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run_grid_reg_enc.R
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library(h2o)
library(KScorrect)
library(reshape2)
Sys.unsetenv("http_proxy")
###
# Load data, regression
###
cd <- read.table(file="train_full3.cd", head=F, sep="\t")
colnames(cd) <- c("index", "type")
cat_ind <- 1 + cd[cd$type == "Categ", 1]
train <- read.table(file="train_full3", head=F, sep="\t")
train[,1] <- as.numeric(train[,1])
train[,cat_ind] <- lapply(train[,cat_ind], factor)
valid <- read.table(file="test3", head=F, sep="\t")
valid[,1] <- as.numeric(valid[,1])
valid[,cat_ind] <- lapply(valid[,cat_ind], factor)
y <- "V1"
x <- setdiff(names(train), c(y))
port = sample(0:65536, 1)
localH2O = h2o.init(nthreads=-1, max_mem_size='60G', port=port)
h2_train <- as.h2o(train)
h2_valid <- as.h2o(valid)
###
# Grid search params
###
seed = 12345
seeds = c(12, 23, 34, 45, 56)
set.seed(seed)
n_models = 50
n_sample = 10
n_trees = 5000
metric = 'rmse'
categorical_encoding_options = c('Enum', 'OneHotExplicit', 'Binary', 'Eigen', 'LabelEncoder', 'SortByResponse')
histogram_type_options = c('uniform_adaptive', 'random', 'quantiles_global', 'round_robin')
gbm_params_default <- list(learn_rate = 0.1,
max_depth = 5,
sample_rate = 1.0,
col_sample_rate = 1.0,
col_sample_rate_change_per_level = 1,
col_sample_rate_per_tree = 1,
min_split_improvement = 1e-5,
min_rows = 10,
histogram_type = 'auto',
categorical_encoding = 'AUTO')
gbm_params_random <- list(learn_rate = rlunif(n_sample, exp(-7), exp(0)),
max_depth = round(runif(n_sample, 2, 10)/1)*1,
sample_rate = runif(n_sample, 0.5, 1),
col_sample_rate = runif(n_sample, 0.5, 1),
col_sample_rate_change_per_level = runif(n_sample, 0, 2),
col_sample_rate_per_tree = runif(n_sample, 0, 1),
min_split_improvement = rlunif(n_sample, exp(-16), exp(0)),
min_rows = rlunif(n_sample, exp(0), exp(5)),
histogram_type = histogram_type_options,
categorical_encoding = categorical_encoding_options)
gbm_search_criteria <- list(strategy = "RandomDiscrete",
max_models = n_models,
max_runtime_secs = 1200000)
###
# Functions
###
### Extract param list from a model
get_best_params <- function(model){
params <- list(learn_rate = model@allparameters$learn_rate,
max_depth = model@allparameters$max_depth,
sample_rate = model@allparameters$sample_rate,
col_sample_rate = model@allparameters$col_sample_rate,
col_sample_rate_change_per_level = model@allparameters$col_sample_rate_change_per_level,
col_sample_rate_per_tree = model@allparameters$col_sample_rate_per_tree,
min_split_improvement = model@allparameters$min_split_improvement,
min_rows = model@allparameters$min_rows,
histogram_type = model@allparameters$histogram_type,
categorical_encoding = model@allparameters$categorical_encoding)
return(params)
}
### Choose the best number of trees using all CV results and the target metric
get_best_cv <- function(model, nfolds = 5){
scoring_history_stats <- data.frame(matrix(NA,0,0))
for (i in 1:nfolds){
cv_model <- h2o.getModel(model@model$cross_validation_models[[i]]$name)
scoring_history <- data.frame(cv_model@model$scoring_history)[,c('number_of_trees', 'validation_rmse')]
scoring_history_stats <- rbind(scoring_history_stats, scoring_history)
}
scoring_history_avg <- dcast(scoring_history_stats, number_of_trees ~ ., mean, value.var = "validation_rmse")
colnames(scoring_history_avg) <- c('n_trees', 'rmse')
min_rmse <- min(scoring_history_avg$rmse)
best_n_trees <- scoring_history_avg[scoring_history_avg$rmse==min_rmse,]$n_trees
return(list(rmse_cv = min_rmse,
n_trees = best_n_trees))
}
### Iterate over all grid models, choose the best model using the target metric
get_best_model <- function(gbm_cv_perf){
best_rmse = Inf
for (i in 1:length(gbm_cv_perf@model_ids)){
model <- h2o.getModel(gbm_cv_perf@model_ids[[i]])
model_cv <- get_best_cv(model)
model_params <- get_best_params(model)
if (model_cv$rmse_cv < best_rmse){
best_rmse <- model_cv$rmse_cv
best_cv <- model_cv
best_params <- model_params
}
}
return(list(best_params = best_params,
best_cv = best_cv))
}
### Load model params from result_(default|tuned).tsv
load_params <- function(file){
tsv_default <- read.table(file=file, head=F, sep="\t", fill=T, colClasses = "character")
params <- tsv_default[2:11,]
param_list <- vector("list", nrow(params))
for (i in 1:nrow(params)) {
param_list[[i]] <- params[i,2]
}
names(param_list) <- params[,1]
n_trees = as.numeric(tsv_default[11,2])
return(list(param_list = param_list,
n_trees = n_trees))
}
### Train 1 model using grid method, use validation frame to calculate metrics
validate_model <- function(x, y, train, validate, params, n_trees, seed){
grid_id = paste0("gbm_validate", seed)
gbm_validate <- h2o.grid("gbm", x = x, y = y,
grid_id = grid_id,
training_frame = train,
validation_frame = validate,
ntrees = n_trees,
seed = seed,
hyper_params = params)
gbm_validate_perf <- h2o.getGrid(grid_id = grid_id,
sort_by = metric,
decreasing = FALSE)
model <- h2o.getModel(gbm_validate_perf@model_ids[[1]])
model_metrics <- model@model$validation_metrics
rmse_val <- model_metrics@metrics$RMSE
return(rmse_val)
}
###
# Train with default parameters
###
### CV to get the best ntrees
gbm_default_cv <- h2o.grid("gbm", x = x, y = y,
grid_id = "gbm_default_cv",
training_frame = h2_train,
nfolds = 5,
keep_cross_validation_predictions = T,
fold_assignment = "AUTO",
ntrees = n_trees,
seed = seed,
hyper_params = gbm_params_default)
gbm_default_cv_perf <- h2o.getGrid(grid_id = "gbm_default_cv",
sort_by = metric,
decreasing = FALSE)
model <- h2o.getModel(gbm_default_cv_perf@model_ids[[1]])
best_cv <- get_best_cv(model)
best_params <- get_best_params(model)
### Train model with the best parameters and ntrees using different random seeds
seeds_default <- c()
for (seed in seeds) {
set.seed(seed)
default_rmse_val <- validate_model(x, y, h2_train, h2_valid, best_params, best_cv$n_trees, seed)
seeds_default <- c(seeds_default, default_rmse_val)
}
seeds_default <- c(seeds_default, mean(seeds_default), sd(seeds_default))
seeds_default <- as.data.frame(seeds_default, row.names = c(sapply(seeds, as.character), "mean", "sd"))
rmse_val_mean <- seeds_default["mean",]
### Save results
result <- data.frame(unlist(best_params), stringsAsFactors = FALSE)
result <- rbind(result,
n_trees = best_cv$n_trees,
rmse_cv = best_cv$rmse_cv,
rmse_val = rmse_val_mean)
colnames(result) <- "Default"
write.table(result, file='result_default_enc.tsv', quote=FALSE, sep='\t')
write.table(seeds_default, file='result_default_enc_seeds.tsv', quote=FALSE, sep='\t')
cat("Default finished\n")
###
# Random hyperparameter search, random subspace
###
### CV to get the best ntrees
gbm_random_cv <- h2o.grid("gbm", x = x, y = y,
grid_id = "gbm_random_cv",
training_frame = h2_train,
search_criteria = gbm_search_criteria,
ntrees = n_trees,
nfolds = 5,
keep_cross_validation_predictions = T,
fold_assignment = "AUTO",
seed = seed,
hyper_params = gbm_params_random)
gbm_random_cv_perf <- h2o.getGrid(grid_id = "gbm_random_cv",
sort_by = metric,
decreasing = FALSE)
best_model <- get_best_model(gbm_random_cv_perf)
### Train model with the best parameters and ntrees using different random seeds
seeds_tuned <- c()
for (seed in seeds) {
set.seed(seed)
tuned_rmse_val <- validate_model(x, y, h2_train, h2_valid, best_model$best_params, best_model$best_cv$n_trees, seed)
seeds_tuned <- c(seeds_tuned, tuned_rmse_val)
}
seeds_tuned <- c(seeds_tuned, mean(seeds_tuned), sd(seeds_tuned))
seeds_tuned <- as.data.frame(seeds_tuned, row.names = c(sapply(seeds, as.character), "mean", "sd"))
rmse_val_mean <- seeds_tuned["mean",]
### Save results
result <- data.frame(unlist(best_model$best_params), stringsAsFactors = FALSE)
result <- rbind(result,
n_trees = best_model$best_cv$n_trees,
rmse_cv = best_model$best_cv$rmse_cv,
rmse_val = rmse_val_mean)
colnames(result) <- "Tuned"
write.table(result, file='result_tuned_enc.tsv', quote=FALSE, sep='\t')
write.table(seeds_tuned, file='result_tuned_enc_seeds.tsv', quote=FALSE, sep='\t')
cat("Tuned finished\n")