def optimize_umda(target, n_agents, n_variables, n_iterations, hyperparams):
"""Abstracts Opytimizer's Univariate Marginal Distribution Algorithm into a single method.
Args:
target (callable): The method to be optimized.
n_agents (int): Number of agents.
n_variables (int): Number of variables.
n_iterations (int): Number of iterations.
hyperparams (dict): Dictionary of hyperparameters.
Returns:
A History object containing all optimization's information.
"""
# Creating the BooleanSpace
space = BooleanSpace(n_agents=n_agents,
n_iterations=n_iterations,
n_variables=n_variables)
# Creating the Function
function = Function(pointer=target)
# Creating UMDA's optimizer
optimizer = UMDA(hyperparams=hyperparams)
# Creating the optimization task
task = Opytimizer(space=space, optimizer=optimizer, function=function)
return task.start(store_best_only=True)
def optimize(opt, target, n_agents, n_iterations, hyperparams):
"""Abstracts all Opytimizer's mechanisms into a single method.
Args:
opt (Optimizer): An Optimizer-child class.
target (callable): The method to be optimized.
n_agents (int): Number of agents.
n_iterations (int): Number of iterations.
hyperparams (dict): Dictionary of hyperparameters.
Returns:
A History object containing all optimization's information.
"""
# Creating the SearchSpace
space = SearchSpace(n_agents=n_agents,
n_variables=1,
n_iterations=n_iterations,
lower_bound=[0.0001],
upper_bound=[1])
# Creating the Optimizer
optimizer = opt(hyperparams=hyperparams)
# Creating the Function
function = Function(pointer=target)
# Creating the optimization task
task = Opytimizer(space=space, optimizer=optimizer, function=function)
return task.start(store_best_only=True)
def optimize_gp(target, n_trees, n_terminals, n_variables, n_iterations,
min_depth, max_depth, functions, lb, ub, hyperparams):
"""Abstracts Opytimizer's Genetic Programming into a single method.
Args:
target (callable): The method to be optimized.
n_trees (int): Number of agents.
n_terminals (int): Number of terminals
n_variables (int): Number of variables.
n_iterations (int): Number of iterations.
min_depth (int): Minimum depth of trees.
max_depth (int): Maximum depth of trees.
functions (list): Functions' nodes.
lb (list): List of lower bounds.
ub (list): List of upper bounds.
hyperparams (dict): Dictionary of hyperparameters.
Returns:
A History object containing all optimization's information.
"""
# Creating the TreeSpace
space = TreeSpace(n_trees=n_trees,
n_terminals=n_terminals,
n_variables=n_variables,
n_iterations=n_iterations,
min_depth=min_depth,
max_depth=max_depth,
functions=functions,
lower_bound=lb,
upper_bound=ub)
# Creating the Function
function = Function(pointer=target)
# Creating GP's optimizer
optimizer = GP(hyperparams=hyperparams)
# Creating the optimization task
task = Opytimizer(space=space, optimizer=optimizer, function=function)
return task.start(store_best_only=True)
# Number of decision variables
n_variables = 1
# Number of running iterations
n_iterations = 100
# Lower and upper bounds (has to be the same size as n_variables)
lower_bound = [0.00001]
upper_bound = [10]
# Creating the SearchSpace class
s = SearchSpace(n_agents=n_agents, n_iterations=n_iterations,
n_variables=n_variables, lower_bound=lower_bound,
upper_bound=upper_bound)
# Hyperparameters for the optimizer
hyperparams = {
'w': 0.7,
'c1': 1.7,
'c2': 1.7
}
# Creating PSO's optimizer
p = PSO(hyperparams=hyperparams)
# Finally, we can create an Opytimizer class
o = Opytimizer(space=s, optimizer=p, function=f)
# Running the optimization task
history = o.start()
# Predicts new data
preds = opf.predict(X_val_selected)
# Calculates accuracy
acc = g.opf_accuracy(Y_val, preds)
return 1 - acc
# Number of agents and decision variables
n_agents = 5
n_variables = 64
# Parameters for the optimizer
params = {
'c1': r.generate_binary_random_number(size=(n_variables, 1)),
'c2': r.generate_binary_random_number(size=(n_variables, 1))
}
# Creates the space, optimizer and function
space = BooleanSpace(n_agents, n_variables)
optimizer = BPSO()
function = Function(supervised_opf_feature_selection)
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function)
# Runs the optimization task
opt.start(n_iterations=3)
cost += fit(model, loss, opt, X_train[:, start:end, :],
Y_train[start:end])
# Predicting samples from evaluating set
preds = predict(model, X_val)
# Calculates accuracy
acc = np.mean(preds == Y_val)
return 1 - acc
# Number of agents and decision variables
n_agents = 10
n_variables = 2
# Lower and upper bounds (has to be the same size as `n_variables`)
lower_bound = [0, 0]
upper_bound = [1, 1]
# Creates the space, optimizer and function
space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
optimizer = PSO()
function = Function(lstm)
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function)
# Runs the optimization task
opt.start(n_iterations=100)
from opytimark.markers.n_dimensional import Sphere from opytimizer import Opytimizer from opytimizer.core import Function from opytimizer.optimizers.misc import GS from opytimizer.spaces import GridSpace # Number of decision variables and step size of the grid n_variables = 2 step = [0.1, 1] # Lower and upper bounds (has to be the same size as `n_variables`) lower_bound = [-10, -10] upper_bound = [10, 10] # Creates the space, optimizer and function space = GridSpace(n_variables, step, lower_bound, upper_bound) optimizer = GS() function = Function(Sphere()) # Bundles every piece into Opytimizer class opt = Opytimizer(space, optimizer, function, save_agents=False) # Runs the optimization task opt.start()
n_iterations = 1000
# Lower and upper bounds (has to be the same size as n_variables)
lower_bound = [-10, -10]
upper_bound = [10, 10]
# Creating the SearchSpace class
s = SearchSpace(n_agents=n_agents, n_iterations=n_iterations,
n_variables=n_variables, lower_bound=lower_bound,
upper_bound=upper_bound)
# Hyperparameters for the optimizer
hyperparams = {
'alpha': 0.5,
'beta': 0.2,
'gamma': 1.0
}
# Creating FA's optimizer
p = FA(hyperparams=hyperparams)
# Defining task's main function
z = Multi(functions=[b.sphere, b.exponential],
weights=[0.5, 0.5], method='weight_sum')
# Finally, we can create an Opytimizer class
o = Opytimizer(space=s, optimizer=p, function=z)
# Running the optimization task
o.start(history=True)
from opytimizer.spaces import SearchSpace
from opytimizer.utils.callback import DiscreteSearchCallback
# Random seed for experimental consistency
np.random.seed(0)
# Number of agents and decision variables
n_agents = 20
n_variables = 2
# Lower and upper bounds (has to be the same size as `n_variables`)
lower_bound = [-10, -10]
upper_bound = [10, 10]
# Defines the allowed values for performing the discrete search
allowed_values = [
list(range(lb, ub, 2)) for lb, ub in zip(lower_bound, upper_bound)
]
# Creates the space, optimizer and function
space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
optimizer = PSO()
function = Function(Sphere())
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function, save_agents=False)
# Runs the optimization task
opt.start(n_iterations=5,
callbacks=[DiscreteSearchCallback(allowed_values=allowed_values)])
# Random seed for experimental consistency
np.random.seed(0)
# Number of agents and decision variables
n_agents = 20
n_variables = 2
# Lower and upper bounds (has to be the same size as `n_variables`)
lower_bound = [-10, -10]
upper_bound = [10, 10]
# Creates the space, optimizer and function
space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
optimizer = PSO()
function = Function(Sphere())
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function, save_agents=False)
# Runs the optimization task
opt.start(n_iterations=10, callbacks=[CheckpointCallback(frequency=10)])
# Deletes the optimization objecs
del opt
# Loads the task from file and resumes it
# Note that the following lines achieves the same results as a 35-iteration running
opt = Opytimizer.load('iter_10_checkpoint.pkl')
opt.start(n_iterations=25)
from opytimizer import Opytimizer from opytimizer.core import Function from opytimizer.optimizers.swarm import PSO from opytimizer.spaces import SearchSpace # Random seed for experimental consistency np.random.seed(0) # Number of agents and decision variables n_agents = 20 n_variables = 2 # Lower and upper bounds (has to be the same size as `n_variables`) lower_bound = [-10, -10] upper_bound = [10, 10] # Creates the space, optimizer and function space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound) optimizer = PSO() function = Function(Sphere()) # Bundles every piece into Opytimizer class opt = Opytimizer(space, optimizer, function, save_agents=False) # Runs the optimization task # Every call on `start` will the continue the optimization for `n_iterations` # Note that the following lines achieves the same results as a 100-iteration running opt.start(n_iterations=50) opt.start(n_iterations=25) opt.start(n_iterations=25)
momentum=0,
decay=0,
temperature=1,
dropout=dropout,
use_gpu=False)
# Training an RBM
error, _ = model.fit(train, batch_size=128, epochs=5)
return error
# Number of agents and decision variables
n_agents = 5
n_variables = 1
# Lower and upper bounds (has to be the same size as `n_variables`)
lower_bound = [0]
upper_bound = [1]
# Creates the space, optimizer and function
space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
optimizer = PSO()
function = Function(dropout_rbm)
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function)
# Runs the optimization task
opt.start(n_iterations=5)
