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lightgbm算法优化-不平衡二分类问题

2018-3-12 11:10| 发布者: 炼数成金_小数| 查看: 18455| 评论: 0|原作者: 苏高生|来自: 大数据文摘

摘要: 本文档采用微软开源的lightgbm算法进行分类,运行速度极快。具体步骤为:读取数据;并行运算:由于lightgbm包可以通过设置相应参数进行并行运算,因此不再调用doParallel与foreach包进行并行运算;特征选择:使用mlr ...

算法 模型 数学 案例 微软

本案例使用的数据为kaggle中“Santander Customer Satisfaction”比赛的数据。此案例为不平衡二分类问题,目标为较大化auc值(ROC曲线下方面积)。目前此比赛已经结束。

竞赛题目链接为:
https://www.kaggle.com/c/santander-customer-satisfaction 

2.建模思路
本文档采用微软开源的lightgbm算法进行分类,运行速度极快。具体步骤为:
读取数据;

并行运算:由于lightgbm包可以通过设置相应参数进行并行运算,因此不再调用doParallel与foreach包进行并行运算;

特征选择:使用mlr包提取了99%的chi.square;

调参:逐步调试lgb.cv函数的参数,并多次调试,直到满意为止;

预测结果:用调试好的参数值构建lightgbm模型,输出预测结果;本案例所用程序输出结果的ROC值为0.833386,已超过Private Leaderboard排名第一的结果(0.829072)。

3.lightgbm算法
由于lightgbm算法没有给出具体的数学公式,因此此处不再介绍,如有需要,请查看github项目网址。

lightgbm算法具体介绍网址:
https://github.com/Microsoft/LightGBM

读取数据
options(Java.parameters = "-Xmx8g") ## 特征选择时使用,但是需要在加载包之前设置
library(readr)
lgb_tr1 <- read_csv("C:/Users/Administrator/Documents/kaggle/scs_lgb/train.csv")
lgb_te1 <- read_csv("C:/Users/Administrator/Documents/kaggle/scs_lgb/test.csv")

数据探索
1.设置并行运算
library(dplyr)
library(mlr)
library(parallelMap)
parallelStartSocket(2)

2.数据各列初步探索
summarizeColumns(lgb_tr1) %>% View()

3.处理缺失值
impute missing values by mean and mode
imp_tr1 <- impute(
    as.data.frame(lgb_tr1), 
    classes = list(
        integer = imputeMean(), 
        numeric = imputeMean()
    )
)
imp_te1 <- impute(
    as.data.frame(lgb_te1), 
    classes = list(
        integer = imputeMean(), 
        numeric = imputeMean()
    )
)

处理缺失值后
summarizeColumns(imp_tr1$data) %>% View()

4.观察训练数据类别的比例–数据类别不平衡
table(lgb_tr1$TARGET)

5.剔除数据集中的常数列
lgb_tr2 <- removeConstantFeatures(imp_tr1$data)
lgb_te2 <- removeConstantFeatures(imp_te1$data)

6.保留训练数据集与测试数据及相同的列
tr2_name <- data.frame(tr2_name = colnames(lgb_tr2))
te2_name <- data.frame(te2_name = colnames(lgb_te2))
tr2_name_inner <- tr2_name %>% 
    inner_join(te2_name, by = c('tr2_name' = 'te2_name'))
TARGET = data.frame(TARGET = lgb_tr2$TARGET)
lgb_tr2 <- lgb_tr2[, c(tr2_name_inner$tr2_name[2:dim(tr2_name_inner)[1]])]
lgb_te2 <- lgb_te2[, c(tr2_name_inner$tr2_name[2:dim(tr2_name_inner)[1]])]
lgb_tr2 <- cbind(lgb_tr2, TARGET)

注:
1)由于本次使用lightgbm算法,故而不对数据进行标准化处理;
2)lightgbm算法运行效率极高,1GB内不进行特征筛选也可以运行的极快,但是此处进行特征筛选,以进一步加快运行速率;
3)本案例直接进行特征筛选,未生成衍生变量,原因为:不知特征实际意义,不好随机生成。

特征筛选–卡方检验
library(lightgbm)

1.试算较大权重值程序,后面将继续优化
grid_search <- expand.grid(
    weight = seq(1, 30, 2) 
    ## table(lgb_tr1$TARGET)[1] / table(lgb_tr1$TARGET)[2] = 24.27261
    ## 故而设定weight在[1, 30]之间
)

lgb_rate_1 <- numeric(length = nrow(grid_search))
set.seed(0)
for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr2$TARGET * i + 1) / sum(lgb_tr2$TARGET * i + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr2[, 1:300]), 
        label = lgb_tr2$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc'
    )
    # 交叉验证
    lgb_tr2_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        learning_rate = .1,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    lgb_rate_1[i] <- unlist(lgb_tr2_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr2_mod$record_evals$valid$auc$eval))]
}
library(ggplot2)
grid_search$perf <- lgb_rate_1
ggplot(grid_search,aes(x = weight, y = perf)) + 
    geom_point()
从此图可知auc值受权重影响不大,在weight=5时达到较大。

3.特征选择
1)特征选择
lgb_tr2$TARGET <- factor(lgb_tr2$TARGET)
lgb.task <- makeClassifTask(data = lgb_tr2, target = 'TARGET')
lgb.task.smote <- oversample(lgb.task, rate = 5)
fv_time <- system.time(
    fv <- generateFilterValuesData(
        lgb.task.smote,
        method = c('chi.squared')
        ## 此处可以使用信息增益/卡方检验的方法,但是不建议使用随机森林方法,效率极低
        ## 如果有兴趣,也可以尝试IV值方法筛选
        ## 特征工程决定目标值(此处为auc)的上限,可以把特征筛选方法作为超参数处理
    )
)

2)制图查看
# plotFilterValues(fv)
plotFilterValuesGGVIS(fv)

3)提取99%的chi.squared(lightgbm算法效率极高,因此可以取更多的变量)

注:提取的X%的chi.squared中的X可以作为超参数处理。
fv_data2 <- fv$data %>% 
    arrange(desc(chi.squared)) %>% 
    mutate(chi_gain_cul = cumsum(chi.squared) / sum(chi.squared))

fv_data2_filter <- fv_data2 %>% filter(chi_gain_cul <= 0.99)
dim(fv_data2_filter) ## 减少了一半的自变量
fv_feature <- fv_data2_filter$name
lgb_tr3 <- lgb_tr2[, c(fv_feature, 'TARGET')]
lgb_te3 <- lgb_te2[, fv_feature]

4)写出数据
write_csv(lgb_tr3, 'C:/users/Administrator/Documents/kaggle/scs_lgb/lgb_tr3_chi.csv')
write_csv(lgb_te3, 'C:/users/Administrator/Documents/kaggle/scs_lgb/lgb_te3_chi.csv')

算法
lgb_tr <- rxImport('C:/Users/Administrator/Documents/kaggle/scs_lgb/lgb_tr3_chi.csv')
lgb_te <- rxImport('C:/Users/Administrator/Documents/kaggle/scs_lgb/lgb_te3_chi.csv')
## 建议lgb_te数据在预测时再读取,以节约内存
library(lightgbm)

1.调试weight参数
grid_search <- expand.grid(
    weight = 1:30
)

perf_weight_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * i + 1) / sum(lgb_tr$TARGET * i + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc'
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        learning_rate = .1,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_weight_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

library(ggplot2)
grid_search$perf <- perf_weight_1
ggplot(grid_search,aes(x = weight, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在weight=4时达到较大,呈递减趋势。

2.调试learning_rate参数
grid_search <- expand.grid(
    learning_rate = 2 ^ (-(8:1))
)

perf_learning_rate_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_learning_rate_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_learning_rate_1
ggplot(grid_search,aes(x = learning_rate, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在learning_rate=2^(-5) 时达到较大,但是 2^(-(6:3)) 区别极小,故取learning_rate = .125,提高运行速度。

3.调试num_leaves参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = seq(50, 800, 50)
)

perf_num_leaves_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_num_leaves_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_num_leaves_1
ggplot(grid_search,aes(x = num_leaves, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在num_leaves=650时达到较大。

4.调试min_data_in_leaf参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    min_data_in_leaf = 2 ^ (1:7)
)

perf_min_data_in_leaf_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        min_data_in_leaf = grid_search[i, 'min_data_in_leaf']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_min_data_in_leaf_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_min_data_in_leaf_1
ggplot(grid_search,aes(x = min_data_in_leaf, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值对min_data_in_leaf不敏感,因此不做调整。

5.调试max_bin参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin = 2 ^ (5:10)
)

perf_max_bin_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_max_bin_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_max_bin_1
ggplot(grid_search,aes(x = max_bin, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在max_bin=2^10 时达到较大,需要再次微调max_bin值。

6.微调max_bin参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin = 100 * (6:15)
)

perf_max_bin_2 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_max_bin_2[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_max_bin_2
ggplot(grid_search,aes(x = max_bin, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在max_bin=1000时达到较大。

7.调试min_data_in_bin参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 2 ^ (1:9)
    
)

perf_min_data_in_bin_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_min_data_in_bin_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_min_data_in_bin_1
ggplot(grid_search,aes(x = min_data_in_bin, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在min_data_in_bin=8时达到较大,但是变化极其细微,因此不做调整。

8.调试feature_fraction参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = seq(.5, 1, .02)
    
)

perf_feature_fraction_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_feature_fraction_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_feature_fraction_1
ggplot(grid_search,aes(x = feature_fraction, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在feature_fraction=.62时达到较大,feature_fraction在[.60,.62]之间时,auc值保持稳定,表现较好;从.64开始呈下降趋势。

9.调试min_sum_hessian参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = seq(0, .02, .001)
)

perf_min_sum_hessian_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_min_sum_hessian_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_min_sum_hessian_1
ggplot(grid_search,aes(x = min_sum_hessian, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在min_sum_hessian=0.005时达到较大,建议min_sum_hessian取值在[0.002, 0.005]区间,0.005后呈递减趋势。

10.调试lamda参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = seq(0, .01, .002),
    lambda_l2 = seq(0, .01, .002)
)

perf_lamda_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_lamda_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_lamda_1
ggplot(data = grid_search, aes(x = lambda_l1, y = perf)) + 
    geom_point() + 
    facet_wrap(~ lambda_l2, nrow = 5)
从此图可知建议lambda_l1 = 0, lambda_l2 = 0

11.调试drop_rate参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = seq(0, 1, .1)
)

perf_drop_rate_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_drop_rate_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_drop_rate_1
ggplot(data = grid_search, aes(x = drop_rate, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在drop_rate=0.2时达到较大,在0, .2, .5较好;在[0, 1]变化不大。

12.调试max_drop参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = seq(1, 10, 2)
)

perf_max_drop_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 4 + 1) / sum(lgb_tr$TARGET * 4 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_max_drop_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_max_drop_1
ggplot(data = grid_search, aes(x = max_drop, y = perf)) + 
    geom_point() +
    geom_smooth()
从此图可知auc值在max_drop=5时达到较大,在[1, 10]区间变化较小。

二次调参
1.调试weight参数
grid_search <- expand.grid(
    learning_rate = .125,
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = 5
)

perf_weight_2 <- numeric(length = nrow(grid_search))

for(i in 1:20){
    lgb_weight <- (lgb_tr$TARGET * i + 1) / sum(lgb_tr$TARGET * i + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[1, 'learning_rate'],
        num_leaves = grid_search[1, 'num_leaves'],
        max_bin = grid_search[1, 'max_bin'],
        min_data_in_bin = grid_search[1, 'min_data_in_bin'],
        feature_fraction = grid_search[1, 'feature_fraction'],
        min_sum_hessian = grid_search[1, 'min_sum_hessian'],
        lambda_l1 = grid_search[1, 'lambda_l1'],
        lambda_l2 = grid_search[1, 'lambda_l2'],
        drop_rate = grid_search[1, 'drop_rate'],
        max_drop = grid_search[1, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        learning_rate = .1,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_weight_2[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

library(ggplot2)
ggplot(data.frame(num = 1:length(perf_weight_2), perf = perf_weight_2), aes(x = num, y = perf)) + 
    geom_point() + 
    geom_smooth()
从此图可知auc值在weight>=3时auc趋于稳定, weight=7 the max

2.调试learning_rate参数
grid_search <- expand.grid(
    learning_rate = seq(.05, .5, .03),
    num_leaves = 650,
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = 5
)

perf_learning_rate_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_learning_rate_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_learning_rate_1
ggplot(data = grid_search, aes(x = learning_rate, y = perf)) + 
    geom_point() +
    geom_smooth()
结论:learning_rate=.11时,auc较大。

3.调试num_leaves参数
grid_search <- expand.grid(
    learning_rate = .11,
    num_leaves = seq(100, 800, 50),
    max_bin=1000,
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = 5
)

perf_num_leaves_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_num_leaves_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_num_leaves_1
ggplot(data = grid_search, aes(x = num_leaves, y = perf)) + 
    geom_point() +
    geom_smooth()
结论:num_leaves=200时,auc较大。

4.调试max_bin参数
grid_search <- expand.grid(
    learning_rate = .11,
    num_leaves = 200,
    max_bin = seq(100, 1500, 100),
    min_data_in_bin = 8,
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = 5
)

perf_max_bin_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_max_bin_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_max_bin_1
ggplot(data = grid_search, aes(x = max_bin, y = perf)) + 
    geom_point() +
    geom_smooth()
结论:max_bin=600时,auc较大;400,800也是可接受值。

5.调试min_data_in_bin参数
grid_search <- expand.grid(
    learning_rate = .11,
    num_leaves = 200,
    max_bin = 600,
    min_data_in_bin = seq(5, 50, 5),
    feature_fraction = .62,
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = 5
)

perf_min_data_in_bin_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_min_data_in_bin_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_min_data_in_bin_1
ggplot(data = grid_search, aes(x = min_data_in_bin, y = perf)) + 
    geom_point() +
    geom_smooth()
结论:min_data_in_bin=45时,auc较大;其中25是可接受值。

6.调试feature_fraction参数
grid_search <- expand.grid(
    learning_rate = .11,
    num_leaves = 200,
    max_bin = 600,
    min_data_in_bin = 45,
    feature_fraction = seq(.5, .9, .02),
    min_sum_hessian = .005,
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = 5
)

perf_feature_fraction_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_feature_fraction_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_feature_fraction_1
ggplot(data = grid_search, aes(x = feature_fraction, y = perf)) + 
    geom_point() +
    geom_smooth()
结论:feature_fraction=.54时,auc较大, .56, .58时也较好。

7.调试min_sum_hessian参数
grid_search <- expand.grid(
    learning_rate = .11,
    num_leaves = 200,
    max_bin = 600,
    min_data_in_bin = 45,
    feature_fraction = .54,
    min_sum_hessian = seq(.001, .008, .0005),
    lambda_l1 = 0,
    lambda_l2 = 0,
    drop_rate = .2,
    max_drop = 5
)

perf_min_sum_hessian_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_min_sum_hessian_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_min_sum_hessian_1
ggplot(data = grid_search, aes(x = min_sum_hessian, y = perf)) + 
    geom_point() +
    geom_smooth()
结论:min_sum_hessian=0.0065时auc取得较大值,取min_sum_hessian=0.003,0.0055时可接受。

8.调试lambda参数
grid_search <- expand.grid(
    learning_rate = .11,
    num_leaves = 200,
    max_bin = 600,
    min_data_in_bin = 45,
    feature_fraction = .54,
    min_sum_hessian = 0.0065,
    lambda_l1 = seq(0, .001, .0002),
    lambda_l2 = seq(0, .001, .0002),
    drop_rate = .2,
    max_drop = 5
)

perf_lambda_1 <- numeric(length = nrow(grid_search))

for(i in 1:nrow(grid_search)){
    lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
    
    lgb_train <- lgb.Dataset(
        data = data.matrix(lgb_tr[, 1:148]), 
        label = lgb_tr$TARGET, 
        free_raw_data = FALSE,
        weight = lgb_weight
    )
    
    # 参数
    params <- list(
        objective = 'binary',
        metric = 'auc',
        learning_rate = grid_search[i, 'learning_rate'],
        num_leaves = grid_search[i, 'num_leaves'],
        max_bin = grid_search[i, 'max_bin'],
        min_data_in_bin = grid_search[i, 'min_data_in_bin'],
        feature_fraction = grid_search[i, 'feature_fraction'],
        min_sum_hessian = grid_search[i, 'min_sum_hessian'],
        lambda_l1 = grid_search[i, 'lambda_l1'],
        lambda_l2 = grid_search[i, 'lambda_l2'],
        drop_rate = grid_search[i, 'drop_rate'],
        max_drop = grid_search[i, 'max_drop']
    )
    # 交叉验证
    lgb_tr_mod <- lgb.cv(
        params,
        data = lgb_train,
        nrounds = 300,
        stratified = TRUE,
        nfold = 10,
        num_threads = 2,
        early_stopping_rounds = 10
    )
    perf_lambda_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]
}

grid_search$perf <- perf_lambda_1
ggplot(data = grid_search, aes(x = lambda_l1, y = perf)) + 
    geom_point() + 
    facet_wrap(~ lambda_l2, nrow = 5)
结论:lambda与auc整体呈负相关,取lambda_l1=.0002, lambda_l2 = .0004

9.调试drop_rate参数

结论:drop_rate=.4时取到较大值,.15, .25可接受。

10.调试max_drop参数


结论:drop_rate=.4时取到较大值,.15, .25可接受。

预测
1.权重
lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)

2.训练数据集
lgb_train <- lgb.Dataset(
    data = data.matrix(lgb_tr[, 1:148]), 
    label = lgb_tr$TARGET, 
    free_raw_data = FALSE,
    weight = lgb_weight
)

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