R語言 lightgbm 算法優化：不平衡二分類問題（附代碼）
來源：大數據文摘
本文約10000字，建議閲讀10分鐘本文以kaggle比賽的數據為例，為你講解不平衡二分類問題的解決方法。
本案例使用的數據為kaggle中“Santander Customer Satisfaction”比賽的數據。此案例為不平衡二分類問題，目標為最大化auc值（ROC曲線下方面積）。目前此比賽已經結束。
競賽題目鏈接為：
http://www.kaggle.com/c/santander-customer-satisfaction
1. 建模思路
本文檔採用微軟開源的lightgbm算法進行分類，運行速度極快。具體步驟為:
讀取數據；
並行運算：由於lightgbm包可以通過設置相應參數進行並行運算，因此不再調用doParallel與foreach包進行並行運算；
特徵選擇：使用mlr包提取了99%的chi.square；
調參：逐步調試lgb.cv函數的參數，並多次調試，直到滿意為止；
預測結果：用調試好的參數值構建lightgbm模型，輸出預測結果；本案例所用程序輸出結果的ROC值為0.833386,已超過Private Leaderboard排名第一的結果(0.829072)。
2. lightgbm算法
由於lightgbm算法沒有給出具體的數學公式，因此此處不再介紹，如有需要，請查看github項目網址。
lightgbm算法具體介紹網址：
http://github.com/Microsoft/LightGBM
讀取數據
options(java.parameters = "-Xmx8g") ## 特徵選擇時使用，但是需要在加載包之前設置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")
數據探索
1. 設置並行運算
library(dplyr)library(mlr)library(parallelMap)parallelStartSocket(2)
2. 數據各列初步探索
summarizeColumns(lgb_tr1) %>% View()
3. 處理缺失值
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()
)
)
處理缺失值後：
summarizeColumns(imp_tr1$data) %>% View()
4. 觀察訓練數據類別的比例–數據類別不平衡
table(lgb_tr1$TARGET)
5. 剔除數據集中的常數列
lgb_tr2 <- removeConstantFeatures(imp_tr1$data)lgb_te2 <- removeConstantFeatures(imp_te1$data)
6. 保留訓練數據集與測試數據及相同的列
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$tr2_name[2:dim(tr2_name_inner)[1]])]lgb_tr2 <- cbind(lgb_tr2, TARGET)
注：
1）由於本次使用lightgbm算法，故而不對數據進行標準化處理；
2）lightgbm算法運行效率極高，1GB內不進行特徵篩選也可以運行的極快，但是此處進行特徵篩選，以進一步加快運行速率；
3）本案例直接進行特徵篩選，未生成衍生變量，原因為：不知特徵實際意義，不好隨機生成。
特徵篩選–卡方檢驗
library(lightgbm)
1. 試算最大權重值程序，後面將繼續優化
grid_search <- expand.grid( weight = seq(1, 30, 2) ## table(lgb_tr1$TARGET)[1] / table(lgb_tr1$TARGET)[2] = 24.27261 ## 故而設定weight在[1, 30]之間)
lgb_rate_1 <- numeric(length = nrow(grid_search))
set.seed(0)
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr2$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 ) # 參數 params <- list( objective = 'binary', metric = 'auc' ) # 交叉驗證 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$perf <- lgb_rate_1ggplot(grid_search,aes(x = weight, y = perf)) + geom_point()
從此圖可知auc值受權重影響不大,在weight=5時達到最大。
2. 特徵選擇
1) 特徵選擇
lgb_tr2$TARGET <- factor(lgb_tr2$TARGET)lgb.task <- makeClassifTask(data = lgb_tr2, target = 'TARGET')lgb.task.smote <- oversample(lgb.task, rate = 5)fv_time <- system.time( fv <- generateFilterValuesData( lgb.task.smote, method = c('chi.squared') ## 此處可以使用信息增益/卡方檢驗的方法，但是不建議使用隨機森林方法，效率極低 ## 如果有興趣，也可以嘗試IV值方法篩選 ## 特徵工程決定目標值（此處為auc）的上限，可以把特徵篩選方法作為超參數處理 ))
2) 製圖查看
# plotFilterValues(fv)plotFilterValuesGGVIS(fv)
3) 提取99%的chi.squared（lightgbm算法效率極高，因此可以取更多的變量）
注：提取的X%的chi.squared中的X可以作為超參數處理。
fv_data2 <- fv$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) ## 減少了一半的自變量fv_feature <- fv_data2_filter$namelgb_tr3 <- lgb_tr2[, c(fv_feature, 'TARGET')]lgb_te3 <- lgb_te2[, fv_feature]
4) 寫出數據
write_csv(lgb_tr3, 'C:/users/Administrator/Documents/kaggle/scs_lgb/lgb_tr3_chi.csv')write_csv(lgb_te3, 'C:/users/Administrator/Documents/kaggle/scs_lgb/lgb_te3_chi.csv')
算法
lgb_tr <- rxImport('C:/Users/Administrator/Documents/kaggle/scs_lgb/lgb_tr3_chi.csv')lgb_te <- rxImport('C:/Users/Administrator/Documents/kaggle/scs_lgb/lgb_te3_chi.csv')## 建議lgb_te數據在預測時再讀取，以節約內存library(lightgbm)
1. 調試weight參數
grid_search <- expand.grid( weight = 1:30)
perf_weight_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 params <- list( objective = 'binary', metric = 'auc' ) # 交叉驗證 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$perf <- perf_weight_1ggplot(grid_search,aes(x = weight, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在weight=4時達到最大,呈遞減趨勢。
2. 調試learning_rate參數
grid_search <- expand.grid( learning_rate = 2 ^ (-(8:1)))
perf_learning_rate_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'] ) # 交叉驗證 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$perf <- perf_learning_rate_1ggplot(grid_search,aes(x = learning_rate, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在learning_rate=2^(-5) 時達到最大,但是 2^(-(6:3)) 區別極小，故取learning_rate = .125，提高運行速度。
3. 調試num_leaves參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = seq(50, 800, 50))
perf_num_leaves_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'] ) # 交叉驗證 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$perf <- perf_num_leaves_1ggplot(grid_search,aes(x = num_leaves, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在num_leaves=650時達到最大。
4. 調試min_data_in_leaf參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, min_data_in_leaf = 2 ^ (1:7))
perf_min_data_in_leaf_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_min_data_in_leaf_1ggplot(grid_search,aes(x = min_data_in_leaf, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值對min_data_in_leaf不敏感，因此不做調整。
5. 調試max_bin參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin = 2 ^ (5:10))
perf_max_bin_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_max_bin_1ggplot(grid_search,aes(x = max_bin, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在max_bin=2^10 時達到最大,需要再次微調max_bin值。
6. 微調max_bin參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin = 100 * (6:15))
perf_max_bin_2 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_max_bin_2ggplot(grid_search,aes(x = max_bin, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在max_bin=1000時達到最大。
7. 調試min_data_in_bin參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin=1000, min_data_in_bin = 2 ^ (1:9) )
perf_min_data_in_bin_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_min_data_in_bin_1ggplot(grid_search,aes(x = min_data_in_bin, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在min_data_in_bin=8時達到最大,但是變化極其細微,因此不做調整。
8. 調試feature_fraction參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin=1000, min_data_in_bin = 8, feature_fraction = seq(.5, 1, .02) )
perf_feature_fraction_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_feature_fraction_1ggplot(grid_search,aes(x = feature_fraction, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在feature_fraction=.62時達到最大,feature_fraction在[.60,.62]之間時，auc值保持穩定,表現較好;從.64開始呈下降趨勢。
9. 調試min_sum_hessian參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin=1000, min_data_in_bin = 8, feature_fraction = .62, min_sum_hessian = seq(0, .02, .001))
perf_min_sum_hessian_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_min_sum_hessian_1ggplot(grid_search,aes(x = min_sum_hessian, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在min_sum_hessian=0.005時達到最大,建議min_sum_hessian取值在[0.002, 0.005]區間,0.005後呈遞減趨勢。
10. 調試lamda參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin=1000, min_data_in_bin = 8, feature_fraction = .62, min_sum_hessian = .005, lambda_l1 = seq(0, .01, .002), lambda_l2 = seq(0, .01, .002))
perf_lamda_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_lamda_1ggplot(data = grid_search, aes(x = lambda_l1, y = perf)) + geom_point() + facet_wrap(~ lambda_l2, nrow = 5)
從此圖可知建議lambda_l1 = 0, lambda_l2 = 0
11. 調試drop_rate參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin=1000, min_data_in_bin = 8, feature_fraction = .62, min_sum_hessian = .005, lambda_l1 = 0, lambda_l2 = 0, drop_rate = seq(0, 1, .1))
perf_drop_rate_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_drop_rate_1ggplot(data = grid_search, aes(x = drop_rate, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在drop_rate=0.2時達到最大,在0, .2, .5較好；在[0, 1]變化不大。
12. 調試max_drop參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin=1000, min_data_in_bin = 8, feature_fraction = .62, min_sum_hessian = .005, lambda_l1 = 0, lambda_l2 = 0, drop_rate = .2, max_drop = seq(1, 10, 2))
perf_max_drop_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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$perf <- perf_max_drop_1ggplot(data = grid_search, aes(x = max_drop, y = perf)) + geom_point() + geom_smooth()
從此圖可知auc值在max_drop=5時達到最大,在[1, 10]區間變化較小。
二次調參
1. 調試weight參數
grid_search <- expand.grid( learning_rate = .125, num_leaves = 650, max_bin=1000, min_data_in_bin = 8, feature_fraction = .62, min_sum_hessian = .005, lambda_l1 = 0, lambda_l2 = 0, drop_rate = .2, max_drop = 5)
perf_weight_2 <- numeric(length = nrow(grid_search))
for(i in 1:20){ lgb_weight <- (lgb_tr$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 ) # 參數 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'] ) # 交叉驗證 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()
從此圖可知auc值在weight>=3時auc趨於穩定, weight=7 the max
2. 調試learning_rate參數
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$TARGET, free_raw_data = FALSE, weight = lgb_weight ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'], max_bin = grid_search[i, 'max_bin'], min_data_in_bin = grid_search[i, 'min_data_in_bin'], feature_fraction = grid_search[i, 'feature_fraction'], min_sum_hessian = grid_search[i, 'min_sum_hessian'], lambda_l1 = grid_search[i, 'lambda_l1'], lambda_l2 = grid_search[i, 'lambda_l2'], drop_rate = grid_search[i, 'drop_rate'], max_drop = grid_search[i, 'max_drop'] ) # 交叉驗證 lgb_tr_mod <- lgb.cv( params, data = lgb_train, nrounds = 300, stratified = TRUE, nfold = 10, num_threads = 2, early_stopping_rounds = 10 ) perf_learning_rate_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]}
grid_search$perf <- perf_learning_rate_1ggplot(data = grid_search, aes(x = learning_rate, y = perf)) + geom_point() + geom_smooth()
結論：learning_rate=.11時，auc最大。
3. 調試num_leaves參數
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$TARGET, free_raw_data = FALSE, weight = lgb_weight ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'], max_bin = grid_search[i, 'max_bin'], min_data_in_bin = grid_search[i, 'min_data_in_bin'], feature_fraction = grid_search[i, 'feature_fraction'], min_sum_hessian = grid_search[i, 'min_sum_hessian'], lambda_l1 = grid_search[i, 'lambda_l1'], lambda_l2 = grid_search[i, 'lambda_l2'], drop_rate = grid_search[i, 'drop_rate'], max_drop = grid_search[i, 'max_drop'] ) # 交叉驗證 lgb_tr_mod <- lgb.cv( params, data = lgb_train, nrounds = 300, stratified = TRUE, nfold = 10, num_threads = 2, early_stopping_rounds = 10 ) perf_num_leaves_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]}
grid_search$perf <- perf_num_leaves_1ggplot(data = grid_search, aes(x = num_leaves, y = perf)) + geom_point() + geom_smooth()
結論：num_leaves=200時，auc最大。
4. 調試max_bin參數
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$TARGET, free_raw_data = FALSE, weight = lgb_weight ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'], max_bin = grid_search[i, 'max_bin'], min_data_in_bin = grid_search[i, 'min_data_in_bin'], feature_fraction = grid_search[i, 'feature_fraction'], min_sum_hessian = grid_search[i, 'min_sum_hessian'], lambda_l1 = grid_search[i, 'lambda_l1'], lambda_l2 = grid_search[i, 'lambda_l2'], drop_rate = grid_search[i, 'drop_rate'], max_drop = grid_search[i, 'max_drop'] ) # 交叉驗證 lgb_tr_mod <- lgb.cv( params, data = lgb_train, nrounds = 300, stratified = TRUE, nfold = 10, num_threads = 2, early_stopping_rounds = 10 ) perf_max_bin_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]}
grid_search$perf <- perf_max_bin_1ggplot(data = grid_search, aes(x = max_bin, y = perf)) + geom_point() + geom_smooth()
結論：max_bin=600時，auc最大;400，800也是可接受值。
5. 調試min_data_in_bin參數
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$TARGET, free_raw_data = FALSE, weight = lgb_weight ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'], max_bin = grid_search[i, 'max_bin'], min_data_in_bin = grid_search[i, 'min_data_in_bin'], feature_fraction = grid_search[i, 'feature_fraction'], min_sum_hessian = grid_search[i, 'min_sum_hessian'], lambda_l1 = grid_search[i, 'lambda_l1'], lambda_l2 = grid_search[i, 'lambda_l2'], drop_rate = grid_search[i, 'drop_rate'], max_drop = grid_search[i, 'max_drop'] ) # 交叉驗證 lgb_tr_mod <- lgb.cv( params, data = lgb_train, nrounds = 300, stratified = TRUE, nfold = 10, num_threads = 2, early_stopping_rounds = 10 ) perf_min_data_in_bin_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]}
grid_search$perf <- perf_min_data_in_bin_1ggplot(data = grid_search, aes(x = min_data_in_bin, y = perf)) + geom_point() + geom_smooth()
結論：min_data_in_bin=45時，auc最大；其中25是可接受值。
6. 調試feature_fraction參數
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$TARGET, free_raw_data = FALSE, weight = lgb_weight ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'], max_bin = grid_search[i, 'max_bin'], min_data_in_bin = grid_search[i, 'min_data_in_bin'], feature_fraction = grid_search[i, 'feature_fraction'], min_sum_hessian = grid_search[i, 'min_sum_hessian'], lambda_l1 = grid_search[i, 'lambda_l1'], lambda_l2 = grid_search[i, 'lambda_l2'], drop_rate = grid_search[i, 'drop_rate'], max_drop = grid_search[i, 'max_drop'] ) # 交叉驗證 lgb_tr_mod <- lgb.cv( params, data = lgb_train, nrounds = 300, stratified = TRUE, nfold = 10, num_threads = 2, early_stopping_rounds = 10 ) perf_feature_fraction_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]}
grid_search$perf <- perf_feature_fraction_1ggplot(data = grid_search, aes(x = feature_fraction, y = perf)) + geom_point() + geom_smooth()
結論：feature_fraction=.54時，auc最大, .56, .58時也較好。
7. 調試min_sum_hessian參數
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$TARGET, free_raw_data = FALSE, weight = lgb_weight ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'], max_bin = grid_search[i, 'max_bin'], min_data_in_bin = grid_search[i, 'min_data_in_bin'], feature_fraction = grid_search[i, 'feature_fraction'], min_sum_hessian = grid_search[i, 'min_sum_hessian'], lambda_l1 = grid_search[i, 'lambda_l1'], lambda_l2 = grid_search[i, 'lambda_l2'], drop_rate = grid_search[i, 'drop_rate'], max_drop = grid_search[i, 'max_drop'] ) # 交叉驗證 lgb_tr_mod <- lgb.cv( params, data = lgb_train, nrounds = 300, stratified = TRUE, nfold = 10, num_threads = 2, early_stopping_rounds = 10 ) perf_min_sum_hessian_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]}
grid_search$perf <- perf_min_sum_hessian_1ggplot(data = grid_search, aes(x = min_sum_hessian, y = perf)) + geom_point() + geom_smooth()
結論：min_sum_hessian=0.0065時auc取得最大值，取min_sum_hessian=0.003，0.0055時可接受。
8. 調試lambda參數
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$TARGET, free_raw_data = FALSE, weight = lgb_weight ) # 參數 params <- list( objective = 'binary', metric = 'auc', learning_rate = grid_search[i, 'learning_rate'], num_leaves = grid_search[i, 'num_leaves'], max_bin = grid_search[i, 'max_bin'], min_data_in_bin = grid_search[i, 'min_data_in_bin'], feature_fraction = grid_search[i, 'feature_fraction'], min_sum_hessian = grid_search[i, 'min_sum_hessian'], lambda_l1 = grid_search[i, 'lambda_l1'], lambda_l2 = grid_search[i, 'lambda_l2'], drop_rate = grid_search[i, 'drop_rate'], max_drop = grid_search[i, 'max_drop'] ) # 交叉驗證 lgb_tr_mod <- lgb.cv( params, data = lgb_train, nrounds = 300, stratified = TRUE, nfold = 10, num_threads = 2, early_stopping_rounds = 10 ) perf_lambda_1[i] <- unlist(lgb_tr_mod$record_evals$valid$auc$eval)[length(unlist(lgb_tr_mod$record_evals$valid$auc$eval))]}
grid_search$perf <- perf_lambda_1ggplot(data = grid_search, aes(x = lambda_l1, y = perf)) + geom_point() + facet_wrap(~ lambda_l2, nrow = 5)
結論：lambda與auc整體呈負相關，取lambda_l1=.0002, lambda_l2 = .0004
9. 調試drop_rate參數
結論：drop_rate=.4時取到最大值，.15, .25可接受。
10. 調試max_drop參數
結論：drop_rate=.4時取到最大值，.15, .25可接受。
預測
1. 權重
lgb_weight <- (lgb_tr$TARGET * 7 + 1) / sum(lgb_tr$TARGET * 7 + 1)
2. 訓練數據集
lgb_train <- lgb.Dataset( data = data.matrix(lgb_tr[, 1:148]), label = lgb_tr$TARGET, free_raw_data = FALSE, weight = lgb_weight)
3. 訓練
# 參數params <- list( learning_rate = .11, num_leaves = 200, max_bin = 600, min_data_in_bin = 45, feature_fraction = .54, min_sum_hessian = 0.0065, lambda_l1 = .0002, lambda_l2 = .0004, drop_rate = .4, max_drop = 14)# 模型lgb_mod <- lightgbm( params = params, data = lgb_train, nrounds = 300, early_stopping_rounds = 10, num_threads = 2)# 預測lgb.pred <- predict(lgb_mod, data.matrix(lgb_te))
4. 結果
lgb.pred2 <- matrix(unlist(lgb.pred), ncol = 1)lgb.pred3 <- data.frame(lgb.pred2)
5. 輸出
write.csv(lgb.pred3, "C:/Users/Administrator/Documents/kaggle/scs_lgb/lgb.pred1_tr.csv")
注：此處給在校讀書的朋友一些建議：
1. 在學校學習機器學習算法時，測試所用數據量一般較少，因此可以嘗試大多數算法，大多數的R函數，例如測試隨機森林算法時，可以選擇randomforest包，如果數據量稍微增多，可以設置並行運算，但是如果數據量達到GB級別，並行運算randomforest包也處理不了了，並且內存會溢出；建議使用專業版R中的函數；
2. 學校學習主要針對理論進行學習，測試數據一般較為乾淨，實際數據結構一般更為複雜一些。
編輯：黃繼彥
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