def sa(self):
print("Creating SA curve")
problem, init_state = self.get_prob(t_pct=0.15)
plt.close()
plt.figure()
for schedule, s_str in zip(
[mlrose.GeomDecay(),
mlrose.ArithDecay(),
mlrose.ExpDecay()], ['GeomDecay', 'ArithDecay', 'ExpDecay']):
_, _, fitness_curve = mlrose.simulated_annealing(
problem,
schedule=schedule,
max_attempts=100,
max_iters=5000,
init_state=init_state,
curve=True)
plt.plot(fitness_curve, label="schedule={}".format(s_str))
plt.title("{}: Simulated Annealing".format(self.prob_name))
plt.legend(loc="best")
plt.xlabel('Number of Iterations')
plt.ylabel('Fitness')
plt.savefig(
os.path.join(self.output_path,
"{}_SA Analysis.png".format(self.prob_name)))
def queens_problem(max_attempts, max_iters):
fitness_cust = mlrose.CustomFitness(queens_max)
problem = mlrose.DiscreteOpt(length=8,
fitness_fn=fitness,
maximize=False,
max_val=8)
#problem = mlrose.DiscreteOpt(length=8, fitness_fn=queens_max, maximize=True, max_val=8)
# Define decay schedule
schedule = mlrose.ExpDecay()
# Define initial state
init_state = np.array([0, 1, 2, 3, 4, 5, 6, 7])
# Solve problem using simulated annealing
best_state, best_fitness = mlrose.simulated_annealing(
problem,
schedule=schedule,
max_attempts=max_attempts,
max_iters=max_iters,
init_state=init_state,
random_state=1)
print(best_state)
print(best_fitness)
def sim_anneal(prob, **kwargs):
start = time.time()
_, best_score, curve, fit_evals = mlrose.simulated_annealing(prob(),
curve=True,
**kwargs)
end = time.time()
return np.array([best_score, len(curve), fit_evals, end - start])
def max_iterations(n,cords,fit,temp,mint):
best_fit1=[]
best_fit2=[]
best_fit3=[]
max_iter=[]
for i in range(100,n,10):
best_state,best_fitness1 = mlrose.genetic_alg(problem_fit, mutation_prob = 0.2, max_attempts = 100,max_iters=i)
best_state,best_fitness2 = mlrose.mimic(problem_fit,pop_size=200,keep_pct=0.3,max_attempts=10,max_iters=i)
best_state,best_fitness3 = mlrose.simulated_annealing(problem_fit,schedule=mlrose.GeomDecay(init_temp=temp,decay=0.9,min_temp=mint),max_attempts=100,max_iters=i,init_state=None)
max_iter.append(i)
best_fit1.append(best_fitness1)
best_fit2.append(best_fitness2)
best_fit3.append(best_fitness3)
print(best_fit1,best_fit2,best_fit3)
max_iter=np.asarray(max_iter)
best_fit1=np.asarray(best_fit1)
best_fit2=np.asarray(best_fit2)
best_fit3=np.asarray(best_fit3)
line1, = plt.plot(max_iter,best_fit1,color='r',label='fitness_score')
line2, = plt.plot(max_iter,best_fit2,color='g',label='fitness_score')
line3, = plt.plot(max_iter,best_fit3,color='b',label='fitness_score')
plt.ylabel('Fitness_score')
plt.xlabel('Number of iterations')
plt.show()
return None
def run_sa(prob, value_range, num_runs):
avgs = []
times = []
for value in value_range:
problem = prob(value)
print("\t\tValue: " + str(value))
run_vals = []
run_times = []
for run in range(0, num_runs):
print("\t\t\tRun " + str(run))
start = timeit.default_timer()
best_state, best_fitness = mlrose.simulated_annealing(
problem, max_attempts=20)
stop = timeit.default_timer()
total_time = stop - start
run_vals.append(best_fitness)
run_times.append(total_time)
avgs.append(np.mean(run_vals))
times.append(np.mean(run_times))
return avgs, times
def simulated_annealing_max_attempts(problem, initial_state,
attempts_percentage):
attempts = int(problem.length * attempts_percentage)
return mlrose.simulated_annealing(problem,
init_state=initial_state,
curve=False,
max_attempts=attempts)
def find_best_param_sa_decay(seed, problem):
init_temp_String = []
init_temp = [0.1, 0.2, 0.3, 0.5, 0.66, 0.99]
for int in init_temp:
init_temp_String.append(str(int))
fitness_list = []
iterations = [
1, 250, 500, 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000
]
score = []
for init in init_temp:
for iter in iterations:
schedule2 = mlrose.GeomDecay(init_temp=500, decay=init)
best_state, best_fitness = mlrose.simulated_annealing(
problem,
max_attempts=10,
max_iters=iter,
random_state=seed,
schedule=schedule2)
score.append(best_fitness)
fitness_list.append(np.mean(score))
print("when init = ", init, " the mean score is ", np.mean(score))
sa_parameter_curve(init_temp_String, fitness_list, "sa_dacay", 0.4, "TSP")
def main():
name_of_exp = "Eight Queens"
fitness = mlrose.CustomFitness(queens_max)
problem = mlrose.DiscreteOpt(length=8,
fitness_fn=fitness,
maximize=True,
max_val=8)
# Define initial state
mimic = []
init_state = np.array([0, 1, 2, 3, 4, 5, 6, 7])
for i in [mlrose.ExpDecay(), mlrose.GeomDecay(), mlrose.ArithDecay()]:
best_state, best_fitness, learning_curve, timing_curve = mlrose.simulated_annealing(
problem,
init_state=init_state,
schedule=i,
max_attempts=1000,
max_iters=1000,
curve=True,
random_state=1)
mimic.append(learning_curve)
print(i)
print(best_fitness)
for x, z in zip(['Exp', 'Geom', 'Arith'], mimic):
plt.plot(z, label=str(x))
plt.legend()
plt.title(
'SA Randomized Optimization DecaySchedule vs Fitness Curve (8-Queens)')
plt.xlabel('Function iteration count')
plt.ylabel('Fitness function value')
plt.show()
def runComplexityAnnealing(pType, problem):
if pType == 'One Max':
neighbor = 10
iterations = 400
elif pType == 'Flip Flop':
neighbor = 36
iterations = 700
else:
neighbor = 4
iterations = 1000
s = time()
best_state, best_fitness = mlrose.simulated_annealing(
problem,
schedule=mlrose.ExpDecay(),
max_attempts=neighbor,
max_iters=iterations,
random_state=1)
# best_state, best_fitness, c = mlrose.simulated_annealing(problem, schedule=mlrose.ExpDecay(),
# max_attempts=neighbor,
# max_iters=iterations,
# random_state=1)
timeTaken = time() - s
return best_fitness, timeTaken
def run_simulated_annealing(init_state_problem):
initial_state, problem = init_state_problem
return mlrose.simulated_annealing(problem,
init_state=initial_state,
max_attempts=int(0.5 *
problem.length),
curve=True)
def test_simulated_annealing_discrete_max():
"""Test simulated_annealing function for a discrete maximization
problem"""
problem = DiscreteOpt(5, OneMax(), maximize=True)
_, _, curve = simulated_annealing(problem,
timing=True,
max_attempts=50)
assert (curve.shape[1] == 2)
def SimulatedAnnealing(self):
start = time.time()
KSbest_state, KSbest_fitness, KScurve = simulated_annealing(
self.knapsack, curve=True)
saKST = time.time() - start
start = time.time()
FPbest_state, FPbest_fitness, FPcurve = simulated_annealing(
self.fourpeaks, curve=True)
saFPT = time.time() - start
start = time.time()
CObest_state, CObest_fitness, COcurve = simulated_annealing(
self.countones,
schedule=GeomDecay(init_temp=.01, decay=.99),
curve=True)
saCOT = time.time() - start
return KSbest_fitness, FPbest_fitness, CObest_fitness, saKST, saFPT, saCOT, KScurve, FPcurve, COcurve
def fit(length, fitness):
problem = mlrose.DiscreteOpt(length = length, fitness_fn = fitness, maximize = True, max_val = 2)
iterations = [10,50,100,200,400,800,1600,3200]
RHC, SA, GA, MM = ([],[],[],[])
time_RHC, time_SA, time_GA, time_MM = ([],[],[],[])
for iter in iterations:
print ("max iterations = " + str(iter))
start_time = time.time()
best_fitness = 0
for times in range(10):
best_state, best_fitness = mlrose.random_hill_climb(problem, max_attempts = 10, max_iters = iter, restarts = 0, init_state = np.random.randint(2, size=(length,)))
best_fitness = max(best_fitness, best_fitness)
#print(best_state)
RHC.append(best_fitness)
print(best_fitness)
time_RHC.append((time.time() - start_time)/10)
start_time = time.time()
best_fitness = 0
for times in range(10):
best_state, best_fitness = mlrose.simulated_annealing(problem, schedule = mlrose.GeomDecay(), max_attempts = 10, max_iters = iter, init_state = np.random.randint(2, size=(length,)))
best_fitness = max(best_fitness, best_fitness)
#print(best_state)
SA.append(best_fitness)
print(best_fitness)
time_SA.append((time.time() - start_time)/10)
start_time = time.time()
best_fitness = 0
best_state, best_fitness = mlrose.genetic_alg(problem, pop_size = 200, mutation_prob = 0.1, max_attempts = 10, max_iters = iter)
#print(best_state)
GA.append(best_fitness)
print(best_fitness)
time_GA.append((time.time() - start_time))
start_time = time.time()
best_fitness = 0
best_state, best_fitness = mlrose.mimic(problem, pop_size = 200, keep_pct = 0.2, max_attempts = 10, max_iters = iter)
#print(best_state)
MM.append(best_fitness)
print(best_fitness)
time_MM.append((time.time() - start_time))
plot(RHC, SA, GA, MM, time_RHC, time_SA, time_GA, time_MM, iterations)
filewrite_array("iterations:", iterations)
filewrite_array("Fitness(RHC):", RHC)
filewrite_array("Fitness(SA):", SA)
filewrite_array("Fitness(GA):", GA)
filewrite_array("Fitness(MM):", MM)
filewrite_array("Fitness(time_RHC):", time_RHC)
filewrite_array("Fitness(time_SA):", time_SA)
filewrite_array("Fitness(time_GA):", time_GA)
filewrite_array("Fitness(time_MM):", time_MM)
def simulated_annealing_decay(problem, initial_state, decay):
if decay == 'Geom':
schedule = mlrose.GeomDecay()
if decay == 'Exp':
schedule = mlrose.ExpDecay()
if decay == 'Arith':
schedule = mlrose.ArithDecay()
return mlrose.simulated_annealing(problem,
init_state=initial_state,
curve=False,
schedule=schedule)
def test_simulated_annealing_continuous_min():
"""Test simulated_annealing function for a continuous minimization
problem"""
problem = ContinuousOpt(5, OneMax(), maximize=False)
best_state, best_fitness, _ = simulated_annealing(problem,
max_attempts=50)
x = np.array([0, 0, 0, 0, 0])
assert (np.array_equal(best_state, x) and best_fitness == 0)
def test_mimic_curve_length_max_attempts():
"""Test random_hill_climb function such that when curve is True for ma_iters
the length of all fitness scores should be equal to max_iters"""
problem = DiscreteOpt(5, OneMax(), maximize=True)
max_attempts = 10
best_state, best_fitness, all_fitness = simulated_annealing(
problem, max_attempts=max_attempts, curve=True)
assert len(all_fitness) != max_attempts
def simulated_annealing(problem, starting_cell):
# mlrose requires indices start from zero
# mlrose also requires init_state to be a 1D np array
#init_cell = np.array([starting_cell-1])
optimized_cells, _ = mlrose.simulated_annealing(problem)
# Add 1 to all cells since in our case, cell numbers start from one not zero!
optimized_cells += 1
return list(optimized_cells)
def test_simulated_annealing_discrete_max():
"""Test simulated_annealing function for a discrete maximization
problem"""
problem = DiscreteOpt(5, OneMax(), maximize=True)
best_state, best_fitness, _ = simulated_annealing(problem,
max_attempts=50)
x = np.array([1, 1, 1, 1, 1])
assert (np.array_equal(best_state, x) and best_fitness == 5)
def simulatedAnnealing(problem, init_state, max_attempts, iterations):
#Define decay schedule
schedule = mlrose.ExpDecay()
#Solve problem using Simulated Annealing
sm_best_state, sm_best_fitness = mlrose.simulated_annealing(
problem,
schedule=schedule,
max_attempts=max_attempts,
max_iters=iterations,
curve=False,
random_state=1)
return sm_best_fitness
def optimization(problem):
df_optim = pd.DataFrame(columns=algos)
df_iter = pd.DataFrame(columns=algos)
df_time = pd.DataFrame(columns=algos)
for rs in random_states:
# RHC
tic = time.process_time()
best_state_rhc, best_fitness_rhc, curve_rhc = \
mlrose.random_hill_climb(problem, max_attempts=20, max_iters=100000,
restarts=0, init_state=None, curve=True, random_state=rs)
toc = time.process_time()
time_rhc = toc - tic
# SA
tic = time.process_time()
best_state_sa, best_fitness_sa, curve_sa = \
mlrose.simulated_annealing(problem, schedule=mlrose.ExpDecay(init_temp=1.0, exp_const=0.005, min_temp=0.001),
max_attempts = 20, max_iters = 100000,
init_state = None, curve = True, random_state = rs)
toc = time.process_time()
time_sa = toc - tic
# GA
tic = time.process_time()
best_state_ga, best_fitness_ga, curve_ga = \
mlrose.genetic_alg(problem, pop_size=200, mutation_prob=0.1, max_attempts=20, max_iters=100000,
curve=True, random_state=rs)
toc = time.process_time()
time_ga = toc - tic
# MIMIC
tic = time.process_time()
best_state_m, best_fitness_m, curve_m = \
mlrose.mimic(problem, pop_size=20, keep_pct=0.2, max_attempts=20, max_iters=100000,
curve=True, random_state=rs, fast_mimic=False)
toc = time.process_time()
time_m = toc - tic
# df
df_optim.loc[len(df_optim)] = [
best_fitness_rhc, best_fitness_sa, best_fitness_ga, best_fitness_m
]
df_iter.loc[len(df_iter)] = [
len(curve_rhc),
len(curve_sa),
len(curve_ga),
len(curve_m)
]
df_time.loc[len(df_time)] = [time_rhc, time_sa, time_ga, time_m]
print(rs)
return (df_optim.mean(axis=0), df_iter.mean(axis=0), df_time.mean(axis=0))
def test_simulated_annealing_curve_length_max_iters():
"""Test random_hill_climb function such that when curve is True for ma_iters
the length of all fitness scores should be equal to max_iters"""
problem = DiscreteOpt(5, OneMax(), maximize=True)
x = np.array([0, 0, 0, 0, 0])
max_iters = 300
best_state, best_fitness, all_fitness = simulated_annealing(
problem, max_iters=max_iters, init_state=x, curve=True)
assert len(all_fitness) == max_iters
def final_test(problem, ga, sa, rhc, mimic, trials=50):
ga_samples = []
sa_samples = []
rhc_samples = []
mimic_samples = []
ga_time_samples = []
sa_time_samples = []
rhc_time_samples = []
mimic_time_samples = []
for _ in range(trials):
start = time.time()
_, ga_fitness, _ = mlrose.genetic_alg(problem,
pop_size=ga[0],
mutation_prob=ga[1])
ga_time_samples.append(time.time() - start)
start = time.time()
_, sa_fitness, _ = mlrose.simulated_annealing(problem, sa)
sa_time_samples.append(time.time() - start)
start = time.time()
_, rhc_fitness, _ = mlrose.random_hill_climb(problem, rhc)
rhc_time_samples.append(time.time() - start)
start = time.time()
_, mimic_fitness, _ = mlrose.mimic(problem,
pop_size=mimic[0],
keep_pct=mimic[1])
mimic_time_samples.append(time.time() - start)
ga_samples.append(ga_fitness)
sa_samples.append(sa_fitness)
rhc_samples.append(rhc_fitness)
mimic_samples.append(mimic_fitness)
fitness_name = repr(problem.fitness_fn).split('.')[-1].split(' ')[0]
if fitness_name == 'CustomFitness':
fitness_name = 'Saw'
print(f'Final results on {fitness_name}')
print()
print(f'GA max: {np.max(ga_samples)}')
print(f'SA max: {np.max(sa_samples)}')
print(f'RHC max: {np.max(rhc_samples)}')
print(f'MIMIC max: {np.max(mimic_samples)}')
print()
print(f'GA mean: {np.mean(ga_samples)}')
print(f'SA mean: {np.mean(sa_samples)}')
print(f'RHC mean: {np.mean(rhc_samples)}')
print(f'MIMIC mean: {np.mean(mimic_samples)}')
print()
print(f'GA mean execution time: {np.mean(ga_time_samples)}')
print(f'SA mean execution time: {np.mean(sa_time_samples)}')
print(f'RHC mean execution time: {np.mean(rhc_time_samples)}')
print(f'MIMIC mean execution time: {np.mean(mimic_time_samples)}')
def test_simulated_annealing_max_iters():
"""Test simulated_annealing function with max_iters less than
infinite"""
problem = DiscreteOpt(5, OneMax(), maximize=True)
x = np.array([0, 0, 0, 0, 0])
best_state, best_fitness, _ = simulated_annealing(problem,
max_attempts=1,
max_iters=1,
init_state=x)
assert best_fitness == 1
def simulatedAnnealing(fitness, x):
# This code was originally taken and modified from https://mlrose.readthedocs.io/en/stable/source/intro.html
start = time.time()
# Initialize fitness function object using pre-defined class
#fitness = mlrose.Queens()
# Define optimization problem object
if (x == 0):
problem = mlrose.DiscreteOpt(length=12,
fitness_fn=fitness,
maximize=False,
max_val=12)
elif (x == 1):
problem = mlrose.DiscreteOpt(length=9,
fitness_fn=fitness,
maximize=False,
max_val=3)
else:
problem = mlrose.DiscreteOpt(length=8,
fitness_fn=fitness,
maximize=True,
max_val=8)
# Define decay schedule
schedule = mlrose.GeomDecay()
# Solve using random hill climb
if (x == 0):
init_state = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
elif (x == 1):
init_state = np.array([0, 1, 2, 0, 1, 2, 0, 1, 1])
else:
init_state = np.array([0, 1, 2, 3, 4, 5, 6, 7])
best_state, best_fitness, fitness_curve = mlrose.simulated_annealing(
problem,
schedule=schedule,
max_attempts=10,
max_iters=1000,
init_state=init_state,
random_state=1,
curve=True)
end = time.time()
print("Simulated Annealing:")
print('The best state found is: ', best_state)
print('The fitness at the best state is: ', best_fitness)
print("Time: " + str(end - start))
return best_fitness, end - start
def _run_with_sa(self, init_weights, num_nodes, problem):
fitness_curve = []
if init_weights is None:
init_weights = np.random.uniform(-1, 1, num_nodes)
if self.curve:
fitted_weights, loss, fitness_curve = simulated_annealing(
problem,
schedule=self.schedule,
max_attempts=self.max_attempts
if self.early_stopping else self.max_iters,
max_iters=self.max_iters,
init_state=init_weights,
curve=self.curve)
else:
fitted_weights, loss, _ = simulated_annealing(
problem,
schedule=self.schedule,
max_attempts=self.max_attempts
if self.early_stopping else self.max_iters,
max_iters=self.max_iters,
init_state=init_weights,
curve=self.curve)
return fitness_curve, fitted_weights, loss
def temp_SA(cords,fit,temp):
best_fit=[]
max_iter=[]
for i in range(10,temp,1):
best_state,best_fitness3 = mlrose.simulated_annealing(problem_fit,schedule=mlrose.GeomDecay(init_temp=i,decay=0.9,min_temp=0.1),max_attempts=100,max_iters=100,init_state=None)
max_iter.append(i)
best_fit.append(best_fitness3)
print(best_fit)
max_iter=np.asarray(max_iter)
best_fit=np.asarray(best_fit)
line1, = plt.plot(max_iter,best_fit,color='r',label='fitness_score')
plt.ylabel('Fitness_score')
plt.xlabel('Number of attempts')
plt.show()
return None
def sim_annealing(self, max_attempts=10, max_iters=np.inf, decay='geom'):
decay_lookup = {
'geom': mlrose.GeomDecay(),
'exp': mlrose.ExpDecay(),
'arith': mlrose.ArithDecay()
}
start = time.time()
best_state, best_fitness = simulated_annealing(
self.problem_fit,
max_attempts=max_attempts,
max_iters=max_iters,
schedule=decay_lookup[decay],
random_state=111)
end = time.time()
time_elapsed = end - start
return [best_fitness, time_elapsed]
def test_simulated_annealing(self,
title,
max_attempts_range=[100],
decay_range=['geom']):
decay_lookup = {
'geom': mlrose.GeomDecay(),
'exp': mlrose.ExpDecay(),
'arith': mlrose.ArithDecay()
}
fig, (ax1, ax2) = plt.subplots(2, figsize=(12, 8), dpi=80)
fig.suptitle(title + " Simmulated Annealing")
print(title + " Simulated Annealing Algo")
best = [0, 0, 0]
for m in max_attempts_range:
fitness_arr = []
time_arr = []
for d in decay_range:
start = time.time()
# solve using simulated annealing
best_state, best_fitness, curve = mlrose.simulated_annealing(
self.problem_fit,
schedule=decay_lookup[d],
max_attempts=m,
max_iters=np.inf,
curve=True)
fitness_arr.append(best_fitness)
time_arr.append(round(time.time() - start, 2))
if best_fitness > best[2]:
best[0] = m
best[1] = d
best[2] = best_fitness
ax1.plot(decay_range, fitness_arr, label=m, lw=2)
ax2.plot(decay_range, time_arr, lw=2)
ax1.set(xlabel="Decay Range", ylabel="Fitness")
ax2.set(xlabel="Decay Range", ylabel="Time(s)")
print(title +
" SA max_attempts={a}, # decay={b}, best_fitness={c}".format(
a=best[0], b=best[1], c=best[2]))
fig.legend(loc='center right', title='Attempts')
plt.tight_layout()
self.saveToNewDir(fig, "./", title + "_Simulated_Annealing.png")
plt.clf()
def runAnnealing(problem, basePath):
iterations = 500
iterations = 1000
neighborhood = [10]
neighborhood = [2, 4, 12, 36, 50, 75]
neighborhood = [4]
schedules = [('Exp', mlrose.ExpDecay()), ('Arith', mlrose.ArithDecay()),
('Geom', mlrose.GeomDecay())]
schedules = [('Exp', mlrose.ExpDecay())]
times = np.zeros((len(neighborhood), iterations))
nIndex = 0
schedule = mlrose.ExpDecay()
fig, ax = plt.subplots()
plt.title('Annealing')
for neighbor in neighborhood:
s = time()
x = []
y = []
for i in range(1, iterations + 1):
best_state, best_fitness = mlrose.simulated_annealing(
problem,
schedule=schedule,
max_attempts=neighbor,
max_iters=i,
random_state=1)
x.append(i)
y.append(best_fitness)
e = time()
timeTaken = e - s
times[nIndex, i - 1] = timeTaken
print('Itt: {0} - Time:{1}'.format(i, timeTaken))
nIndex += 1
plotLine(x, y, ax,
'Neighbors: {0} - {1}'.format(neighbor, 'Exponential Decay'))
plotTime(x, times, ax)
showLegend(fig, ax)
if basePath:
plt.savefig('{0}\\{1}.png'.format(basePath, 'Annealing'))
else:
plt.show()
return
def run(self, n=1):
"""
n : the number of runs to do
returns best_fitnesses (list), learning_curves (list)
"""
best_fitnesses = []
learning_curves = []
for i in np.arange(n):
_, _, learning_curve = mlrose.simulated_annealing(
self.problem,
schedule=self.schedule,
max_attempts=self.max_attempts,
max_iters=self.max_iters,
random_state=None,
curve=True)
best_fitness = np.max(learning_curve)
best_fitnesses.append(best_fitness)
learning_curves.append(learning_curve)
return best_fitnesses, learning_curves
