def plot_sa_optimization(problem, random_seeds, **kwargs):
sa_curve = [] # curve to plot for SA
# For all temperature exponential decay rate
for exp_decay_rate in kwargs['sa_decay_rates']:
# Define exponential decay schedule
print('\nSA: exp decay rate = {:.3f}'.format(exp_decay_rate))
exp_decay = ExpDecay(init_temp=kwargs['sa_init_temp'],
exp_const=exp_decay_rate,
min_temp=kwargs['sa_min_temp'])
sa_objectives = [
] # list of results from multiple random runs for current rate
# For multiple random runs
for random_seed in random_seeds:
# Run SA and get best state and objective found
best_state, best_objective, _ = simulated_annealing(
problem,
schedule=exp_decay,
max_attempts=kwargs['sa_max_iters'],
max_iters=kwargs['sa_max_iters'],
curve=False,
random_state=random_seed)
sa_objectives.append(best_objective)
print('SA: best_fitness = {:.3f}'.format(best_objective))
sa_curve.append(sa_objectives) # append random run to SA curve
# Plot, set title and labels
plt.figure()
utils.plot_helper(x_axis=kwargs['sa_decay_rates'],
y_axis=np.array(sa_curve).T,
label='SA')
utils.set_plot_title_labels(
title='{} - Objective vs. temperature exponential decay rate'.format(
kwargs['plot_name']),
x_label='Exponential decay rate',
y_label=kwargs['plot_ylabel'])
# Save figure
plt.savefig(IMAGE_DIR + '{}_objective_vs_temp'.format(kwargs['plot_name']))
def plot_nn_performances(x_train, y_train, random_seeds, **kwargs):
"""Plot Neural Networks performances on the training set.
Use different optimizations algorithms (RHC, SA, GA and GD) and compare results on the training set using
k-fold cross-validation.
Args:
x_train (ndarray): training data.
y_train (ndarray): training labels.
random_seeds (list or array): random seeds for multiple random runs to use for k-fold cross-validation.
kwargs (dict): additional arguments to pass for curves plotting:
- rhc_max_iters (list or ndarray): RHC list or array of maximum number of iterations to plot vs.
- sa_max_iters (list or ndarray): SA list or array of maximum number of iterations to plot vs.
- ga_max_iters (list or ndarray): GA list or array of maximum number of iterations to plot vs.
- gd_max_iters (list or ndarray): GD list or array of maximum number of iterations to plot vs.
- init_temp (float): SA initial temperature.
- exp_decay_rate (float): SA temperature exponential decay rate.
- min_temp (float): SA minimum temperature.
- pop_size (int): GA population size.
- mutation_prob (float): GA mutation probability.
Returns:
None.
"""
# Initialize algorithms, corresponding acronyms and max number of iterations
algorithms = [
'random_hill_climb', 'simulated_annealing', 'genetic_alg',
'gradient_descent'
]
acronyms = ['RHC', 'SA', 'GA', 'GD']
max_iters = [
'rhc_max_iters', 'sa_max_iters', 'ga_max_iters', 'gd_max_iters'
]
# Initialize lists of training curves, validation curves and training times curves
train_curves, val_curves, train_time_curves = [], [], []
# Define SA exponential decay schedule
exp_decay = ExpDecay(init_temp=kwargs['init_temp'],
exp_const=kwargs['exp_decay_rate'],
min_temp=kwargs['min_temp'])
# Create one figure for training and validation losses, the second for training time
plt.figure()
train_val_figure = plt.gcf().number
plt.figure()
train_times_figure = plt.gcf().number
# For each of the optimization algorithms to test the Neural Network with
for i, algorithm in enumerate(algorithms):
print('\nAlgorithm = {}'.format(algorithm))
# For multiple random runs
for random_seed in random_seeds:
# Initialize training losses, validation losses and training time lists for current random run
train_losses, val_losses, train_times = [], [], []
# Compute stratified k-fold
x_train_fold, x_val_fold, y_train_fold, y_val_fold = train_test_split(
x_train,
y_train,
test_size=0.2,
shuffle=True,
random_state=random_seed,
stratify=y_train)
# For each max iterations to run for
for max_iter in kwargs[max_iters[i]]:
# Define Neural Network using current algorithm
nn = NeuralNetwork(hidden_nodes=[50, 30],
activation='relu',
algorithm=algorithm,
max_iters=int(max_iter),
bias=True,
is_classifier=True,
learning_rate=0.001,
early_stopping=False,
clip_max=1e10,
schedule=exp_decay,
pop_size=kwargs['pop_size'],
mutation_prob=kwargs['mutation_prob'],
max_attempts=int(max_iter),
random_state=random_seed,
curve=False)
# Train on current training fold and append training time
start_time = time.time()
nn.fit(x_train_fold, y_train_fold)
train_times.append(time.time() - start_time)
# Compute and append training and validation log losses
train_loss = log_loss(y_train_fold, nn.predict(x_train_fold))
val_loss = log_loss(y_val_fold, nn.predict(x_val_fold))
train_losses.append(train_loss)
val_losses.append(val_loss)
print('{} - train loss = {:.3f}, val loss = {:.3f}'.format(
max_iter, train_loss, val_loss))
# Append curves for current random seed to corresponding lists of curves
train_curves.append(train_losses)
val_curves.append(val_losses)
train_time_curves.append(train_times)
# Plot training and validation figure for current algorithm
plt.figure(train_val_figure)
utils.plot_helper(x_axis=kwargs[max_iters[i]],
y_axis=np.array(train_curves),
label='{} train'.format(acronyms[i]))
utils.plot_helper(x_axis=kwargs[max_iters[i]],
y_axis=np.array(val_curves),
label='{} val'.format(acronyms[i]))
# Plot training time figure for current algorithm
plt.figure(train_times_figure)
utils.plot_helper(x_axis=kwargs[max_iters[i]],
y_axis=np.array(train_time_curves),
label=acronyms[i])
# Set title and labels to training and validation figure
plt.figure(train_val_figure)
utils.set_plot_title_labels(title='Neural Network - Loss vs. iterations',
x_label='Iterations',
y_label='Loss')
# Save figure
plt.savefig(IMAGE_DIR + 'nn_objective_vs_iterations')
# Set title and labels to training time figure
plt.figure(train_times_figure)
utils.set_plot_title_labels(title='Neural Network - Time vs. iterations',
x_label='Iterations',
y_label='Time (seconds)')
# Save figure
plt.savefig(IMAGE_DIR + 'nn_time_vs_iterations')
def plot_performances(problem, random_seeds, **kwargs):
# Initialize lists of objectives curves and time curves
rhc_objectives, sa_objectives, ga_objectives, mimic_objectives = [], [], [], []
rhc_times, sa_times, ga_times, mimic_times = [], [], [], []
# Set an exponential decay schedule for SA
exp_decay = ExpDecay(init_temp=kwargs['sa_init_temp'],
exp_const=kwargs['sa_exp_decay_rate'],
min_temp=kwargs['sa_min_temp'])
# For multiple random runs
for random_seed in random_seeds:
# Run RHC and get best state and objective found
_, best_objective, objective_curve = random_hill_climb(
problem,
max_attempts=kwargs['rhc_max_iters'],
max_iters=kwargs['rhc_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
rhc_objectives.append(objective_curve)
rhc_times.append(utils.times)
print('\nRHC: best_fitness = {:.3f}'.format(best_objective))
# Run SA and get best state and objective found
_, best_objective, objective_curve = simulated_annealing(
problem,
schedule=exp_decay,
max_attempts=kwargs['sa_max_iters'],
max_iters=kwargs['sa_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
sa_objectives.append(objective_curve)
sa_times.append(utils.times)
print('SA: best_fitness = {:.3f}'.format(best_objective))
# Run GA and get best state and objective found
_, best_objective, objective_curve = genetic_alg(
problem,
pop_size=kwargs['ga_pop_size'],
pop_breed_percent=1.0 - kwargs['ga_keep_pct'],
max_attempts=kwargs['ga_max_iters'],
max_iters=kwargs['ga_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
ga_objectives.append(objective_curve)
ga_times.append(utils.times)
print('GA: best_fitness = {:.3f}'.format(best_objective))
# Run MIMIC and get best state and objective found
_, best_objective, objective_curve = mimic(
problem,
pop_size=kwargs['mimic_pop_size'],
keep_pct=kwargs['mimic_keep_pct'],
max_attempts=kwargs['mimic_max_iters'],
max_iters=kwargs['mimic_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
mimic_objectives.append(objective_curve)
mimic_times.append(utils.times)
print('MIMIC: best_fitness = {:.3f}'.format(best_objective))
# Array of iterations to plot objectives vs. for RHC, SA, GA and MIMIC
rhc_iterations = np.arange(1, kwargs['rhc_max_iters'] + 1)
sa_iterations = np.arange(1, kwargs['sa_max_iters'] + 1)
ga_iterations = np.arange(1, kwargs['ga_max_iters'] + 1)
mimic_iterations = np.arange(1, kwargs['mimic_max_iters'] + 1)
# Plot objective curves, set title and labels
plt.figure()
utils.plot_helper(x_axis=rhc_iterations,
y_axis=np.array(rhc_objectives),
label='RHC')
utils.plot_helper(x_axis=sa_iterations,
y_axis=np.array(sa_objectives),
label='SA')
utils.plot_helper(x_axis=ga_iterations,
y_axis=np.array(ga_objectives),
label='GA')
utils.plot_helper(x_axis=mimic_iterations,
y_axis=np.array(mimic_objectives),
label='MIMIC')
utils.set_plot_title_labels(title='{} - Fitness versus iterations'.format(
kwargs['plot_name']),
x_label='Iterations',
y_label=kwargs['plot_ylabel'])
# Save figure
plt.savefig(IMAGE_DIR +
'{}_fitness_vs_iterations'.format(kwargs['plot_name']))
# Plot times, set title and labels
plt.figure()
utils.plot_helper(x_axis=rhc_iterations,
y_axis=np.array(rhc_times),
label='RHC')
utils.plot_helper(x_axis=sa_iterations,
y_axis=np.array(sa_times),
label='SA')
utils.plot_helper(x_axis=ga_iterations,
y_axis=np.array(ga_times),
label='GA')
utils.plot_helper(x_axis=mimic_iterations,
y_axis=np.array(mimic_times),
label='MIMIC')
utils.set_plot_title_labels(title='{} - Time versus iterations'.format(
kwargs['plot_name']),
x_label='Iterations',
y_label='Time (seconds)')
# Save figure
plt.savefig(IMAGE_DIR +
'{}_time_vs_iterations'.format(kwargs['plot_name']))
def plot_ga_mimic_optimization(problem, param_name, random_seeds, **kwargs):
ga_curve, mimic_curve = [], [] # curves to plot for GA and MIMIC
# Initialize, for GA and MIMIC, the parameter we don't have to loop through and label for plotting
if param_name == 'keep_pct':
ga_pop_size = kwargs['ga_pop_size']
mimic_pop_size = kwargs['mimic_pop_size']
label = 'Percentage to keep'
elif param_name == 'pop_size':
ga_keep_pct = kwargs['ga_keep_pct']
mimic_keep_pct = kwargs['mimic_keep_pct']
label = 'Population size'
else:
raise Exception('Param name has to be either pop_size or keep_pct'
) # raise exception if invalid entry
# For all parameters
for param in kwargs[param_name + 's']:
print('\nGA & MIMIC: {} = {:.3f}'.format(param_name, param))
ga_objectives, mimic_objectives = [], [
] # list of results from multiple random runs for current parameter
# Initialize, for GA and MIMIC, the parameter we have to loop through
if param_name == 'keep_pct':
ga_keep_pct = param
mimic_keep_pct = param
elif param_name == 'pop_size':
ga_pop_size = int(param)
mimic_pop_size = int(param)
# For multiple random runs
for random_seed in random_seeds:
# Run GA and get best state and objective found
best_state, best_objective, _ = genetic_alg(
problem,
pop_size=ga_pop_size, # population size
pop_breed_percent=1.0 - ga_keep_pct, # percentage to breed
max_attempts=kwargs['ga_max_iters'], # unlimited attempts
max_iters=kwargs['ga_max_iters'],
curve=False,
random_state=random_seed)
ga_objectives.append(best_objective)
print('GA: best_objective = {:.3f}'.format(best_objective))
# Run MIMIC and get best state and objective found
best_state, best_objective, _ = mimic(
problem,
pop_size=mimic_pop_size, # population size
keep_pct=mimic_keep_pct, # percentage to keep
max_attempts=kwargs['mimic_max_iters'], # unlimited attempts
max_iters=kwargs['mimic_max_iters'],
curve=False,
random_state=random_seed)
mimic_objectives.append(best_objective)
print('MIMIC: best_fitness = {:.3f}'.format(best_objective))
# Append random run to GA and MIMIC curves
ga_curve.append(ga_objectives)
mimic_curve.append(mimic_objectives)
# Plot, set title and labels
plt.figure()
utils.plot_helper(x_axis=kwargs[param_name + 's'],
y_axis=np.array(ga_curve).T,
label='GA')
utils.plot_helper(x_axis=kwargs[param_name + 's'],
y_axis=np.array(mimic_curve).T,
label='MIMIC')
utils.set_plot_title_labels(title='{} - Objective vs. {}'.format(
kwargs['plot_name'], label),
x_label=label,
y_label=kwargs['plot_ylabel'])
# Save figure
plt.savefig(IMAGE_DIR +
'{}_objective_vs_{}'.format(kwargs['plot_name'], param_name))
def plot_performances(problem, random_seeds, **kwargs):
"""Plot performances for RHC, SA, GA and MIMIC.
Args:
problem (DiscreteOpt): mlrose Discrete Optimization problem to run.
random_seeds (list): random seeds to use for averaging results over multiple random runs.
kwargs (dict): additional arguments to pass for curves plotting:
- sa_init_temp (float): SA initial temperature.
- sa_min_temp (float): SA minimum temperature.
- sa_exp_decay_rate (float): SA temperature exponential decay rate.
- sa_max_iters (int): SA maximum number of iterations.
- pop_sizes (list or ndarray): lits or array of population sizes to objective plot vs.
- keep_pcts (list or ndarray): lits or array of keep percentages to objective plot vs.
- ga_pop_size (int): GA population size.
- ga_keep_pct (float): GA keep percentage.
- ga_max_iters (int): GA maximum number of iterations.
- mimic_pop_size (int): MIMIC population size.
- mimic_keep_pct (float): MIMIC keep percentage.
- mimic_max_iters (int): MIMIC maximum number of iterations.
- plot_name (string): name of the plot.
- plot_ylabel (string): y axis label.
Returns:
None.
"""
# Initialize lists of objectives curves and time curves
rhc_objectives, sa_objectives, ga_objectives, mimic_objectives = [], [], [], []
rhc_times, sa_times, ga_times, mimic_times = [], [], [], []
# Set an exponential decay schedule for SA
exp_decay = ExpDecay(init_temp=kwargs['sa_init_temp'],
exp_const=kwargs['sa_exp_decay_rate'],
min_temp=kwargs['sa_min_temp'])
# For multiple random runs
for random_seed in random_seeds:
# Run RHC and get best state and objective found
_, best_objective, objective_curve = random_hill_climb(
problem,
max_attempts=kwargs['rhc_max_iters'],
max_iters=kwargs['rhc_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
rhc_objectives.append(objective_curve)
rhc_times.append(utils.times)
print('\nRHC: best_objective = {:.3f}'.format(best_objective))
# Run SA and get best state and objective found
_, best_objective, objective_curve = simulated_annealing(
problem,
schedule=exp_decay,
max_attempts=kwargs['sa_max_iters'],
max_iters=kwargs['sa_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
sa_objectives.append(objective_curve)
sa_times.append(utils.times)
print('SA: best_objective = {:.3f}'.format(best_objective))
# Run GA and get best state and objective found
_, best_objective, objective_curve = genetic_alg(
problem,
pop_size=kwargs['ga_pop_size'],
pop_breed_percent=1.0 - kwargs['ga_keep_pct'],
max_attempts=kwargs['ga_max_iters'],
max_iters=kwargs['ga_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
ga_objectives.append(objective_curve)
ga_times.append(utils.times)
print('GA: best_objective = {:.3f}'.format(best_objective))
# Run MIMIC and get best state and objective found
_, best_objective, objective_curve = mimic(
problem,
pop_size=kwargs['mimic_pop_size'],
keep_pct=kwargs['mimic_keep_pct'],
max_attempts=kwargs['mimic_max_iters'],
max_iters=kwargs['mimic_max_iters'],
curve=True,
random_state=random_seed,
state_fitness_callback=utils.time_callback,
callback_user_info=[])
mimic_objectives.append(objective_curve)
mimic_times.append(utils.times)
print('MIMIC: best_objective = {:.3f}'.format(best_objective))
# Array of iterations to plot objectives vs. for RHC, SA, GA and MIMIC
rhc_iterations = np.arange(1, kwargs['rhc_max_iters'] + 1)
sa_iterations = np.arange(1, kwargs['sa_max_iters'] + 1)
ga_iterations = np.arange(1, kwargs['ga_max_iters'] + 1)
mimic_iterations = np.arange(1, kwargs['mimic_max_iters'] + 1)
# Plot objective curves, set title and labels
plt.figure()
utils.plot_helper(x_axis=rhc_iterations,
y_axis=np.array(rhc_objectives),
label='RHC')
utils.plot_helper(x_axis=sa_iterations,
y_axis=np.array(sa_objectives),
label='SA')
utils.plot_helper(x_axis=ga_iterations,
y_axis=np.array(ga_objectives),
label='GA')
utils.plot_helper(x_axis=mimic_iterations,
y_axis=np.array(mimic_objectives),
label='MIMIC')
utils.set_plot_title_labels(
title='{} - Objective versus iterations'.format(kwargs['plot_name']),
x_label='Iterations',
y_label=kwargs['plot_ylabel'])
# Save figure
plt.savefig(IMAGE_DIR +
'{}_objective_vs_iterations'.format(kwargs['plot_name']))
# Plot times, set title and labels
plt.figure()
utils.plot_helper(x_axis=rhc_iterations,
y_axis=np.array(rhc_times),
label='RHC')
utils.plot_helper(x_axis=sa_iterations,
y_axis=np.array(sa_times),
label='SA')
utils.plot_helper(x_axis=ga_iterations,
y_axis=np.array(ga_times),
label='GA')
utils.plot_helper(x_axis=mimic_iterations,
y_axis=np.array(mimic_times),
label='MIMIC')
utils.set_plot_title_labels(title='{} - Time versus iterations'.format(
kwargs['plot_name']),
x_label='Iterations',
y_label='Time (seconds)')
# Save figure
plt.savefig(IMAGE_DIR +
'{}_time_vs_iterations'.format(kwargs['plot_name']))
def plot_ga_mimic_optimization(problem, param_name, random_seeds, **kwargs):
"""Plot objective function vs parameter for GA and MIMIC.
Args:
problem (DiscreteOpt): mlrose Discrete Optimization problem to run.
param_name (string): GA and MIMIC param to plot objective vs, 'pop_size' or 'keep_pct'.
random_seeds (list): random seeds to use for averaging results over multiple random runs.
kwargs (dict): additional arguments to pass for curves plotting:
- pop_sizes (list or ndarray): lits or array of population sizes to objective plot vs.
- keep_pcts (list or ndarray): lits or array of keep percentages to objective plot vs.
- ga_pop_size (int): GA population size.
- ga_keep_pct (float): GA keep percentage.
- ga_max_iters (int): GA maximum number of iterations.
- mimic_pop_size (int): MIMIC population size.
- mimic_keep_pct (float): MIMIC keep percentage.
- mimic_max_iters (int): MIMIC maximum number of iterations.
- plot_name (string): name of the plot.
- plot_ylabel (string): y axis label.
Returns:
None.
"""
ga_curve, mimic_curve = [], [] # curves to plot for GA and MIMIC
# Initialize, for GA and MIMIC, the parameter we don't have to loop through and label for plotting
if param_name == 'keep_pct':
ga_pop_size = kwargs['ga_pop_size']
mimic_pop_size = kwargs['mimic_pop_size']
label = 'Percentage to keep'
elif param_name == 'pop_size':
ga_keep_pct = kwargs['ga_keep_pct']
mimic_keep_pct = kwargs['mimic_keep_pct']
label = 'Population size'
else:
raise Exception('Param name has to be either pop_size or keep_pct'
) # raise exception if invalid entry
# For all parameters
for param in kwargs[param_name + 's']:
print('\nGA & MIMIC: {} = {:.3f}'.format(param_name, param))
ga_objectives, mimic_objectives = [], [
] # list of results from multiple random runs for current parameter
# Initialize, for GA and MIMIC, the parameter we have to loop through
if param_name == 'keep_pct':
ga_keep_pct = param
mimic_keep_pct = param
elif param_name == 'pop_size':
ga_pop_size = int(param)
mimic_pop_size = int(param)
# For multiple random runs
for random_seed in random_seeds:
# Run GA and get best state and objective found
best_state, best_objective, _ = genetic_alg(
problem,
pop_size=ga_pop_size, # population size
pop_breed_percent=1.0 - ga_keep_pct, # percentage to breed
max_attempts=kwargs['ga_max_iters'], # unlimited attempts
max_iters=kwargs['ga_max_iters'],
curve=False,
random_state=random_seed)
ga_objectives.append(best_objective)
print('GA: best_objective = {:.3f}'.format(best_objective))
# Run MIMIC and get best state and objective found
best_state, best_objective, _ = mimic(
problem,
pop_size=mimic_pop_size, # population size
keep_pct=mimic_keep_pct, # percentage to keep
max_attempts=kwargs['mimic_max_iters'], # unlimited attempts
max_iters=kwargs['mimic_max_iters'],
curve=False,
random_state=random_seed)
mimic_objectives.append(best_objective)
print('MIMIC: best_objective = {:.3f}'.format(best_objective))
# Append random run to GA and MIMIC curves
ga_curve.append(ga_objectives)
mimic_curve.append(mimic_objectives)
# Plot, set title and labels
plt.figure()
utils.plot_helper(x_axis=kwargs[param_name + 's'],
y_axis=np.array(ga_curve).T,
label='GA')
utils.plot_helper(x_axis=kwargs[param_name + 's'],
y_axis=np.array(mimic_curve).T,
label='MIMIC')
utils.set_plot_title_labels(title='{} - Objective vs. {}'.format(
kwargs['plot_name'], label),
x_label=label,
y_label=kwargs['plot_ylabel'])
# Save figure
plt.savefig(IMAGE_DIR +
'{}_objective_vs_{}'.format(kwargs['plot_name'], param_name))
def plot_sa_optimization(problem, random_seeds, **kwargs):
"""Plot objective function vs temperature for SA.
Args:
problem (DiscreteOpt): mlrose Discrete Optimization problem to run.
random_seeds (list): random seeds to use for averaging results over multiple random runs.
kwargs (dict): additional arguments to pass for curves plotting:
- sa_decay_rates (list or ndarray): lits or array of exponential decay rates to objective plot vs.
- sa_init_temp (float): SA initial temperature.
- sa_min_temp (float): SA minimum temperature.
- sa_max_iters (int): SA maximum number of iterations.
- plot_name (string): name of the plot.
- plot_ylabel (string): y axis label.
Returns:
None.
"""
sa_curve = [] # curve to plot for SA
# For all temperature exponential decay rate
for exp_decay_rate in kwargs['sa_decay_rates']:
# Define exponential decay schedule
print('\nSA: exp decay rate = {:.3f}'.format(exp_decay_rate))
exp_decay = ExpDecay(init_temp=kwargs['sa_init_temp'],
exp_const=exp_decay_rate,
min_temp=kwargs['sa_min_temp'])
sa_objectives = [
] # list of results from multiple random runs for current rate
# For multiple random runs
for random_seed in random_seeds:
# Run SA and get best state and objective found
best_state, best_objective, _ = simulated_annealing(
problem,
schedule=exp_decay,
max_attempts=kwargs['sa_max_iters'],
max_iters=kwargs['sa_max_iters'],
curve=False,
random_state=random_seed)
sa_objectives.append(best_objective)
print('SA: best_fitness = {:.3f}'.format(best_objective))
sa_curve.append(sa_objectives) # append random run to SA curve
# Plot, set title and labels
plt.figure()
utils.plot_helper(x_axis=kwargs['sa_decay_rates'],
y_axis=np.array(sa_curve).T,
label='SA')
utils.set_plot_title_labels(
title='{} - Objective vs. temperature exponential decay rate'.format(
kwargs['plot_name']),
x_label='Exponential decay rate',
y_label=kwargs['plot_ylabel'])
# Save figure
plt.savefig(IMAGE_DIR + '{}_objective_vs_temp'.format(kwargs['plot_name']))