run tests in parallel. (2186835b) · Commits · LARN / PRRT

prrt/hec_search.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -84,6 +84,8 @@ class HECSearch:
		n_c * req_delay) / self.prrtSystemParameters.source_packet_interval)

		def get_k_lim(self, start, end):
		if end - start <= 1:
		return end
		mid_point = math.ceil((end + start) / 2)
		p_r = np.around(self.residual_packet_erasure_rate(mid_point, self.n_max, self.prrtChannelParameters.loss_rate_fwd),
		8) # Pr(k, n_max)

prrt/restricted_integer_composition.pyx

+3 −2

Original line number	Diff line number	Diff line
		@@ -40,8 +40,9 @@ def gen_repair_schedules(redundancy, positions, min, max, is_order_ascending):

		def gen_repair_schedule(redundancy, positions, min, max):
		if min > max:
		raise Exception(
		"Illegal input combinations. Make sure the min > max. And, number of total redundancy is greater that positions*min.")
		return []
		# raise Exception(
		# "Illegal input combinations. Make sure the min < max.")
		if positions == 0:
		return [redundancy]
		opt_schedule = [min for p in range(positions)]

prrt/search_evaluation.py

+63 −21

Original line number	Diff line number	Diff line
		import pandas as pd
		import hec_search
		import os
		import prrt
		import time
		import hec_search
		import numpy as np
		import pandas as pd
		from multiprocessing import Pool

		dataset_file_path = 'documents/in_12_param_4_sz_mini_1000.csv'
		ds_basename = os.listdir("../../hecps/code/ML/data/")
		# ds_basename = ["in_12_param_4_sz_zzatql"]
		ds_rel_input_path = "../../hecps/code/ML/data/"
		ds_rel_result_path = "documents/result/"
		#['documents/1','documents/2','documents/3','documents/4','documents/5','documents/6']


		def get_n_p_max(rtt_prop_fwd, pkt_length, data_rate_btl_fwd):
		@@ -14,34 +20,70 @@ def get_n_p_max(rtt_prop_fwd, pkt_length, data_rate_btl_fwd):
		def evaluate(searchAlgorithm, appParams, channelParams, systemParams):
		n_p_min = 1
		n_p_max = get_n_p_max(channelParams.rtt_prop_fwd, appParams.pkt_length, channelParams.data_rate_btl_fwd)

		if n_p_min <= n_p_max:
		start = time.time()
		search = hec_search.HECSearch(searchAlgorithm, n_p_min, n_p_max, appParams, channelParams, systemParams)
		search_result = search.search()
		duration = time.time() - start

		return [search_result, duration]
		else:
		return []

		def test_case():
		def test_case(dataset_basename):
		counter = 0
		save_result_to = pd.DataFrame()
		# Load dataset in chunk
		for df_in_chunk in pd.read_csv(dataset_file_path, sep=',', chunksize=1000):
		for df_in_chunk in pd.read_csv(ds_rel_input_path + dataset_basename, sep=',', chunksize=100):
		for index, row in df_in_chunk.iterrows():
		appParams = prrt.PrrtApplicationParameters(row['app_max_latency'], row['app_max_residual_loss_rate'], row['app_data_rate'], row['app_pkt_length'])
		chnlParams = prrt.PrrtChannelParameters(row['ch_loss_rate'], 0, row['ch_rtt_prop_fwd'], 0, row['ch_data_rate_btl_fwd'], 0)
		sysParams = prrt.PrrtSystemParameters(row['sys_block_coding_dly'], row['sys_red_pkt_trans_dly'], row['sys_proc_dly'], row['sys_pkt_loss_detection_dly'], row['sys_src_pkt_interval'])
		if 970 > index > 900:
		for searchAlgorithm in ["GreedySearch"]:

		for searchAlgorithm in ["GreedySearch", "FullSearch"]:
		print(str(index))
		result = evaluate(searchAlgorithm, appParams, chnlParams, sysParams)
		config = result[0]
		save_result_to = save_result_to.append({'search': searchAlgorithm, 'config': [config.k, len(config.n_p), config.n_p], 'duration' : np.around(result[1],0)}, ignore_index=True, sort=False)
		search_result = evaluate(searchAlgorithm, appParams, chnlParams, sysParams)
		if len(search_result) != 0:
		config = search_result[0]
		save_result_to = save_result_to.append({'app_max_latency': row['app_max_latency'],
		'app_max_residual_loss_rate': row['app_max_residual_loss_rate'],
		'app_data_rate' : row['app_data_rate'],
		'app_pkt_length' : row['app_pkt_length'],
		'ch_loss_rate' : row['ch_loss_rate'],
		'ch_rtt_prop_fwd' : row['ch_rtt_prop_fwd'],
		'ch_data_rate_btl_fwd' : row['ch_data_rate_btl_fwd'],
		'sys_block_coding_dly' : row['sys_block_coding_dly'],
		'sys_red_pkt_trans_dly' : row['sys_red_pkt_trans_dly'],
		'sys_proc_dly' : row['sys_proc_dly'],
		'sys_pkt_loss_detection_dly' : row['sys_pkt_loss_detection_dly'],
		'sys_src_pkt_interval' : row['sys_src_pkt_interval'],
		'search': searchAlgorithm,
		'config': [config.k, len(config.n_p), config.n_p],
		'duration' : np.around(search_result[1],0)}, ignore_index=True, sort=False)
		else:
		save_result_to = save_result_to.append({'app_max_latency': row['app_max_latency'],
		'app_max_residual_loss_rate': row['app_max_residual_loss_rate'],
		'app_data_rate' : row['app_data_rate'],
		'app_pkt_length' : row['app_pkt_length'],
		'ch_loss_rate' : row['ch_loss_rate'],
		'ch_rtt_prop_fwd' : row['ch_rtt_prop_fwd'],
		'ch_data_rate_btl_fwd' : row['ch_data_rate_btl_fwd'],
		'sys_block_coding_dly' : row['sys_block_coding_dly'],
		'sys_red_pkt_trans_dly' : row['sys_red_pkt_trans_dly'],
		'sys_proc_dly' : row['sys_proc_dly'],
		'sys_pkt_loss_detection_dly' : row['sys_pkt_loss_detection_dly'],
		'sys_src_pkt_interval' : row['sys_src_pkt_interval'],
		'search': searchAlgorithm,
		'config': ["INV_PRM_NPM"],
		'duration' : 0}, ignore_index=True, sort=False)


		counter += 1
		print("Chunk round: " + str(counter))
		save_result_to.to_csv('result.csv', sep=',', index = False)
		#print("Chunk round: " + str(counter))
		save_result_to.to_csv(ds_rel_result_path + dataset_basename, sep=',', index = False)

		test_case()

		#test_case("documents/5")
		if __name__ == '__main__':
		pool = Pool(processes=8)
		pool.map(test_case, ds_basename, chunksize=1)