def visualize_solutions_overview_second_batch():
from database import Task, TaskJobs, Config, Graph, get_session, CelestialGraph, AngularGraphSolution
from instance_evolver import GraphGenome
from utils import visualize_graph_2d, visualize_min_sum_sol_2d, visualize_solution_2d
from matplotlib import pyplot as plt
session = get_session("angular.db")
from utils.visualization import visualize_solution_scatter, VisTypes
min_sum_tasks = session.query(Task).filter(Task.parent_id == 45).all()
local_min_sum_tasks = session.query(Task).filter(
Task.parent_id == 40).all()
makespan_tasks = session.query(Task).filter(
Task.parent_id == 30, ~Task.name.like("%Reduced%")).all()
min_sum_jobs = session.query(TaskJobs).filter(
TaskJobs.task_id.in_([t.id for t in min_sum_tasks])).all()
local_min_sum_jobs = session.query(TaskJobs).filter(
TaskJobs.task_id.in_([t.id for t in local_min_sum_tasks])).all()
makespan_jobs = session.query(TaskJobs).filter(
TaskJobs.task_id.in_([t.id for t in makespan_tasks])).all()
#visualize_solution_scatter(min_sum_jobs, "Bla", vis_type=VisTypes.All, logscale=True, loc=9)
#visualize_solution_scatter(local_min_sum_jobs, "Bla", vis_type=VisTypes.All, logscale=True, loc=9)
visualize_solution_scatter(makespan_jobs,
"Bla",
vis_type=VisTypes.All,
logscale=True,
loc=9)
print("finished")
def general_instances_test():
from solver.x_split_solver import SplitXSolver
from solver import MscColoringSolver
from create_gen_instances_script import create_random_instance
from database import TaskJobs
solver_x = SplitXSolver()
solver_c = MscColoringSolver()
jobs = []
for i in tqdm.trange(7, 20, desc="Vertices"):
for j in tqdm.trange(3,10, desc="Edge probability"):
for k in tqdm.trange(3, desc="Repetition"):
g = create_random_instance(i, edge_chance=0.1*j)
g.id = i*100+j*10+k
sol_x = solver_x.solve(g)
sol_c = solver_c.solve(g)
try:
assert sol_x.makespan < max(get_lower_bounds(g)) * 5.5 * math.ceil(math.log2(g.vert_amount))
assert sol_c.makespan < max(get_lower_bounds(g)) * 5.5 * math.ceil(math.log2(g.vert_amount))
except AssertionError as e:
print("Solution x-split:", sol_x.makespan, max(get_lower_bounds(g)) * 5.5 * math.ceil(math.log2(g.vert_amount)), "time:", sol_x.runtime)
print("Solution color:", sol_c.makespan, max(get_lower_bounds(g)) * 5.5 * math.ceil(math.log2(g.vert_amount)), "time:", sol_x.runtime)
raise e
jobs.append(TaskJobs(task=None, solution=sol_x, graph=g))
jobs.append(TaskJobs(task=None, solution=sol_c, graph=g))
from utils.visualization import visualize_solution_scatter, VisTypes
visualize_solution_scatter(jobs, "Bla", vis_type=VisTypes.LB_Runtime, logscale=True, loc=9)
def visualize_geo_solutions_overview():
from database import Task, TaskJobs, Config, Graph, get_session, CelestialGraph, AngularGraphSolution
from utils.visualization import visualize_solution_scatter, VisTypes
session = get_session("angular.db")
min_sum_tasks = session.query(Task).filter(Task.parent_id.in_([80,107])).all()
local_min_sum_tasks = session.query(Task).filter(Task.parent_id.in_([89,109])).all() + session.query(Task).filter(Task.id == 82).all()
makespan_tasks = session.query(Task).filter(Task.parent_id.in_([97,111])).all()
min_sum_jobs = session.query(TaskJobs).filter(TaskJobs.task_id.in_([t.id for t in min_sum_tasks])).all()
local_min_sum_jobs = session.query(TaskJobs).filter(TaskJobs.task_id.in_([t.id for t in local_min_sum_tasks])).all()
makespan_jobs = session.query(TaskJobs).filter(TaskJobs.task_id.in_([t.id for t in makespan_tasks])).all()
#visualize_solution_scatter(min_sum_jobs, "Bla", vis_type=VisTypes.All, logscale=True, loc=9)
visualize_solution_scatter(local_min_sum_jobs, "Bla", vis_type=VisTypes.All, logscale=True, loc=9)
def bnb_sols():
from database import Task, TaskJobs, Config, Graph, get_session, CelestialGraph, AngularGraphSolution
from solver.bnb import MinSumAbstractGraphSolver, MinSumOrderSolver
from create_gen_instances_script import create_random_instance
import tqdm
import pickle
sols = None
try:
with open("bnb_sols.pk", "rb") as f:
bnb1sols, bnb2sols = pickle.load(f)
except (EOFError, FileNotFoundError):
pass
bnb1 = MinSumAbstractGraphSolver(time_limit=900)
bnb2 = MinSumOrderSolver(time_limit=900)
if not bnb1sols or not bnb2sols:
graphs = [
create_random_instance(n, edge_chance=e)
for e in np.arange(0.5, 1, 0.1) for n in range(5, 8)
for i in range(2)
]
bnb1 = MinSumAbstractGraphSolver(time_limit=900)
bnb2 = MinSumOrderSolver(time_limit=900)
bnb1sols = []
bnb2sols = []
for g in tqdm.tqdm(graphs):
bnb1sols.append(bnb1.solve(g))
bnb2sols.append(bnb2.solve(g))
sols = bnb1sols + bnb2sols
for i, (s1, s2) in enumerate(zip(bnb1sols, bnb2sols)):
s1.graph.id = i
s1.graph_id = 1
s2.graph.id = i
s2.graph_id = 1
with open("bnb_sols.pk", "wb") as f:
pickle.dump((bnb1sols, bnb2sols), f)
from utils.visualization import visualize_solution_scatter, VisTypes
visualize_solution_scatter(bnb1sols + bnb2sols,
"Branch and Bound runtimes",
solution_type="runtime",
vis_type=VisTypes.Absolute)
visualize_solution_scatter(bnb1sols + bnb2sols,
"Branch and Bound Vs Lower Bound",
solution_type="min_sum",
vis_type=VisTypes.VsLB)
def visualize_solutions_overview():
from database import Task, TaskJobs, Config, Graph, get_session, CelestialGraph, AngularGraphSolution
from instance_evolver import GraphGenome
from utils import visualize_graph_2d, visualize_min_sum_sol_2d, visualize_solution_2d
from matplotlib import pyplot as plt
session = get_session("angular.db")
from utils.visualization import visualize_solution_scatter, VisTypes
min_sum_tasks = session.query(Task).filter(Task.parent_id == 2).all()
local_min_sum_tasks = session.query(Task).filter(Task.parent_id == 11).all()
makespan_tasks = session.query(Task).filter(Task.parent_id == 19).all()
min_sum_jobs = session.query(TaskJobs).filter(TaskJobs.task_id.in_([t.id for t in min_sum_tasks])).all()
local_min_sum_jobs = session.query(TaskJobs).filter(TaskJobs.task_id.in_([t.id for t in local_min_sum_tasks])).all()
makespan_jobs = session.query(TaskJobs).filter(TaskJobs.task_id.in_([t.id for t in makespan_tasks])).all()
visualize_solution_scatter(min_sum_jobs, "Bla", vis_type=VisTypes.LB_Runtime, logscale=True, loc=9)
visualize_solution_scatter(local_min_sum_jobs, "Bla", vis_type=VisTypes.LB_Runtime, logscale=True, loc=9)
visualize_solution_scatter(makespan_jobs, "Bla", vis_type=VisTypes.LB_Runtime, logscale=True, loc=9)