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https://github.com/ferencberes/ethp2psim.git
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readding former script
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Ferenc Beres
243
scripts/compare_protocols.py
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243
scripts/compare_protocols.py
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import argparse, json
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from datetime import datetime as dt
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from ethp2psim.network import *
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from ethp2psim.protocols import *
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from ethp2psim.adversary import *
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from ethp2psim.simulator import *
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from ethp2psim.data import GoerliTestnet
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from ethp2psim.experiments import run_and_eval, run_experiment
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def get_adversary_for_protocol(
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protocol: Protocol, is_active: bool, nodes: list, seed: int = None
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) -> Adversary:
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"""
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Generate adversary with respect to the given parameters
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Parameters
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----------
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protocol : protocol
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The protocol that you intend to break with an adversary
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is_active : bool
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Set whether the adversary is allowed to refuse to propagate any message
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nodes : list
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List of adversarial node IDs
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seed : int
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Random seed for the experiment
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"""
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if isinstance(protocol, DandelionProtocol):
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adv = DandelionAdversary(
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protocol,
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active=is_active,
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adversaries=nodes,
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seed=seed,
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)
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elif isinstance(protocol, OnionRoutingProtocol):
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adv = OnionRoutingAdversary(
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protocol,
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active=is_active,
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adversaries=nodes,
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seed=seed,
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)
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else:
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adv = Adversary(
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protocol,
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active=is_active,
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adversaries=nodes,
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seed=seed,
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)
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return adv
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def run_single_experiment(config: dict) -> list:
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seed = np.random.randint(10**5)
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nw_generator = NodeWeightGenerator(config["nw_gen_mode"])
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ew_generator = EdgeWeightGenerator(config["ew_gen_mode"])
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if config["network_size"] == 0:
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G = GoerliTestnet().graph
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net = Network(nw_generator, ew_generator, graph=G, seed=seed)
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num_msg = int(G.number_of_nodes() * config["msg_fraction"])
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else:
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net = Network(
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nw_generator,
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ew_generator,
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config["network_size"],
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config["degree"],
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seed=seed,
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)
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num_msg = int(config["network_size"] * config["msg_fraction"])
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protocols = []
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# decide whether to execute the simple BroadcastProtocol
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if not config["exclude_simple_broadcast"]:
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protocols.append(
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BroadcastProtocol(net, broadcast_mode=config["broadcast_mode"], seed=seed)
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)
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# add Dandelion(++) instances to the experiment if the related parameters were defined
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for spreading_proba in config["dandelion_spreading_probas"]:
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protocols.append(
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DandelionProtocol(
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net, spreading_proba, broadcast_mode=config["broadcast_mode"], seed=seed
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)
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)
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protocols.append(
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DandelionPlusPlusProtocol(
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net, spreading_proba, broadcast_mode=config["broadcast_mode"], seed=seed
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)
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)
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# add OnionRoutingProtocol instances to the experiment if the related parameters were defined
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for num_relayers in config["onion_routing_num_relayers"]:
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protocols.append(
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OnionRoutingProtocol(
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net, num_relayers, broadcast_mode=config["broadcast_mode"], seed=seed
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)
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)
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# use the same set of adversarial nodes for all protocols
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num_adv_nodes = int(net.num_nodes * config["adversary_ratio"])
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if config["adversary_centrality_metric"] != "none":
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adv_nodes = net.get_central_nodes(
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num_adv_nodes, config["adversary_centrality_metric"]
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)
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else:
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adv_nodes = net.sample_random_nodes(num_adv_nodes, False)
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single_run_results = []
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for protocol in protocols:
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# generate adversary with respect to the protocol
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adv = get_adversary_for_protocol(
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protocol, config["active_adversary"], adv_nodes, seed
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)
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# by fixing the seed we sample the same messages
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sim = Simulator(adv, num_msg, seed=seed, verbose=False)
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# print()
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# print(sim.messages)
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new_reports = run_and_eval(sim)
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single_run_results += new_reports
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return single_run_results
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parser = argparse.ArgumentParser(
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description="Script for experimenting with baseline protocols (Broadcast, Dandelion, Dandelion++)"
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)
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parser.add_argument(
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"--network_size",
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type=int,
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default=500,
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help="P2P network size (Default: 500). If you specify 0 then Goerli Testnet is used.",
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)
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parser.add_argument(
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"--degree",
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type=int,
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default=50,
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help="P2P network degree in the random regular graph model (Default: 50)",
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)
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parser.add_argument(
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"--ew_gen_mode",
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type=str,
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choices=["random", "normal"],
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default="normal",
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help="EdgeWeightGenerator mode (Default: normal)",
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)
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parser.add_argument(
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"--nw_gen_mode",
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type=str,
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choices=["random", "stake"],
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default="random",
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help="NodeWeightGenerator mode (Default: random)",
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)
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parser.add_argument(
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"--msg_fraction",
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type=float,
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default=0.1,
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help="Fraction of nodes that send message in one simulation (Default: 0.1)",
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)
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parser.add_argument(
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"--broadcast_mode",
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type=str,
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choices=["sqrt", "all"],
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default="sqrt",
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help="Define whether a node sends message to all neighbors or just a square root amount of them (Default: sqrt)",
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)
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parser.add_argument(
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"--exclude_simple_broadcast",
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action="store_true",
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help="Exclude the simple BroadcastProtocol from the experiments. By default it is always executed.",
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)
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parser.add_argument(
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"--dandelion_spreading_probas",
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nargs="+",
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type=float,
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default=[],
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help="Broadcast probabilities for Dandelion(++) (Default: [] - Dandelion is not executed)",
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)
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parser.add_argument(
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"--onion_routing_num_relayers",
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nargs="+",
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type=int,
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default=[],
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help="Number of relayers in OnionRoutingProtocol (Default: [] - OnionRoutingProtocol is not executed)",
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)
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parser.add_argument(
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"--adversary_ratios",
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nargs="+",
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type=float,
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default=[0.1],
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help="Adversary ratios for the experiment (Default: [0.1])",
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)
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parser.add_argument(
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"--active_adversary",
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action="store_true",
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help="Set to use active adversary in the experiment",
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)
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parser.add_argument(
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"--adversary_centrality_metric",
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type=str,
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choices=["degree", "betweenness", "pagerank", "none"],
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default="none",
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help="Set metric to choose top central nodes to be adversaries.",
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)
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parser.add_argument(
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"--num_trials", type=int, default=1, help="Number of trials (Default: 1)"
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)
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parser.add_argument(
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"--output_file_prefix",
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type=str,
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default=None,
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help="Specify output file prefix. Otherwise the timestamp will be used.",
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)
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if __name__ == "__main__":
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# load arguments
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args = parser.parse_args()
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adversary_ratios = args.adversary_ratios
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num_trials = args.num_trials
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max_threads = 1
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if args.output_file_prefix != None:
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output_file_prefix = args.output_file_prefix
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else:
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output_file_prefix = dt.now().strftime("%Y-%m-%d_%H:%M:%S")
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# save config file
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config = vars(args)
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with open("%s.json" % output_file_prefix, "w") as f:
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f.write(json.dumps(config))
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# prepare queries
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queries = []
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for adv_ratio in adversary_ratios * num_trials:
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query = config.copy()
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query["adversary_ratio"] = adv_ratio
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queries.append(query)
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# print(query)
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print(len(queries))
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# run experiment
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results_df = run_experiment(run_single_experiment, queries, max_threads)
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# save results
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results_df.to_csv("%s.csv" % output_file_prefix, index=False)
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