def __init__(
self,
space: Space,
optimizer: Optimizer,
function: Function,
save_agents: Optional[bool] = False,
) -> None:
"""Initialization method.
Args:
space: Space-child instance.
optimizer: Optimizer-child instance.
function: Function or Function-child instance.
save_agents: Saves all agents in the search space.
"""
logger.info("Creating class: Opytimizer.")
# Space
self.space = space
# Optimizer (and its additional variables)
self.optimizer = optimizer
self.optimizer.compile(space)
# Function
self.function = function
# Optimization history
self.history = History(save_agents)
# Current iteration
self.iteration = 0
# Total number of iterations
self.total_iterations = 0
logger.debug(
"Space: %s | Optimizer: %s| Function: %s.",
self.space,
self.optimizer,
self.function,
)
logger.info("Class created.")
def __init__(self, space, optimizer, function, save_agents=False):
"""Initialization method.
Args:
space (Space): Space-child instance.
optimizer (Optimizer): Optimizer-child instance.
function (Function): Function or Function-child instance.
save_agents (bool): Saves all agents in the search space.
"""
logger.info('Creating class: Opytimizer.')
# Space
self.space = space
# Optimizer (and its additional variables)
self.optimizer = optimizer
self.optimizer.compile(space)
# Function
self.function = function
# Optimization history
self.history = History(save_agents)
# Current iteration
self.iteration = 0
# Total number of iterations
self.total_iterations = 0
# Logs the properties
logger.debug('Space: %s | Optimizer: %s| Function: %s.',
self.space, self.optimizer, self.function)
logger.info('Class created.')
if __name__ == '__main__':
# Gathers the input arguments
args = get_arguments()
# Gathering variables from arguments
dataset = args.dataset
descriptor = args.descriptor
fold = args.fold
type = args.type
meta = args.mh
# Defining an input file
input_file = f'output/{meta}_{type}_{dataset}_val_{fold}.pkl'
# Creating a History object
h = History()
# Loading the input file
h.load(input_file)
# Loading the predictions and labels
preds, y = l.load_candidates(dataset, 'test', fold)
# If descriptor is global-based
if descriptor == 'global':
# Gets the global predictors
preds = preds[:, :35]
# If descriptor is cnn-based
elif descriptor == 'cnn':
# Gets the CNN predictors
import numpy as np
import opytimizer.visualization.convergence as c
from opytimizer.utils.history import History
# Creating the history object
history = History()
# Loading saved optimization task
history.load('')
# Gathering desired keys from the object
# In this case, we will the first agent's position and fitness
agent_pos = history.get(key='agents', index=(0, 0))
agent_fit = history.get(key='agents', index=(0, 1))
# We will also gather the best agent's position and fitness
best_agent_pos = history.get(key='best_agent', index=(0,))
best_agent_fit = history.get(key='best_agent', index=(1,))
# Plotting convergence graphs
# Plotting the convergence of agent's positions
c.plot(agent_pos[0], agent_pos[1], labels=['$x_0$', '$x_1$'],
title='Sphere Function: $x^2 \mid x \in [-10, 10]$', subtitle='Agent: 0 | Algorithm: Particle Swarm Optimization')
# Plotting the convergence of best agent's positions
c.plot(best_agent_pos[0], best_agent_pos[1], labels=['$x^*_0$', '$x^*_1$'],
title='Sphere Function: $x^2 \mid x \in [-10, 10]$', subtitle="Agent: Best | Algorithm: Particle Swarm Optimization")
# Plotting the convergence of agent's and best agent's fitness
c.plot(agent_fit, best_agent_fit, labels=[