2019招行fintech精英训练营线上赛-赛题2参赛源码+项目说明（第2）.zip

## 2019招行fintech精英训练营线上赛--赛题2开源代码 最终排名：第二名 融合模型最高分：A榜：0.74062；B榜：0.74347 单模型最高分：A榜：0.73989；B榜：0.7422  ### 题目介绍 赛题2：基于收支记录判断借贷意愿 手机APP的首页首屏，是流量最大的页面，对应页面资源极其珍贵。现要将借钱卡片投放到首页，展现给最有点击意愿的用户。请基于用户真实的历史收支数据来预测用户是否会点击借钱卡片。 ### 解决方案 #### 特征 ```python features227 = train227[['id']] 用户基础属性227 = pd.read_csv(features_dir227 + "用户基础属性.csv") features227 = pd.merge(features227, 用户基础属性227, on='id', how='left') 收入总和227 = pd.read_csv(features_dir227 + "收入总和.csv") features227 = pd.merge(features227, 收入总和227, on='id', how='left') 支出总和227 = pd.read_csv(features_dir227 + "支出总和.csv") features227 = pd.merge(features227, 支出总和227, on='id', how='left') 收支总和227 = pd.read_csv(features_dir227 + "收支总和.csv") features227 = pd.merge(features227, 收支总和227, on='id', how='left') 收支记录次数227 = pd.read_csv(features_dir227 + "收支记录次数.csv") features227 = pd.merge(features227, 收支记录次数227, on='id', how='left') 收入最大值227 = pd.read_csv(features_dir227 + "收入最大值.csv") features227 = pd.merge(features227, 收入最大值227, on='id', how='left') 支出最大值227 = pd.read_csv(features_dir227 + "支出最大值.csv") features227 = pd.merge(features227, 支出最大值227, on='id', how='left') 各类别收支记录次数227 = pd.read_csv(features_dir227 + "各类别收支记录次数.csv") features227 = pd.merge(features227, 各类别收支记录次数227, on='id', how='left') 各类别收支金额227 = pd.read_csv(features_dir227 + "各类别收支金额.csv") features227 = pd.merge(features227, 各类别收支金额227, on='id', how='left') # 加入以下两个特征后，有明显提升 各原始类别收支记录次数227 = pd.read_csv(features_dir227 + "各原始类别收支记录次数.csv") features227 = pd.merge(features227, 各原始类别收支记录次数227, on='id', how='left') 各原始类别收支金额227 = pd.read_csv(features_dir227 + "各原始类别收支金额.csv") features227 = pd.merge(features227, 各原始类别收支金额227, on='id', how='left') 收入均值227 = pd.read_csv(features_dir227 + "收入均值.csv") features227 = pd.merge(features227, 收入均值227, on='id', how='left') 支出均值227 = pd.read_csv(features_dir227 + "支出均值.csv") features227 = pd.merge(features227, 支出均值227, on='id', how='left') 收支均值227 = pd.read_csv(features_dir227 + "收支均值.csv") features227 = pd.merge(features227, 收支均值227, on='id', how='left') # 选24个得分最高的原始类别特征，两两相加，得到288维特征，然后再筛选，略有提升 原始类别次数两两相加组合特征227 = pd.read_csv(features_dir227 + "原始类别次数两两相加组合特征.csv") features227 = pd.merge(features227, 原始类别次数两两相加组合特征227, on='id', how='left') 原始类别金额两两相加组合特征227 = pd.read_csv(features_dir227 + "原始类别金额两两相加组合特征.csv") features227 = pd.merge(features227, 原始类别金额两两相加组合特征227, on='id', how='left') ``` #### 模型：xgboost ```python def pipeline(iteration,random_seed,max_depth,lambd,subsample,colsample_bytree,min_child_weight): if max_depth==3: n_estimators = 400 elif max_depth==4: n_estimators = 300 kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) preds306 = np.zeros((X306_csr.shape[0])) i = 0 models = [0]*kf.get_n_splits() roc_scores = [] for train_index, val_index in kf.split(X227_csr, Y227): X_train, X_val = X227_csr[train_index], X227_csr[val_index] y_train, y_val = Y227[train_index], Y227[val_index] eval_set = [(X_train, y_train), (X_val, y_val)] models[i] = xgb.XGBClassifier(n_estimators=n_estimators, max_depth = max_depth, reg_lambda = lambd, subsample = subsample, colsample_bytree = colsample_bytree, min_child_weight = min_child_weight, n_jobs = 6, random_state=random_seed) models[i].fit(X_train, y_train, eval_metric=['auc', "error", "logloss"], eval_set=eval_set, verbose=False) score = models[i].predict_proba(X_val)[:, 1] roc = roc_auc_score(y_val, score) roc_scores.append(roc) print(roc) preds306 = preds306 + models[i].predict_proba(X306_csr)[:,1] i+=1 preds306 = preds306 / kf.get_n_splits() pickle.dump(preds306, open(inter_dir + 'preds306_'+str(iteration)+'.p', 'wb')) pickle.dump(models, open(inter_dir + 'models_'+str(iteration)+'.p','wb')) pickle.dump(roc_scores, open(inter_dir + 'roc_scores_'+str(iteration)+'.p','wb')) print("mean_roc_score: {}".format(np.mean(roc_scores))) ``` #### 融合：ranking 随机参数，训练出30个模型。 ```python random.seed(42) random_seed = list(range(43)) max_depth = [3,4] lambd = list(range(0,5)) subsample = [i/1000.0 for i in range(700,800)] colsample_bytree = [i/1000.0 for i in range(700,800)] min_child_weight = [i/100.0 for i in range(150,250)] # n_feature = range(150,282,2) random.shuffle(random_seed) random.shuffle(max_depth) random.shuffle(lambd) random.shuffle(subsample) random.shuffle(colsample_bytree) random.shuffle(min_child_weight) ``` 然后做融合。 ranking是先把每个模型的结果排序，然后再求平均。 比赛中发现，ranking比bagging（直接对每个模型结果求平均），略微好一丁点。 ### 其它开源 - uncleban (第一名) 大家久等的开源来了~~ https://github.com/uncleban/2019_cmb_fintech 第二题a榜第一baseline，这份代码可以复现线上a榜0.74151，b榜0.74242的成绩。提取特征部分用时2min左右，模型训练用时6min左右。 整理不易，大家的star就是最好的回报，谢谢大家，欢迎转发~ - VoyagerIII B榜0.73611 交流分享，欢迎star 用的XGBoost 特征两百维，有一定注释。 https://github.com/VoyagerIII/CERES