rewards = colorPerEpisode(training_data['episode_starts']) # Compute Ground Truth Correlation gt_corr, gt_corr_mean = plotCorrelation(states_rewards, ground_truth, target_positions, only_print=args.print_corr) result_dict = { 'gt_corr': gt_corr.tolist(), 'gt_corr_mean': gt_corr_mean } # Write the results in a json file log_folder = os.path.dirname(args.input_file) with open("{}/gt_correlation.json".format(log_folder), 'w') as f: json.dump(result_dict, f) else: plotRepresentation(states_rewards['states'], rewards, cmap=cmap) if not args.print_corr: getInputBuiltin()('\nPress any key to exit.') elif args.data_folder != "": print("Plotting ground truth...") training_data, ground_truth, true_states, _ = loadData(args.data_folder) rewards = training_data['rewards'] name = "Ground Truth States - {}".format(args.data_folder) if args.color_episode: rewards = colorPerEpisode(training_data['episode_starts']) if args.plot_against: plotAgainst(true_states, rewards, cmap=cmap) elif args.pretty_plot_against:
parser.add_argument('--no-display-plots', action='store_true', default=False, help='disables live plots of the representation learned') parser.add_argument('--data-folder', type=str, default="", help='Dataset folder', required=True) parser.add_argument('--training-set-size', type=int, default=-1, help='Limit size of the training set (default: -1)') parser.add_argument('--state-dim', type=int, default=3, help='State dimension') input = getInputBuiltin() args = parser.parse_args() DISPLAY_PLOTS = not args.no_display_plots plot_script.INTERACTIVE_PLOT = DISPLAY_PLOTS args.data_folder = parseDataFolder(args.data_folder) args.method = "pca" log_folder = "logs/{}/baselines/{}".format(args.data_folder, getModelName(args)) createFolder(log_folder, "{} folder already exist".format(args.method)) folder_path = '{}/NearestNeighbors/'.format(log_folder) createFolder(folder_path, "NearestNeighbors folder already exist") saveExpConfig(args, log_folder) print('Log folder: {}'.format(log_folder))