import numpy as np
from numpy import genfromtxt
import csv
import pandas
from pathlib import Path
import argparse
	
def sample_single_act(pred_path, save_path, j):
	data = pandas.read_csv(pred_path).values
	total_data = []

	for u in range(1,6):
		act_data = data[data[:,1]==u]
		final_save_path = save_path + "/rate_" + str(j) + "_act_" + str(int(u)) + "_pred.csv"
		head = []
		for r in range(7):
			head.append('act'+str(r+1))
		head.append('task_name')
		head.append('gt')
		head.insert(0,'action_id')
		pandas.DataFrame(act_data[:,1:]).to_csv(final_save_path, header=head)
			
	
def main():
	# parsing parameters
	parser = argparse.ArgumentParser(description='')
	parser.add_argument('--batch_size', type=int, default=8, help='batch size')
	parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
	parser.add_argument('--hidden_size', type=int, default=64, help='hidden_size')
	parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')

	args = parser.parse_args()	
	
	task = np.arange(7)
	user_num = 5
	bs = args.batch_size
	lr = args.lr # 1e-4
	hs = args.hidden_size #128
	model_type = args.model_type #'lstmlast'

	rate = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

	for i in task:
		for j in rate:		
			for l in range(user_num):
				pred_path = "prediction/task" + str(i) + "/" +  model_type + "_bs_" + str(bs) + "_lr_" + str(lr) + "_hidden_size_" + str(hs) + "/user" + str(l) + "_rate_" + str(j) + "_pred.csv"
				if j == 100:
					pred_path = "prediction/task" + str(i) + "/" +  model_type + "_bs_" + str(bs) + "_lr_" + str(lr) + "_hidden_size_" + str(hs) + "/user" + str(l) + "_pred.csv"		
				save_path = "prediction/single_act/task" + str(i) + "/" +  model_type + "_bs_" + str(bs) + "_lr_" + str(lr) + "_hidden_size_" + str(hs) + "/user" + str(l)
				Path(save_path).mkdir(parents=True, exist_ok=True)
				data = sample_single_act(pred_path, save_path, j)

if __name__ == '__main__':
	# split the prediction by action sequence id, from 10% to 90%
	main()