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main_5.py
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main_5.py
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import numpy as np
import random
import pickle
import time
import datetime
import os
import subprocess
import utils
from jonas import Jonas
from danielle import Danielle
from n_split_GCSQ import n_split_GCSQ
from r_qubo import r_qubo
from GCSQ import GCSQ
from algorithm import IterativeQuantumAlgorithmWithK
from ours_iterative_exactly import ours_iterative_exactly
from ours_iterative_at_most import ours_iterative_at_most
from k_split_GCSQ_exactly import k_split_GCSQ_exactly
from k_split_GCSQ_at_most import k_split_GCSQ_at_most
from r_qubo_iterative import r_qubo_iterative
# generated by ChatGPT
def create_nested_directory(path):
try:
# Check if the path exists
if not os.path.exists(path):
# If not, create the missing directories
os.makedirs(path)
print(f"Created directory: {path}")
return True
except Exception as e:
print(f"Error creating directory: {e}")
return False
def create_data_synthetic_test(graph_sizes, num_graphs_per_size, seed, mean=0.5):
data = {}
for n in graph_sizes:
graphs = []
for graph_num in range(num_graphs_per_size):
graph = utils.generate_problem(n, mean=mean)
graphs.append(graph)
data[n] = graphs
utils.append_to_pickle(data, f"data/tests/data_{graph_sizes}_{num_graphs_per_size}_{seed}.pkl")
return data, True
def run_algorithm(serialized_algorithm, serialized_edges, num_agents, serialized_run_id, serialized_directory_path, graph_num):
try:
algorithm = pickle.loads(serialized_algorithm)
edges = pickle.loads(serialized_edges)
run_id = pickle.loads(serialized_run_id)
directory = pickle.loads(serialized_directory_path)
print(f" Running {algorithm.name} for seed {algorithm.seed} for graph_size {num_agents} for graph {graph_num} ...")
# based on code by Jonas Nüßlein
start_time = time.time()
coalitions = algorithm.solve(num_agents, edges, graph_num)
end_time = time.time()
value = np.sum([utils.value(c, edges) for c in coalitions])
total_time = end_time - start_time
algorithm.data = (coalitions, value, total_time)
done = (num_agents, graph_num)
utils.append_to_pickle(algorithm.data, f"{directory}/data_{algorithm.name}__{algorithm.seed}__{run_id}.pkl")
utils.append_to_pickle(done, f"{directory}/done_{algorithm.name}__{algorithm.seed}__{run_id}.pkl")
print(f" Coalition structure value for {algorithm.name}: {value} - Time: {total_time}")
except ValueError as e:
raise ValueError(f" Error (probably not enough logical qubits available): {str(e)}") from None
except np.core._exceptions._ArrayMemoryError as e:
raise Exception(f" Error (probably not enough memory available): {str(e)}") from None
except Exception as e:
# for e.g. "No embedding found" exception
raise Exception(f"{str(e)}") from None
def main(algorithm_list, data, graph_sizes, num_graphs_per_size, experiment, directory):
run_id = str(datetime.datetime.now().date()) + '_' + str(datetime.datetime.now().time()).replace(':', '-')
for algorithm in algorithm_list:
#too_large = False
if isinstance(algorithm, IterativeQuantumAlgorithmWithK):
if algorithm.k == 5:
agents = [20,22,24,26,28]
else:
agents = graph_sizes
for num_agents in agents:
time.sleep(10)
if algorithm.k <= num_agents:
print(f"\n\n\nTest for graphsize {num_agents}")
if algorithm.k == 5 and num_agents == 20:
this_range = range(11, num_graphs_per_size)
else:
this_range = range(num_graphs_per_size)
for graph_num in this_range:
time.sleep(1)
print(f"\n\n Graph {graph_num}")
graph = data[num_agents][graph_num]
if synthetic:
edges = graph
else:
edges = utils.transform(graph)
print(f"\n Running {algorithm.name}...")
# the following code (call of subprocess) is partially based on code generated by ChatGPT
try:
# Run algorithm in a subprocess to avoid a potential SIGKILL of the entire script
# due to too much memory usage of this algorithm
# Serialize the objects and strings to pass to sub-process
serialized_algorithm = pickle.dumps(algorithm)
serialized_graph = pickle.dumps(edges)
serialized_run_id = pickle.dumps(run_id)
serialized_directory = pickle.dumps(directory)
# subprocess command
command = [
"python",
"-c",
f"from main import run_algorithm; "
f"print(run_algorithm({serialized_algorithm}, {serialized_graph}, {num_agents}, {serialized_run_id}, {serialized_directory}, {graph_num}))"
]
subprocess.run(command, check=True)
except subprocess.CalledProcessError as e:
print(" Error: Running algorithm failed, most likely due to insufficient available memory. Error message: ", e)
#too_large = True
#break
#if too_large:
#break
#else:
#pass
else:
if isinstance(algorithm, Jonas):
agents = graph_sizes[:-4]
elif isinstance(algorithm, Danielle):
agents = graph_sizes[:-7]
else:
agents = graph_sizes[:-8]
for num_agents in agents:
print(f"\n\n\nTest for graphsize {num_agents}")
if num_agents == 20 or num_agents == 12 or num_agents == 14:
this_range = range(1, num_graphs_per_size)
else:
this_range = range(num_graphs_per_size)
for graph_num in this_range:
print(f"\n\n Graph {graph_num}")
graph = data[num_agents][graph_num]
if synthetic:
edges = graph
else:
edges = utils.transform(graph)
print(f"\n Running {algorithm.name}...")
# the following code (call of subprocess) is partially based on code generated by ChatGPT
try:
# Run algorithm in a subprocess to avoid a potential SIGKILL of the entire script
# due to too much memory usage of this algorithm
# Serialize the objects and strings to pass to sub-process
serialized_algorithm = pickle.dumps(algorithm)
serialized_graph = pickle.dumps(edges)
serialized_run_id = pickle.dumps(run_id)
serialized_directory = pickle.dumps(directory)
# subprocess command
command = [
"python",
"-c",
f"from main import run_algorithm; "
f"print(run_algorithm({serialized_algorithm}, {serialized_graph}, {num_agents}, {serialized_run_id}, {serialized_directory}, {graph_num}))"
]
subprocess.run(command, check=True)
except subprocess.CalledProcessError as e:
print(" Error: Running algorithm failed, most likely due to insufficient available memory. Error message: ", e)
#too_large = True
#break
#if too_large:
#break
print(f"Done running tests for {experiment}.")
if __name__ == "__main__":
# TODO: Determine sensible number of seeds for statistical significance
num_seeds = 1
for _ in range(num_seeds):
# Setting the seed
#seed = random.randint(0, 2 ** 32 - 1)
seed = 0 # Seed not relevant for D-Wave
random.seed(seed)
np.random.seed(seed)
print(f"Seed: {seed}")
# loading E.ON data
data, synthetic = pickle.load(open('data/data_new_20samples_4_28.pkl', 'rb')), False
# alternative: load synthetic data
# data, synthetic = create_data_synthetic_test([10, 15, 20], 25, seed)
if synthetic:
data_name = "synthetic"
else:
data_name = "eon_data"
graph_sizes = list(data.keys()) # is [4,6,8,10,12,14,16,18,20,22,24,26,28] for E.ON data
num_graphs_per_size = len(data[graph_sizes[0]]) # is 20 for E.ON data
num_graph_sizes = len(graph_sizes)
# Simulate
'''
solvers = ["qbsolv", "qaoa"]
parallel = [True] #, False] -> Try sequential later (maybe)
k_list = [i for i in range(3, graph_sizes[-1])]
'''
# D-Wave -> uncomment this and comment simulate for running with D-Wave
solvers = ["dwave"]
parallel = [True]
k_list = [5, 2]
for solver in solvers:
'''
algorithm_list = [Jonas(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver),
Danielle(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver),
##n_split_GCSQ(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver),
r_qubo(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver)
]
directory = f"results/{data_name}/quantum/{solver}"
directory_exists = create_nested_directory(directory)
if directory_exists:
main(algorithm_list=algorithm_list, data=data, graph_sizes=graph_sizes, num_graphs_per_size=num_graphs_per_size,
experiment=f"non-iterative algorithms with {solver}", directory=directory)
'''
for mode in parallel:
'''
algorithm_list = [GCSQ(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver, parallel=mode)]
directory = f"results/{data_name}/quantum/{solver}/{'parallel' if mode else 'sequential'}"
directory_exists = create_nested_directory(directory)
if directory_exists:
main(algorithm_list=algorithm_list, data=data, graph_sizes=graph_sizes,
num_graphs_per_size=num_graphs_per_size, experiment=f"GCS-Q with {solver} in {mode} mode",
directory=directory)
'''
for k in k_list:
algorithm_list = [#ours_iterative_exactly(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver, parallel=mode, k=k),
ours_iterative_at_most(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver, parallel=mode, k=k),
#k_split_GCSQ_exactly(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver, parallel=mode, k=k),
## k_split_GCSQ_at_most(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver, parallel=mode, k=k),
#r_qubo_iterative(seed=seed, num_graph_sizes=num_graph_sizes, solver=solver, parallel=mode, k=k)
]
directory = f"results/{data_name}/quantum/{solver}/{'parallel' if mode else 'sequential'}/k={k}"
directory_exists = create_nested_directory(directory)
if directory_exists:
main(algorithm_list=algorithm_list, data=data, graph_sizes=graph_sizes,
num_graphs_per_size=num_graphs_per_size, experiment=f"k-split algorithms with {solver} in {mode} mode for k={k}",
directory=directory)
print("Taking 10 Minute break...")
time.sleep(600)
# Classical algorithms
'''
algorithm_list = [] # TODO
directory = f"results/{data_name}/classical"
directory_exists = create_nested_directory(directory)
if directory_exists:
main(algorithm_list=algorithm_list, data=data, graph_sizes=graph_sizes,
num_graphs_per_size=num_graphs_per_size,
experiment=f"classical algorithms",
directory=directory)
'''