('_mean','')
    if 'skew' in feature:
        feature = feature.replace('_skew','')
    if 'kurt' in feature:
        feature = feature.replace('_kurt','')
    if 'rms' in feature:
        feature = feature.replace('_rms','')
    if 'boxing' in feature:
        feature = feature.replace('_boxing','')
    if 'fengzhi' in feature:
        feature = feature.replace('_fengzhi','')
    if 'maichong' in feature:
        feature = feature.replace('_maichong','')
    if 'yudu' in feature:
        feature = feature.replace('_yudu','')
    if '25' in feature:
        feature = feature.replace('_25','')
    if '50' in feature:
        feature = feature.replace('_50','')
    if '75' in feature:
        feature = feature.replace('_75','')
    else:
        print('全部替换完成')
    feature_list.append(feature)
 
new_feature_list = sorted(set(feature_list),key = feature_list.index)
new_feature_list
 
len(new_feature_list)
 
allfeature = list(c['feature'][:100])
allfeature

模型建立

模型可以选择常规的机器学习模型，如随机森林、lightgbm、xgboost、catboost等等，但是由于我做的特征出现过拟合验证的问题，在这些模型上的效果不太理想，于是我选择了gbdt+lr的模型进行训练预测，但是效果还是不咋样，于是调参等一系列操作都没有进行。这里就不放代码了，对gbdt+lr模型感兴趣的点击这个链接看看：http://t.csdn.cn/t6ZKI代码在文末的github里。

不过进行cvk折交叉验证值得记录一下，因为我只用过cvk折交叉验证来在随机森林上进行调参。因为样本不均衡，这里没有选择使用train_test_split或者KFold，而是使用了根据样本标签比例来划分数据集的StratifiedKFold。

from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score, recall_score
import xgboost as xgb
 
kf = StratifiedKFold(n_splits=5, shuffle=True)
f1_scores1 = []
recall_scores1 = []
counter = 0
for train_index, test_index in kf.split(train_data,label):
    samples = np.array(train_data)
    labels = np.array(label)
    X_train, X_test = samples[train_index], samples[test_index]
    y_train, y_test = labels[train_index], labels[test_index]
    ng_nums = y_test.sum()
    print('Test ok nums: {}'.format(len(y_test) - ng_nums))
    print('Test NG nums: {}'.format(ng_nums))
 
 
    model1 = xgb.XGBClassifier(max_depth=8, learning_rate=0.05,n_estimators=100, objective='binary:logistic',
                                eval_metric=['logloss', 'auc', 'error'],
                                use_label_encoder=False)
 
    model1.fit(X_train,y_train)
    test_pred1 = model1.predict(X_test)
    f1_scores1.append(f1_score(y_test,test_pred1))
    recall_scores1.append(recall_score(y_test,test_pred1,average='binary'))
print(f1_scores1)
print(recall_scores1)

模型融合代码：

from sklearn.ensemble import VotingClassifier
voting_clf = VotingClassifier(estimators=[('rfc',rfc),('cbc',cbc),('xgb',xgb)],
                             voting='soft')
for clf in (rfc, cbc, xgb, voting_clf):
    clf.fit(x_train, y_train)
    y_pred = clf.predict(x_test)

docker常用操作

#查看linux空间
df -h 
#安装依赖
pip install -r requirements.txt
#创建docker镜像
chmod +x build.sh
sudo ./build.sh
#挂载数据到镜像
sudo docker run -it -v /home/wyp/data:/code/components/data components-demo:1.0.0 /bin/bash
sudo docker run -it -v /home/wyp/train/NG:/code/components/data components-demo:1.0.0 /bin/bash
#执行代码
chmod +x run.sh
./run.sh
 

总结

首先，由于对业务背景认识不够准确，对异常样本ng也没有理解到位，在构建特征时的操作是无脑堆特征，有的特征是不必要的，有的特征是冗余的。我也没有加以鉴别就直接筛选使用。在赛后看到大佬们做了eda并分析得到有的特征是一样的等相关有用信息进行构造特征，由此我也认识到特征工程的重要性，好的特征是成功的基础。同时我也学会新的缺失值处理和筛选方法，也不再拘泥于使用一种模型，尝试了多个模型，进行了cvk折交叉验证，也尝试使用投票法融合模型，总体学到了不少知识，收获很大。

其次，打比赛还是不能看榜，人比人属实是气死人。自己做自己的就好，有收获，有成果提交上去试试就行。为了打比赛把身体搞垮了也得不偿失。比赛确实是玄学，后期在机缘巧合之下从50+到75分，没有点运气加持那是不可能的。

最后，感谢飞哥一个比赛全程的交流学习，感谢群里的大佬的答疑，感谢军哥的指点，感谢队友凯凯的合作。

然后2023ccf dcic欺诈风险识别（风控，二分类）和第六届全国工业互联网数据创新应用大赛——产品量测值预测赛题（回归）再战！！！