from opytimizer.utils.history import History # File name to be loaded file_name = '' # Creating an empty History object h = History() # Loading history from pickle file h.load(file_name) # Displaying content print(h)
# 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 preds = preds[:, 35:] # Gathering the best weights
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=[