苏高生，西南财经大学统计学硕士毕业，现就职于中国电信，主要负责企业存量客户大数据分析、数据建模。研究方向：机器学习，最喜欢的编程语言：R语言，没有之一。
E-mail：sugs01@outlook.com
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紧接上文：lightgbm algorithm case of kaggle（上）
各位看客，请继续......
五、二次调参
1.调试weight参数
grid_search <- expand.grid(
learning_rate = .125,
num_leaves = 600,
max_bin = 30,
min_data_in_bin = 64,
feature_fraction = .64,
min_sum_hessian = .004,
lambda_l1 = .002,
lambda_l2 = .008,
drop_rate = .3,
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[, 1:137]),
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$eval))]}
ggplot(data.frame(num = 1:length(perf_weight_2), perf = perf_weight_2), aes(x = num, y = perf)) +
geom_point() +
geom_smooth()
结论：从此图可知auc值在weight>=2时auc 趋于稳定, weight=8时取最大值
2.调试learning_rate参数
grid_search <- expand.grid(
learning_rate = seq(.05, .5, .01),
num_leaves = 600,
max_bin = 30,
min_data_in_bin = 64,
feature_fraction = .64,
min_sum_hessian = .004,
lambda_l1 = .002,
lambda_l2 = .008,
drop_rate = .3,
max_drop = 5)
perf_learning_rate_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_learning_rate_1
ggplot(data = grid_search, aes(x = learning_rate, y = perf)) +
geom_point() +
geom_smooth()
结论：learning_rate=.2时，auc最大
3.调试num_leaves参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = seq(50, 800, 50),
max_bin = 30,
min_data_in_bin = 64,
feature_fraction = .64,
min_sum_hessian = .004,
lambda_l1 = .002,
lambda_l2 = .008,
drop_rate = .3,
max_drop = 5
)
perf_num_leaves_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_num_leaves_1
ggplot(data = grid_search, aes(x = num_leaves, y = perf)) +
geom_point() +
geom_smooth()
结论：num_leaves=300时，auc最大
4.调试max_bin参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = 300,
max_bin = seq(30, 150, 10),
min_data_in_bin = 64,
feature_fraction = .64,
min_sum_hessian = .004,
lambda_l1 = .002,
lambda_l2 = .008,
drop_rate = .3,
max_drop = 5
)
perf_max_bin_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_max_bin_1
ggplot(data = grid_search, aes(x = max_bin, y = perf)) +
geom_point() +
geom_smooth()
结论：max_bin=120时，auc最大
5.调试min_data_in_bin参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = 300,
max_bin = 120,
min_data_in_bin = seq(20, 100, 5),
feature_fraction = .64,
min_sum_hessian = .004,
lambda_l1 = .002,
lambda_l2 = .008,
drop_rate = .3,
max_drop = 5
)
perf_min_data_in_bin_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_min_data_in_bin_1
ggplot(data = grid_search, aes(x = min_data_in_bin, y = perf)) +
geom_point() +
geom_smooth()
结论：min_data_in_bin=20时，auc最大
6.调试feature_fraction参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = 300,
max_bin = 120,
min_data_in_bin = 20,
feature_fraction = .5,
min_sum_hessian = .004,
lambda_l1 = .002,
lambda_l2 = .008,
drop_rate = .3,
max_drop = 5)perf_feature_fraction_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_feature_fraction_1
ggplot(data = grid_search, aes(x = feature_fraction, y = perf)) +
geom_point() +
geom_smooth()
结论：feature_fraction=.5时，auc最大,=.62时也较好
7.调试min_sum_hessian参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = 300,
max_bin = 120,
min_data_in_bin = 20,
feature_fraction = .5,
min_sum_hessian = 0,
lambda_l1 = .002,
lambda_l2 = .008,
drop_rate = .3,
max_drop = 5
)
perf_min_sum_hessian_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_min_sum_hessian_1
ggplot(data = grid_search, aes(x = min_sum_hessian, y = perf)) +
geom_point() +
geom_smooth()
结论：min_sum_hessian与auc呈负相关，min_sum_hessian=0时，取min_sum_hessian=0
8.调试lambda参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = 300,
max_bin = 120,
min_data_in_bin = 20,
feature_fraction = .5,
min_sum_hessian = 0,
lambda_l1 = seq(0, .01, .002),
lambda_l2 = seq(0, .01, .002),
drop_rate = .3, max_drop = 5
)
perf_lambda_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_lamda_1
ggplot(data = grid_search, aes(x = lambda_l1, y = perf)) +
geom_point() +
facet_wrap(~ lambda_l2, nrow = 5)
结论：lambda与auc呈负相关，取lambda_l1=.002, lambda_l2 = .01
9.调试drop_rate参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = 300,
max_bin = 120,
min_data_in_bin = 20,
feature_fraction = .5,
min_sum_hessian = 0,
lambda_l1 = .002,
lambda_l2 = .01,
drop_rate = seq(0, .5, .05),
max_drop = 5)perf_drop_rate_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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_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$perf <- perf_drop_rate_1
ggplot(data = grid_search, aes(x = drop_rate, y = perf)) +
geom_point()
结论：drop_rate=.3时取到最大值，与第一次调参没有变化
10.调试max_drop参数
grid_search <- expand.grid(
learning_rate = .2,
num_leaves = 300,
max_bin = 120,
min_data_in_bin = 20,
feature_fraction = .5,
min_sum_hessian = 0,
lambda_l1 = .002,
lambda_l2 = .01,
drop_rate = .3,
max_drop = seq(19, 29, 2)
)
perf_max_drop_1 <- numeric(length = nrow(grid_search))
for(i in 1:nrow(grid_search)){
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$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$perf <- perf_max_drop_1ggplot(data = grid_search, aes(x = max_drop, y = perf)) + geom_point()
结论：max_drop=23时取到最大值
六、集成学习
1)参数
set.seed(1)
grid_search <- expand.grid(
learning_rate = sample(115:125, 10, replace = FALSE) / 100,
num_leaves = sample(250:350, 10, replace = FALSE),
max_bin = sample(115:125, 5, replace = FALSE),
min_data_in_bin = sample(18:22, replace = FALSE),
feature_fraction = c(.5, .62),
min_sum_hessian = 0,
lambda_l1 = .002,
lambda_l2 = c(.008, .009, .01),
drop_rate = sample(126:134, 4, replace = FALSE) / 1000,
max_drop = c(23, 27, 29)
)
sample_ind <- sample(dim(grid_search)[1], 100, replace = FALSE)
lgb.pred <- list()grid_search2 <- grid_search[sample_ind, ]rm(grid_search)
2)权重
lgb_weight <- (lgb_tr$TARGET * 8 + 1) / sum(lgb_tr$TARGET * 8 + 1)
3)训练数据集
lgb_train <- lgb.Dataset(
data = data.matrix(lgb_tr[, 1:137]),
label = lgb_tr$TARGET,
free_raw_data = FALSE,
weight = lgb_weight
)
4)训练
for (i in 1:nrow(grid_search2)[1]){
# 参数
params <- list(
objective = 'binary',
metric = 'auc',
learning_rate = grid_search2[i, 'learning_rate'],
num_leaves = grid_search2[i, 'num_leaves'],
max_bin = grid_search2[i, 'max_bin'],
min_data_in_bin = grid_search2[i, 'min_data_in_bin'],
feature_fraction = grid_search2[i, 'feature_fraction'],
min_sum_hessian = grid_search2[i, 'min_sum_hessian'],
lambda_l1 = grid_search2[i, 'lambda_l1'],
lambda_l2 = grid_search2[i, 'lambda_l2'],
drop_rate = grid_search2[i, 'drop_rate'],
max_drop = grid_search2[i, 'max_drop']
)
# 模型
lgb_mod <- lightgbm(
params = params,
data = lgb_train,
nrounds = 300,
early_stopping_rounds = 10,
num_threads = 2
)
# 预测
lgb.pred[[i]] <- predict(lgb_mod, data.matrix(lgb_te))}
5)结果
lgb.pred2 <- matrix(unlist(lgb.pred), ncol = 100)
lgb.pred3 <- data.frame(prob1 = apply(lgb.pred2, 1, mean))
6)输出
write.csv(lgb.pred3, "C:/Users/Administrator/Documents/kaggle/scs_lgb/lgb.pred1.csv"
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