library(magrittr)
library(tidyverse)
library(furrr)

plan(multiprocess, workers = 8)


param_dr <- here::here("results","2019_05_28_clean_complete_data","model_parameters")
model_dr <- here::here("bin","2019_05_28_prepare_predictive_models","models")
data_dr <- here::here("results","2019_07_24_segregated_data")
cv_dr <- here::here("results","2019_05_28_clean_complete_data")

snp_dr <- here::here("results","2019_08_15_Emca4_interactions","datasets")

dir.create(snp_dr,showWarnings = FALSE)

out_dr <- here::here("results","2019_08_15_Emca4_interactions","predictions")

dir.create(out_dr,showWarnings = FALSE)
dir.create(file.path(out_dr,"logs"),showWarnings = FALSE)
dir.create(file.path(out_dr,"err"),showWarnings = FALSE)
dir.create(file.path(out_dr,"out"),showWarnings = FALSE)

params <- list.files(param_dr,full.names = TRUE)
models <- list.files(model_dr,full.names = TRUE)

models <- tibble(models)

### need to modify if we add more more models
models %<>% 
	mutate(
        params = case_when(
    		str_detect(models,"decision_trees") ~ "dont.rds",
            str_detect(models,"lasso") ~ str_subset(params,"lasso"), 
            str_detect(models,"ridge") ~ str_subset(params, "ridge"),
			str_detect(models,"elastic") ~ str_subset(params, "glmnet"),
            str_detect(models,"gbm") ~ str_subset(params,"gbm"),
            str_detect(models,"logistic") ~ "dont.rds",
            str_detect(models,"random_forest") ~ str_subset(params,"rf"),
            str_detect(models,"svm_linear") ~ str_subset(params,"svm_linear"),
            str_detect(models,"svm_poly") ~ str_subset(params,"svm_poly"),
            str_detect(models,"svm_radial") ~ str_subset(params,"svm_radial")
        )
    ) %>% 
    filter(!str_detect(models,"gbm")) %>% 
    filter(!str_detect(models,"svm")) %>% 
    filter(!str_detect(models,"logistic")) %>% 
    filter(!str_detect(models,"adaptive"))

models %<>% filter(str_detect(basename(models),"elastic"))


data_sets <- tibble(
    data_file = list.files(data_dr,full.names = TRUE,pattern ="rds")) %>% 
    mutate(
        phase = if_else(str_detect(data_file,"aim1"), "aim1","aim2"),
        phase_dr = file.path(cv_dr,paste0(phase,"_cv")))
        
data_sets %<>% filter(str_detect(basename(data_file),"aim1"))
data_sets %<>% filter(str_detect(basename(data_file),"geno"))

data_sets %<>% filter(str_detect(basename(data_file),"emca4"))

phase_dr <- data_sets$phase_dr %>% unique()

### add the couple of all_snps, all_snps_survey datasets
only_snps   <- here::here("results/2019_05_28_clean_complete_data/aim1_only_snps_geno.rds")
snps_survey <- here::here("results/2019_05_28_clean_complete_data/aim1_snps_survey_geno.rds")

data_sets %<>% add_row(data_file = only_snps,phase = "aim1",phase_dr)
data_sets %<>% add_row(data_file = snps_survey, phase = "aim1",phase_dr)

#### add the interactions:

emca4_list <- c("rs2935776","rs3870371","rs1562430","rs1456315","rs6470588","rs1264202","rs7014346","rs9792269","rs284489")

build_interactions <- function(data,snps)
{
	data_mat <- data %>% select(-person_id,-status) %>% select_if(is.numeric)
	data_mat <- as.matrix(data_mat)
	
	all_pairs <- t(combn(snps,2))
	nms <- apply(all_pairs, 1, FUN = function(x)str_c(x[1],x[2],sep = "_"))
	sp_all_pairs <- all_pairs %>%
		as.data.frame() %>% 
		{ split(.,seq_len(nrow(.)))}
	inter_list <- map(sp_all_pairs, ~ data_mat[,c(.$V1,.$V2)])		
	inter_list %<>% map(matrixStats::rowProds)
	
	interactions <- bind_cols(inter_list) %>% 
		as_tibble() %>% 
		set_colnames(nms)
	
	bind_cols(data,interactions) 
		
}

data_sets %<>% 
	mutate(
		data = future_map(data_file,readRDS),
		data_int = future_map(data,build_interactions,emca4_list))

data_sets %<>% 
	select(-data) %>% 
	mutate(
		data_file = basename(data_file) %>% str_replace(".rds","_inter.rds"),
		data_file = file.path(snp_dr,data_file),
		data_int = map2(data_int,data_file,saveRDS)) %>% 
	select(-data_int)

data_sets %<>% nest(-phase_dr,-phase)
data_sets %<>% 
	mutate(
        cv_files = map(phase_dr,list.files,full.names = TRUE),
        cv_files = map(cv_files , ~ tibble(cv_files = . )),
        all_files = map2(data,cv_files,crossing)
    )

data_sets %<>% select(phase,all_files) %>% unnest()

data_sets %<>% crossing(models)

data_sets %<>% select(models,data_file,cv_files,params)


data_sets %<>% mutate_all(list( aux = ~ basename(.) %>% str_remove(".rds") %>% 
	str_remove_all("_parameters") %>% str_remove_all(".R"))) %>% 
    mutate(
        outdir = paste(data_file_aux,cv_files_aux,models_aux,sep = "_")
    )

data_sets %<>% select(-contains("aux"))


data_sets %>% write_csv(file.path(out_dr,"2019_08_15_predictions_Emca4_inter_elastic_nets_params.csv"),
				col_names = FALSE)