Add target construction and training script for DeepHealth model

- Implemented target construction in `targets.py` for next-token and unique-time set supervision.
- Added validation functions and utility methods for target building.
- Created a comprehensive training script in `train.py` that includes data loading, model building, optimizer setup, and training loop with early stopping and logging.
- Integrated loss functions and readout mechanisms based on target modes.
- Established dataset splitting and DataLoader configurations for training, validation, and testing.

prepare_data.R

@@ -0,0 +1,26 @@
+library(data.table)
+setDTthreads(40)
+library(readr)
+
+field_id <- read.csv("field_ids_enriched.csv", header = TRUE, stringsAsFactors = FALSE)
+uid <- unique(c("eid", field_id$field_instance))
+
+big_path   <- "/mnt/storage/shared_data/UKBB/20230518-from-zhourong/HHdata_221103_0512.csv"
+header_dt  <- fread(big_path, nrows = 0)     # Read 0 rows => only column names
+all_names  <- names(header_dt)
+keep_names <- intersect(all_names,uid)
+ukb_disease <- fread(big_path,
+                 select     = keep_names,
+                 showProgress = TRUE)
+
+big_path <- "/mnt/storage/shared_data/UKBB/20230518-from-zhourong/HH_data_220812_0512.csv"
+header_dt  <- fread(big_path, nrows = 0)     # Read 0 rows => only column names
+all_names  <- names(header_dt)
+keep_names <- intersect(all_names,uid)
+ukb_others <- fread(big_path,
+                 select     = keep_names,
+                 showProgress = TRUE)
+
+# merge disease and other data by "eid"
+ukb_data <- merge(ukb_disease, ukb_others, by = "eid", all = TRUE)
+fwrite(ukb_data, "ukb_data.csv")