AI比赛|百度AI比赛-点击反欺诈预测-XGBoost十折子模型融合大数据|算法|python

1：报名地址 https://aistudio.baidu.com/aistudio/competition/detail/52

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2：排名分数
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3：证书登场

4：模型源码废话不多说，直接上源码

import pandas as pd import warnings warnings.filterwarnings('ignore')# 数据加载 train = pd.read_csv('./train.csv') test = pd.read_csv('./test1.csv') test = test.iloc[:, 1:] train = train.iloc[:, 1:] train# ['os', 'osv', 'lan', 'sid’] features = train.columns.tolist() features.remove('label') print(features)for feature in features: print(feature, train[feature].nunique())# 对osv进行数据清洗 def osv_trans(x): x = str(x).replace('Android_', '').replace('Android ', '').replace('W', '') if str(x).find('.')>0: temp_index1 = x.find('.') if x.find(' ')>0: temp_index2 = x.find(' ') else: temp_index2 = len(x)if x.find('-')>0: temp_index2 = x.find('-')result = x[0:temp_index1] + '.' + x[temp_index1+1:temp_index2].replace('.', '') try: return float(result) except: print(x+'#########') return 0 try: return float(x) except: print(x+'#########') return 0 #train['osv'] => LabelEncoder ? # 采用众数，进行缺失值的填充 train['osv'].fillna('8.1.0', inplace=True) # 数据清洗 train['osv'] = train['osv'].apply(osv_trans)# 采用众数，进行缺失值的填充 test['osv'].fillna('8.1.0', inplace=True) # 数据清洗 test['osv'] = test['osv'].apply(osv_trans)train['lan'].value_counts() #lan_map = {'zh-CN': 1, } train['lan'].value_counts().index lan_map = {'zh-CN': 1, 'zh_CN':2, 'Zh-CN': 3, 'zh-cn': 4, 'zh_CN_#Hans':5, 'zh': 6, 'ZH': 7, 'cn':8, 'CN':9, 'zh-HK': 10, 'tw': 11, 'TW': 12, 'zh-TW': 13,'zh-MO':14, 'en':15, 'en-GB': 16, 'en-US': 17, 'ko': 18, 'ja': 19, 'it': 20, 'mi':21} train['lan'] = train['lan'].map(lan_map) test['lan'] = test['lan'].map(lan_map) test['lan'].value_counts()# 对于有缺失的lan 设置为22 train['lan'].fillna(22, inplace=True) test['lan'].fillna(22, inplace=True) remove_list = ['os', 'lan', 'sid'] col = features for i in remove_list: col.remove(i) colfrom datetime import datetime# lambda 是一句话函数，匿名函数 train['timestamp'] = train['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000)) #1559892728241.7212 #1559871800477.1477 #1625493942.538375 #import time #time.time() #test['timestamp'] = test['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000)) #test['timestamp'] test['timestamp'] = test['timestamp'].apply(lambda x: datetime.fromtimestamp(x/1000)) test['timestamp']def version_trans(x): if x=='V3': return 3 if x=='v1': return 1 if x=='P_Final_6': return 6 if x=='V6': return 6 if x=='GA3': return 3 if x=='GA2': return 2 if x=='V2': return 2 if x=='50': return 5 return int(x) train['version'] = train['version'].apply(version_trans) test['version'] = test['version'].apply(version_trans) train['version'] = train['version'].astype('int') test['version'] = test['version'].astype('int') # 特征筛选 features = train[col] # 构造fea_hash_len特征 features['fea_hash_len'] = features['fea_hash'].map(lambda x: len(str(x))) features['fea1_hash_len'] = features['fea1_hash'].map(lambda x: len(str(x)))# Thinking：为什么将很大的，很长的fea_hash化为0？ # 如果fea_hash很长，都归为0，否则为自己的本身 features['fea_hash'] = features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x)) features['fea1_hash'] = features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x)) featurestest_features = test[col] # 构造fea_hash_len特征 test_features['fea_hash_len'] = test_features['fea_hash'].map(lambda x: len(str(x))) test_features['fea1_hash_len'] = test_features['fea1_hash'].map(lambda x: len(str(x)))# Thinking：为什么将很大的，很长的fea_hash化为0？ # 如果fea_hash很长，都归为0，否则为自己的本身 test_features['fea_hash'] = test_features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x)) test_features['fea1_hash'] = test_features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x)) test_featurestemp = pd.DatetimeIndex(features['timestamp']) features['year'] = temp.year features['month'] = temp.month features['day'] = temp.day features['week_day'] = temp.weekday #星期几 features['hour'] = temp.hour features['minute'] = temp.minute# 求时间的diff start_time = features['timestamp'].min() features['time_diff'] = features['timestamp'] - start_time features['time_diff'] = features['time_diff'].dt.days + features['time_diff'].dt.seconds/3600/24 features[['timestamp', 'year', 'month', 'day', 'week_day', 'hour', 'minute', 'time_diff']]# 创建时间戳索引 temp = pd.DatetimeIndex(test_features['timestamp']) test_features['year'] = temp.year test_features['month'] = temp.month test_features['day'] = temp.day test_features['week_day'] = temp.weekday #星期几 test_features['hour'] = temp.hour test_features['minute'] = temp.minute# 求时间的diff #start_time = features['timestamp'].min() test_features['time_diff'] = test_features['timestamp'] - start_time test_features['time_diff'] = test_features['time_diff'].dt.days + test_features['time_diff'].dt.seconds/3600/24 #test_features[['timestamp', 'year', 'month', 'day', 'week_day', 'hour', 'minute', 'time_diff']] test_features['time_diff']features['dev_height'].value_counts() features['dev_width'].value_counts() # 构造面积特征 features['dev_area'] = features['dev_height'] * features['dev_width'] test_features['dev_area'] = test_features['dev_height'] * test_features['dev_width']# 构造相除特征 features['dev_rato'] = features['dev_height'] / features['dev_width'] test_features['dev_rato'] = test_features['dev_height'] / test_features['dev_width']#features['ntt'].value_counts() features['carrier'].value_counts() features['package'].value_counts() # version - osv APP版本与操作系统版本差 features['osv'].value_counts() features['version_osv'] = features['osv'] - features['version'] test_features['version_osv'] = test_features['osv'] - test_features['version']#定义10折子模型 from sklearn.model_selection import StratifiedKFold from sklearn.metrics import accuracy_score def xgb_model(clf,train_x,train_y,test): sk=StratifiedKFold(n_splits=10,random_state=2021)prob=[] mean_acc=0 for k,(train_index,val_index) in enumerate(sk.split(train_x,train_y)): train_x_real=train_x.iloc[train_index] train_y_real=train_y.iloc[train_index] val_x=train_x.iloc[val_index] val_y=train_y.iloc[val_index] #模型训练 clf=clf.fit(train_x_real,train_y_real)val_y_pred=clf.predict(val_x) acc_val=accuracy_score(val_y,val_y_pred) print('第{}个子模型 accuracy{}'.format(k+1,acc_val)) mean_acc+=mean_acc/10test_y_pred=clf.predict_proba(test) prob.append(test_y_pred) print(mean_acc)mean_prob=sum(prob)/10 return mean_probimport xgboost as xgb model_xgb2 = xgb.XGBClassifier( max_depth=15, learning_rate=0.005, n_estimators=5300, objective='binary:logistic', tree_method='auto', subsample=0.7, colsample_bytree=0.7, min_child_samples=3, eval_metric='auc', reg_lambda=0.5 ) result_xgb2=xgb_model(model_xgb2,features.drop(['timestamp'], axis=1),train['label'],test_features.drop(['timestamp'], axis=1))result_xgb3=[x[1] for x in result_xgb2] result_xgb3=[1 if x>=0.5 else 0 for x in result_xgb3] print(result_xgb3)res = pd.DataFrame(test['sid']) res['label'] = result_xgb3 res.to_csv('./baseline_.89.116.csv', index=False) res

5：提分要点1：数据清洗
去掉一些不合理的数据，例如对字段osv的清洗
2：缺失值补齐
3：特征筛选与构造
fea_hash，
fea1_hash
dev_area：设备面积大小
version_osv：APP版本与操作系统版本差
4：创建时间戳索引
5：五折子模型或者十折子模型
6：祖传参数调整
6：注 1：建议先做特征筛选和与调祖传参数让单个模型的分数尽可能高，再进行模型融合。
2：高手很多，不喜勿喷

【AI比赛|百度AI比赛-点击反欺诈预测-XGBoost十折子模型融合】