alpha = 0.3 USE_CACHE = False DUMP_CACHE = False cv = CrossValidationManager('RET') configurations = [ '{}{}'.format(cv.get_run_id(i)[0], cv.get_run_id(i)[1]) for i in ([0, 1, 3, 4, 7] if dset == 'Valid' else [2, 5, 6, 8, 9]) ] # [range(10)] #configurations = range(n_groups) # configurations = [1] dset = 'Valid' runs, run_net_types, run_metrics, run_epochs, run_names, _, _ = load_experiments( experiment_name) for m, metric_rating in enumerate(rating_metrics): Valid_epochs, Idx_malig_pearson, Idx_malig_kendall, Idx_rating_pearson, Idx_rating_kendall = [], [], [], [], [] for run, net_type, dist, epochs, metric in zip(runs, run_net_types, run_metrics, run_epochs, run_metrics): plot_data_filename = './Plots/Data/correlation_{}{}.p'.format( net_type, run) try: if USE_CACHE is False: print('NOTE: SKIPPING TO EVELUATION') assert False valid_epochs, idx_malig_pearson, idx_malig_kendall, idx_rating_pearson, idx_rating_kendall = \
if __name__ == "__main__": # Setup exp_name = 'DirSimilarityLoss' dset = 'Test' rating_norm = 'none' n_groups = 5 start = timer() num_of_indexes = 3 + 4 # cv = CrossValidationManager('RET') # configurations = ['{}{}'.format(cv.get_run_id(i)[0], cv.get_run_id(i)[1]) for i in [0, 1, 3, 4, 7]] # [range(10)] configurations = range(n_groups) # configurations = [1] runs, run_net_types, run_metrics, _, run_names, run_ep_perf, run_ep_comb = load_experiments( exp_name) data = np.zeros((len(runs), num_of_indexes)) dataStd = np.zeros((len(runs), num_of_indexes)) # evaluate run_id = 0 for run, net_type, _, metric, epochs in zip(runs, run_net_types, range(len(runs)), run_metrics, run_ep_perf): print("Evaluating classification accuracy for {}{}".format( net_type, run)) acc, acc_std, _ = eval_classification(run=run, net_type=net_type, dset=dset, metric=metric,
def plot_row(i, distances, label=None): tau, l_e = index.kumar(distances, res=0.01) conc = index.concentration(distances) contrast = index.relative_contrast_imp(distances) p[i].hist(distances.flatten() / np.mean(distances), bins=20) p[i].axes.title.set_text('std:{:.2f}, conc:{:.2f}, ctrst:{:.2f}'.format( conc[2], conc[0], contrast[0])) p[i].axes.yaxis.label.set_text('distribution') p[i].axes.xaxis.label.set_text('distances') Ret.pca(epoch=e, plt_=p[i + 1], label=label) p[i + 2].plot(l_e[1], l_e[0]) p[i + 2].axes.title.set_text('Kumari (tau = {:.2f}'.format(tau)) runs, run_net_types, run_metrics, run_epochs, run_names, _, _ = load_experiments( 'SiamRating') # evaluate Epochs, Idx_hubness, Idx_symmetry, Idx_concentration, Idx_contrast, Idx_kummar = [], [], [], [], [], [] for m, metric in enumerate(metrics): print("Begin: {} metric".format(metric)) for run, net_type, r, epochs, name in zip(runs, run_net_types, range(len(runs)), run_epochs, run_names): print("Evaluating run {}{}".format(net_type, run)) # initialize figures plt.figure("Distances - {}".format(name)) p = [None] * 9 for i in range(9):