def get_prob(self, t_pct=None, p_length=None):
if self.prob_name == 'Four Peaks':
fitness = mlrose.FourPeaks(t_pct)
p_len = 100
self.schedule = mlrose.ExpDecay()
self.restarts = 0
self.mutation_prob = 0.1
self.keep_pct = 0.1
self.pop_size = 500
elif self.prob_name == "Continuous Peaks":
fitness = mlrose.ContinuousPeaks(t_pct)
p_len = 100
self.schedule = mlrose.GeomDecay()
self.restarts = 0
self.mutation_prob = 0.1
self.keep_pct = 0.2
self.pop_size = 200
elif self.prob_name == "Max K Color":
fitness = mlrose.MaxKColor(self.COLOREDGE)
p_len = 100
self.schedule = mlrose.ExpDecay()
self.restarts = 0
self.mutation_prob = 0.2
self.keep_pct = 0.2
self.pop_size = 200
elif self.prob_name == "Flip Flop":
fitness = mlrose.FlipFlop()
p_len = 100
self.schedule = mlrose.ArithDecay()
self.restarts = 0
self.mutation_prob = 0.2
self.keep_pct = 0.5
self.pop_size = 500
elif self.prob_name == "One Max":
fitness = mlrose.OneMax()
p_len = 100
self.schedule = mlrose.GeomDecay()
self.restarts = 0
self.mutation_prob = 0.2
self.keep_pct = 0.1
self.pop_size = 100
else:
fitness = None
p_len = 0
if p_length is None:
p_length = p_len
problem = mlrose.DiscreteOpt(length=p_length, fitness_fn=fitness)
init_state = np.random.randint(2, size=p_length)
return problem, init_state
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 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 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 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 __init__(self,
schedule=mlrose.GeomDecay(),
max_attempts=250,
max_iters=np.inf,
init_state=None,
curve=False,
random_state=None):
print("Initialized Simulated Annealing Optimizer")
self.schedule = schedule
self.max_attempts = max_attempts
self.max_iters = max_iters
self.init_state = init_state
self.curve = curve
self.random_state = random_state
self.bestState = []
self.bestFitness = 0
self.parameters = {
'max_iters':
np.arange(1, 251),
'schedule':
[mlrose.GeomDecay(),
mlrose.ArithDecay(),
mlrose.ExpDecay()]
}
self.bestParameters = {
'max_iters': int(max(np.arange(1, 251))),
'schedule': mlrose.GeomDecay()
}
def main():
# Load the Wine dataset
full_path = 'data/winequality-white.csv'
X, y = load_dataset(full_path)
#Split data into training and test sets
X_train_scaled, X_test_scaled, y_train_hot, y_test_hot = train_test_split(X, y, test_size=0.2, random_state=RANDOM_STATE)
# Initialize neural network object and fit object
t_bef = time.time()
nn_model1 = mlrose.NeuralNetwork(hidden_nodes = [4], activation = 'relu', \
algorithm = 'simulated_annealing', max_iters = 1000, \
bias = True, is_classifier = True, learning_rate = 0.9, \
early_stopping = True, clip_max = 5, schedule=mlrose.ExpDecay(init_temp=20.0, exp_const=0.005, min_temp=0.001), max_attempts = 10, \
random_state = RANDOM_STATE)
nn_model1.fit(X_train_scaled, y_train_hot)
t_aft = time.time()
curve = nn_model1.fitness_curve
# Predict labels for train set and assess accuracy
y_train_pred = nn_model1.predict(X_train_scaled)
y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)
print("Train accuracy:", y_train_accuracy)
# Predict labels for test set and assess accuracy
y_test_pred = nn_model1.predict(X_test_scaled)
y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)
print("Test accuracy:", y_test_accuracy)
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 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 create_nn_schedule(decay):
if decay == 'Geom':
schedule = mlrose.GeomDecay()
if decay == 'Exp':
schedule = mlrose.ExpDecay()
if decay == 'Arith':
schedule = mlrose.ArithDecay()
return mlrose.NeuralNetwork(hidden_nodes=[10],
algorithm='simulated_annealing',
max_iters=1000,
schedule=schedule,
random_state=7106080)
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 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 sa(self, max_iter=10000, schedule=None):
if schedule is None:
schedule = mlrose.ExpDecay()
learner = mlrose.NeuralNetwork(hidden_nodes=[2],
activation='relu',
algorithm='simulated_annealing',
max_iters=max_iter,
bias=True,
learning_rate=3e-03,
early_stopping=True,
max_attempts=1000,
schedule=schedule)
return learner
def pruebas():
# read data
print(
'\n============== Problema del agente viajero ======================')
#### get data
times_list, lips, prod_lips = times_array('datos_macbelle_dummie.csv',
'tiempos_produccion.csv')
#### metaheuristics
n = len(lips)
#genetic alg
# simple
args = {'random_state': 41}
bs, bf = metaheuristic(times_list, meta=mr.genetic_alg, nnodes=n, **args)
# modified hypérparameters
args2 = {'random_state': 41, 'mutation_prob': 0.3, 'max_attempts': 20}
bs2, bf2 = metaheuristic(times_list,
meta=mr.genetic_alg,
nnodes=n,
**args2)
# simulated anealing
# simple
args_sa = {'random_state': 41}
bs_sa, bf_sa = metaheuristic(times_list,
meta=mr.simulated_annealing,
nnodes=n,
**args_sa)
# change temperature decay
# look documentation mlrose of decays
args_sa2 = {'random_state': 41, 'schedule': mr.ExpDecay(exp_const=0.05)}
bs_sa2, bf_sa2 = metaheuristic(times_list,
meta=mr.simulated_annealing,
nnodes=n,
**args_sa2)
# combined with genetic alg solutions
args_sa3 = {
'random_state': 41,
'schedule': mr.GeomDecay(decay=0.05),
'init_state': bs
} #use GA solution as initial state
bs_sa3, bf_sa3 = metaheuristic(times_list,
meta=mr.simulated_annealing,
nnodes=n,
**args_sa3)
return ()
def nn_sa_temps(train_x, train_y, test_x, test_y):
min_temps = [1, 5]
init_temps = [100, 500]
markers = ['o', 'x', '^', 's']
colors = ['g', 'b', 'r', 'c', 'y', 'm']
counter = 0
for index, min_temp in enumerate(min_temps):
for jindex, init_temp in enumerate(init_temps):
schedule = mlrose.ExpDecay(init_temp=init_temp,
exp_const=0.001,
min_temp=min_temp)
nn_model = mlrose.NeuralNetwork(hidden_nodes=[8, 6],
activation='tanh',
algorithm='simulated_annealing',
schedule=schedule,
max_iters=1000,
bias=True,
is_classifier=True,
learning_rate=0.0001,
early_stopping=True,
clip_max=1000,
max_attempts=100,
random_state=3,
curve=True)
fitted_model = nn_model.fit(train_x, train_y)
label = "min_temp: " + str(min_temp) + ", init_temp: " + str(
init_temp)
plt.plot(fitted_model.fitness_curve,
c=colors[counter],
label=label)
counter += 1
plt.xlabel("No. of Iterations")
plt.ylabel("Fitness")
plt.title("NN Training: SA")
plt.legend(loc="lower right")
plt.tight_layout()
plt.savefig("nn_sa_temps")
plt.cla()
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 main():
# want to maximize this
fitness = mlrose.CustomFitness(detected_max)
problem = mlrose.DiscreteOpt(length=24,
fitness_fn=fitness,
maximize=True,
max_val=scale_factor)
schedule = mlrose.ExpDecay()
best_state, max_faces = mlrose.simulated_annealing(
problem,
schedule=schedule,
max_attempts=10,
max_iters=1000,
init_state=initial_state,
random_state=1)
print('Optimal state found: ', best_state)
print('Max fitness found: ', max_faces)
# save the optimal found
get_img_from_state(best_state)
print("Number of faces in output: ", len(detect_faces(cv2.imread(OUTPUT))))
def runalgos(problem, max_attempts, iterations):
#Define decay schedule
schedule = mlrose.ExpDecay()
#Solve problem using Random Hill Climbing
rh_best_state, rh_best_fitness = mlrose.random_hill_climb(
problem,
max_attempts=max_attempts,
max_iters=iterations,
restarts=1,
init_state=init_state,
random_state=1)
#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)
#Solve problem using Gentic Algorithm
genA_best_state, genA_best_fitness = mlrose.genetic_alg(
problem,
pop_size=200,
mutation_prob=0.1,
max_attempts=max_attempts,
max_iters=iterations,
curve=False,
random_state=1)
#Solve problem using MIMIC
mimic_best_state, mimic_best_fitness = mlrose.mimic(
problem,
pop_size=200,
keep_pct=0.2,
max_attempts=max_attempts,
max_iters=iterations,
curve=False,
random_state=1)
#return rh_best_state,rh_best_fitness,sm_best_state,sm_best_fitness,genA_best_state,genA_best_fitness,mimic_best_state,mimic_best_fitness
return rh_best_fitness, sm_best_fitness, genA_best_fitness, mimic_best_fitness
def main():
# mlrose
fitness_cust = mlrose.CustomFitness(simulate)
problem = mlrose.DiscreteOpt(fitness_fn=fitness_cust, maximize=False, length=2, max_val = 30)
schedule = mlrose.ExpDecay()
init_state = np.array([28,7])
best_time_values, best_avg_time = mlrose.simulated_annealing(problem, schedule = schedule,
max_attempts = 10, max_iters = 100,
init_state = init_state, random_state = 1)
print("Best time values:")
print(best_time_values)
print("Best avg time:")
print(best_avg_time)
rsed = int(sys.argv[16])
dat = np.loadtxt(open("data/digit.csv", "rb"), delimiter=",", skiprows=1)
target = dat[:, dat.shape[1] - 1].flatten().astype(np.int32)
V = dat[:, 0:(dat.shape[1] - 1)]
Xtrain = V[0:splitN] # 70% of data are training data.
endN = min(splitN + splitN, 5540)
Xtest = V[splitN:endN]
ytrain = target[0:splitN]
ytest = target[splitN:endN]
oneH = OneHotEncoder()
ytrain = oneH.fit_transform(ytrain.reshape(-1, 1)).todense()
ytest = oneH.fit_transform(ytest.reshape(-1, 1)).todense()
schedule = mlrose.ExpDecay(init_temp=iniT, exp_const=Tdcy, min_temp=minT)
timeCostAvg = 0.0
ytrainAccAvg = 0.0
ytestAccAvg = 0.0
fitnessAvg = np.zeros(mxit)
Ntry = 10
for i in range(Ntry):
model = mlrose.NeuralNetwork(hidden_nodes=[16],
activation='relu',
algorithm='simulated_annealing',
max_iters=mxit,
bias=True,
is_classifier=True,
learning_rate=lrte,
def main():
name_of_exp = "One Max"
fitness = mlrose.OneMax()
rhc = []
sa = []
ga = []
mimic = []
z_s = ['RHC', 'SA', 'GA', 'MIMIC']
for i in [5, 10, 15]:
problem = mlrose.DiscreteOpt(length=i,
fitness_fn=fitness,
maximize=True,
max_val=2)
# Define initial state
init_state = np.zeros(i)
# Solve problem using simulated annealing
best_fitness = 0
learning_curve = []
max_atts = 10
print("RHC")
while best_fitness != i:
best_state, best_fitness, learning_curve, timing_curve = mlrose.random_hill_climb(
problem,
max_attempts=max_atts,
max_iters=max_atts,
restarts=1,
init_state=init_state,
curve=True,
random_state=1)
max_atts += 10
rhc.append(learning_curve)
print(i)
print(best_fitness)
print(max_atts)
print("SA")
best_fitness = 0
learning_curve = []
max_atts = 10
while best_fitness != i:
best_state, best_fitness, learning_curve, timing_curve = mlrose.simulated_annealing(
problem,
max_attempts=max_atts,
max_iters=max_atts,
schedule=mlrose.ExpDecay(),
init_state=init_state,
curve=True,
random_state=1)
max_atts += 10
sa.append(learning_curve)
print(i)
print(best_fitness)
print(max_atts)
print("GA")
best_fitness = 0
learning_curve = []
max_atts = 10
while best_fitness != i:
best_state, best_fitness, learning_curve, timing_curve = mlrose.genetic_alg(
problem,
pop_size=100,
mutation_prob=0.1,
max_attempts=max_atts,
max_iters=max_atts,
curve=True,
random_state=1)
max_atts += 10
ga.append(learning_curve)
print(i)
print(best_fitness)
print(max_atts)
print("MIMC")
best_fitness = 0
learning_curve = []
max_atts = 10
while best_fitness != i:
best_state, best_fitness, learning_curve, timing_curve = mlrose.mimic(
problem,
pop_size=300,
keep_pct=0.1,
max_attempts=max_atts,
max_iters=max_atts,
curve=True,
random_state=1,
fast_mimic=True)
max_atts += 10
mimic.append(learning_curve)
print(i)
print(best_fitness)
print(max_atts)
f, axarr = plt.subplots(1, 4)
f.set_figheight(3)
f.set_figwidth(12)
for y in rhc:
for i in ['5', '10', '15']:
axarr[0].plot(y, label='{}'.format(i))
axarr[0].set_title('RHC vs Input Size')
for y in sa:
for i in ['5', '10', '15']:
axarr[1].plot(y, label='{}'.format(i))
axarr[1].set_title('SA vs Input Size')
for y in ga:
for i in ['5', '10', '15']:
axarr[2].plot(y, label='{}'.format(i))
axarr[2].set_title('GA vs Input Size')
for y in mimic:
for i in ['5', '10', '15']:
axarr[3].plot(y, label='{}'.format(i))
axarr[3].set_title('MIMC vs Input Size')
# Fine-tune figure; hide x ticks for top plots and y ticks for right plots
#plt.setp([a.get_xticklabels() for a in axarr[0]], visible=False)
#plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)
plt.legend(handles=[
Line2D([0], [0], color='g', lw=4, label='5'),
Line2D([0], [0], color='brown', lw=4, label='10'),
Line2D([0], [0], color='y', lw=4, label='15')
])
#plt.title('Input size vs fitness curve One Max')
# plt.xlabel('Function iteration count')
#plt.ylabel('Fitness function value')
plt.show()
def main():
name_of_exp = "TSP"
# Create list of city coordinates
coords_list = [(1, 1), (4, 2), (5, 2), (6, 4), (4, 4), (3, 6), (1, 5), (2, 3)]
# Initialize fitness function object using coords_list
fitness_coords = mlrose.TravellingSales(coords=coords_list)
problem = mlrose.TSPOpt(length=8, fitness_fn=fitness_coords,
maximize=False)
# Define initial state
x_s = []
y_s = []
z_s = ['RHC', 'SA', 'GA', 'MIMIC']
w_s = []
max_val = 19.0
found_flag = False
for restarts in np.arange(0, 5):
if found_flag:
break
for max_iter_atts in np.arange(10, 1000, 10):
if found_flag:
break
# Solve problem using simulated annealing
best_state, best_fitness, learning_curve, timing_curve = mlrose.random_hill_climb(problem, max_attempts=int(
max_iter_atts), max_iters=int(max_iter_atts),
restarts=int(restarts),
curve=True,
random_state=1)
if best_fitness <= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(restarts)
found_flag = True
found_flag = False
for sched in [mlrose.ExpDecay(), mlrose.GeomDecay(), mlrose.ArithDecay()]:
if found_flag:
break
for max_iter_atts in np.arange(10, 1000, 10):
if found_flag:
break
best_state, best_fitness, learning_curve, timing_curve = mlrose.simulated_annealing(problem,
max_attempts=int(
max_iter_atts),
max_iters=int(
max_iter_atts),
schedule=sched,
curve=True,
random_state=1)
if best_fitness <= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(sched)
found_flag = True
found_flag = False
for prob in np.arange(0.1, 1.1, 0.1):
if found_flag:
break
for pop_size in np.arange(100, 1000, 100):
if found_flag:
break
for max_iter_atts in np.arange(100, 1000, 100):
if found_flag:
break
best_state, best_fitness, learning_curve, timing_curve = mlrose.genetic_alg(problem,
pop_size=int(pop_size),
mutation_prob=prob,
max_attempts=int(
max_iter_atts),
max_iters=int(
max_iter_atts),
curve=True,
random_state=1)
if best_fitness <= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(prob)
print(pop_size)
found_flag = True
found_flag = False
for prob in np.arange(0.1, 0.5, 0.1):
if found_flag:
break
for pop_size in np.arange(100, 1000, 100):
if found_flag:
break
for max_iter_atts in np.arange(100, 1000, 100):
if found_flag:
break
best_state, best_fitness, learning_curve, timing_curve = mlrose.mimic(problem, pop_size=int(pop_size),
keep_pct=prob,
max_attempts=int(max_iter_atts),
max_iters=int(max_iter_atts),
curve=True,
random_state=1,
fast_mimic=True)
if best_fitness <= max_val:
x_s.append(np.arange(0, len(learning_curve)))
y_s.append(learning_curve)
w_s.append(timing_curve)
print(best_state)
print(best_fitness)
print(max_iter_atts)
print(prob)
print(pop_size)
found_flag = True
for x, y, z in zip(x_s, y_s, z_s):
plt.plot(x, y, label=z)
plt.legend()
plt.title('Randomized Optimization Iterations vs Fitness Function Value for {}'.format(name_of_exp))
plt.xlabel('Function iteration count')
plt.ylabel('Fitness function value')
plt.show()
plt.clf()
for x, w, z in zip(x_s, w_s, z_s):
plt.plot(x, w, label=z)
plt.legend()
plt.title('Randomized Optimization Time vs Fitness Function Value for {}'.format(name_of_exp))
plt.xlabel('Function iteration count')
plt.ylabel('Time in Seconds')
plt.show()
def solve_sa(problem_fit):
""" Solving using Simulated Annealing """
min_fitness = np.inf
best_params_dict = {}
# Parameter list
initial_temp_list = [10, 100, 1000]
min_temp_list = [1, 10, 100]
decay_list = [0.001, 0.01, 0.1]
# Solve
for t0 in initial_temp_list:
for t in min_temp_list:
# Initial temp should be greater than Minimum temp
if t0 < t:
continue
for d in decay_list:
for c in range(2):
if c == 0:
schedule = mlrose.ExpDecay(init_temp=t0,
exp_const=d,
min_temp=t)
else:
schedule = mlrose.ArithDecay(init_temp=t0,
decay=d,
min_temp=t)
# Start timer
t_start = time.time()
best_state, best_fitness, fitness_curve = mlrose.simulated_annealing(
problem=problem_fit,
schedule=schedule,
max_attempts=500,
max_iters=5000,
random_state=np.random.seed(7),
curve=True)
# End timer
t_end = time.time()
# Storing the best result
if best_fitness < min_fitness:
min_fitness = best_fitness
best_params_dict['Fitness'] = best_fitness
best_params_dict['Solution'] = best_state
best_params_dict['Initial_temp'] = t0
best_params_dict['Min_temp'] = t
best_params_dict['Decay'] = d
best_params_dict[
'Schedule'] = "Exponential" if c == 0 else "Arithmetic"
best_params_dict['Fitness_curve'] = fitness_curve
best_params_dict['Time'] = round(t_end - t_start, 2)
# Printing the best result
print("-- Parameters --")
print("\tCooling Schedule: ", best_params_dict['Schedule'])
print("\tInitial Temperature: ", best_params_dict['Initial_temp'])
print("\tMin Temperature: ", best_params_dict['Min_temp'])
print("\tDecay rate: ", best_params_dict['Decay'])
print("\tMax Attempts at each step: 100")
print("\tStopping Criterion = Max Iterations of the algorithm: 10000")
print("Results:")
print("\tSolution (Order of city traversal by index): ",
best_params_dict['Solution'])
print("\tFitness: ", round(best_params_dict['Fitness'], 2))
print("Computational Time: ", best_params_dict['Time'], " seconds\n\n")
# Plotting
plt.plot(-(best_params_dict['Fitness_curve']))
plt.title("Convergence curve: TSP-Qatar using Simulated Annealing")
plt.xlabel("Iterations")
plt.ylabel("Fitness")
plt.savefig("tsp_qatar_sa.png")
def sim_annealing_runner(problem):
initial_temps = np.arange(.1, 5, .5) #.1)
final_temps = np.arange(.001, 1, .1)
decay_rates = np.arange(.001, 1, .1)
attempts = np.arange(10, 500, 50).astype(int)
iterations = np.arange(10, 500, 50).astype(int)
scoring_dict = {}
timing_dict = {}
init_temp_scores = []
final_temp_scores = []
for i, temp in enumerate(initial_temps):
anneal_schedule = mlrose.ExpDecay(init_temp=temp)
best_fits = []
for i in MC_RUNS:
_, best_fitness = mlrose.simulated_annealing(
problem,
schedule=anneal_schedule,
max_attempts=100,
max_iters=800)
best_fits.append(best_fitness)
init_temp_scores.append(best_fits)
scoring_dict['Initial Temperature'] = pd.DataFrame(init_temp_scores,
columns=MC_RUNS,
index=initial_temps)
for i, temp in enumerate(final_temps):
anneal_schedule = mlrose.ExpDecay(min_temp=temp)
best_fits = []
for i in MC_RUNS:
_, best_fitness = mlrose.simulated_annealing(
problem,
schedule=anneal_schedule,
max_attempts=100,
max_iters=800)
best_fits.append(best_fitness)
final_temp_scores.append(best_fits)
scoring_dict['Ending Temperature'] = pd.DataFrame(final_temp_scores,
index=final_temps,
columns=MC_RUNS)
decay_scores = []
for i, rate in enumerate(decay_rates):
anneal_schedule = mlrose.ExpDecay(exp_const=rate)
best_fits = []
for i in MC_RUNS:
_, best_fitness = mlrose.simulated_annealing(
problem,
schedule=anneal_schedule,
max_attempts=100,
max_iters=800)
best_fits.append(best_fitness)
decay_scores.append(best_fits)
scoring_dict['Decay Rate'] = pd.DataFrame(decay_scores,
columns=MC_RUNS,
index=decay_rates)
attempts_scores = []
attempts_timing = []
for i, att in enumerate(attempts):
anneal_schedule = mlrose.ExpDecay()
best_fits = []
times = []
for i in MC_RUNS:
start = datetime.now()
_, best_fitness = mlrose.simulated_annealing(
problem,
schedule=anneal_schedule,
max_attempts=int(att),
max_iters=800)
end = datetime.now()
best_fits.append(best_fitness)
times.append((end - start).total_seconds())
attempts_scores.append(best_fits)
attempts_timing.append(times)
scoring_dict['Number of Attempts'] = pd.DataFrame(attempts_scores,
columns=MC_RUNS,
index=attempts)
timing_dict['Number of Attempts'] = pd.DataFrame(attempts_timing,
columns=MC_RUNS,
index=attempts)
iteration_scores = []
for i, iteration in enumerate(iterations):
anneal_schedule = mlrose.ExpDecay()
best_fits = []
for i in MC_RUNS:
_, best_fitness = mlrose.simulated_annealing(
problem,
schedule=anneal_schedule,
max_attempts=100,
max_iters=int(iteration))
best_fits.append(best_fitness)
iteration_scores.append(best_fits)
scoring_dict['Max Iterations'] = pd.DataFrame(iteration_scores,
columns=MC_RUNS,
index=iterations)
return scoring_dict, timing_dict
end = time.time()
return np.array([best_score, len(curve), fit_evals, end - start])
# ## SA param search
runs = 15
temps = np.arange(1, 32, 2)
sa_results = {}
with concurrent.futures.ThreadPoolExecutor() as exe:
for prob in probs:
if prob['name'] not in sa_results: sa_results[prob['name']] = {}
for temp in temps:
if temp not in sa_results[prob['name']]:
sa_results[prob['name']][temp] = []
for run in range(runs + 1):
schedule = mlrose.ExpDecay(init_temp=temp, min_temp=0)
sa_results[prob['name']][temp].append(
exe.submit(sim_anneal,
prob['obj'],
max_iters=500,
schedule=schedule))
# ### SA param search plotting
sa_plot_data = {}
for prob in sa_results:
sa_plot_data[prob] = None
for temp in sa_results[prob]:
data = None
for run in sa_results[prob][temp]:
# DATA: temp, score, iters, fit_evals, runtime
if data is None:
import time as tt
import numpy as np
import mlrose as mlrose
import pandas as pd
ps = [8,32,64,128]
for p in ps:
fitness = mlrose.FlipFlop()
istate = np.array(np.zeros(p), dtype=int)
problem = mlrose.DiscreteOpt(length=p, fitness_fn=fitness,
maximize=True, max_val=2)
schedule = mlrose.ExpDecay(init_temp=0.5, exp_const=0.005, min_temp=0.001)
start_fit_time = tt.time()
best_state, best_fitness,fitness_curve = mlrose.random_hill_climb(problem,max_attempts = 500, max_iters = 500,restarts=0,init_state = istate, random_state = 1,curve=True)
end_fit_time = tt.time()
it = len(fitness_curve)
time = end_fit_time - start_fit_time
tpi = time/it
print('SA')
print(f'sa, input size: {p},time={time}, iterations={it}, time per iteration={tpi}')
# FOUR PEAKS
fourpeaks = mlrose.FourPeaks()
fourpeaks_problem = mlrose.DiscreteOpt(length=60,
maximize=True,
max_val=2,
fitness_fn=fourpeaks)
base_test(fourpeaks,
theoretical_best_fitness=lambda x: 2 * x - 0.1 * x - 1) # 73s
optimize_ga(fourpeaks_problem) # 768s
optimize_sa(fourpeaks_problem) # 532s
optimize_rhc(fourpeaks_problem) # 822s
optimize_mimic(fourpeaks_problem) # 2464s
final_test(fourpeaks_problem, [700, 0.4], mlrose.ExpDecay(8, 0.00001), 5000,
[500, 0.022]) # 1191s
# SAW
saw_fitness = mlrose.CustomFitness(saw, problem_type='discrete')
saw_problem = mlrose.DiscreteOpt(length=700,
maximize=True,
max_val=2,
fitness_fn=saw_fitness)
base_test(saw_fitness,
theoretical_best_fitness=lambda x: x,
lengths=range(200, 701, 100)) # 2476s
optimize_ga(saw_problem) # 7255s
optimize_sa(saw_problem) # 8730s
optimize_rhc(saw_problem) # 103s
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from sklearn.metrics import accuracy_score
# Initialize fitness function object using pre-defined class
fitness = mlrose.FourPeaks(t_pct=0.15)
# Define optimization problem object
problem = mlrose.DiscreteOpt(length=100,
fitness_fn=fitness,
maximize=True,
max_val=2)
# Define decay schedule
schedule = mlrose.ExpDecay()
# Run Genetic Algorithm
best_state, best_fitness, fit_curve = mlrose.genetic_alg(problem,
max_attempts=1000,
max_iters=1000,
curve=True,
random_state=1)
# Stop Timer
elapsed_time = timer() - start # in seconds
print('Time elapsed time in seconds is: ', elapsed_time)
print('The best state found is: ', best_state)
print('The fitness at the best state is: ', best_fitness)
# Plot Curve
import matplotlib.pyplot as plt
plt.plot(fit_curve)
plt.ylabel('Fitness')
def optimize_sa(problem, trials=50):
init_temps = np.linspace(1.0, 10.0, 10)
decay_rates = np.linspace(0.1, 0.99, 10)
for i in range(len(decay_rates)):
decay_rates[i] = int(decay_rates[i] * 100) / 100
fitness_values = [[] for _ in range(len(decay_rates))]
for i, decay_rate in enumerate(decay_rates):
for init_temp in init_temps:
samples = []
for _ in range(trials):
decay = mlrose.GeomDecay(init_temp=init_temp, decay=decay_rate)
_, fitness_value, _ = mlrose.simulated_annealing(
problem, decay)
samples.append(fitness_value)
fitness_values[i].append(np.mean(samples))
display_cm(
np.array(fitness_values), f'Simulated annealing tuning on '
f'{repr(problem.fitness_fn).split(".")[-1].split(" ")[0]}, length={problem.length}',
'Initial temperature', 'Decay rate', init_temps, decay_rates)
fitness_values = [[] for _ in range(len(decay_rates))]
for i, decay_rate in enumerate(decay_rates):
for init_temp in init_temps:
samples = []
for _ in range(trials):
decay = mlrose.ArithDecay(init_temp=init_temp,
decay=decay_rate)
_, fitness_value, _ = mlrose.simulated_annealing(
problem, decay)
samples.append(fitness_value)
fitness_values[i].append(np.mean(samples))
display_cm(
np.array(fitness_values), f'Simulated annealing tuning on '
f'{repr(problem.fitness_fn).split(".")[-1].split(" ")[0]}, length={problem.length}',
'Initial temperature', 'Decay rate', init_temps, decay_rates)
init_temps = np.linspace(1.0, 10.0, 10)
exp_consts = np.linspace(0.0001, 0.003, 10)
for i in range(len(exp_consts)):
exp_consts[i] = int(exp_consts[i] * 10000) / 10000
fitness_values = [[] for _ in range(len(exp_consts))]
for i, exp_const in enumerate(exp_consts):
for init_temp in init_temps:
samples = []
time_samples = []
for _ in range(trials):
decay = mlrose.ExpDecay(init_temp=init_temp,
exp_const=exp_const)
start = time.time()
_, fitness_value, _ = mlrose.simulated_annealing(
problem, decay)
time_samples.append(time.time() - start)
samples.append(fitness_value)
fitness_values[i].append(np.mean(samples))
display_cm(
np.array(fitness_values), f'Simulated annealing tuning on '
f'{repr(problem.fitness_fn).split(".")[-1].split(" ")[0]}, length={problem.length}',
'Initial temperature', 'Exp const', init_temps, exp_consts)
