def compute_average_histograms(self): ''' Do per subject and nitems, get average histogram ''' angle_space = np.linspace(-np.pi, np.pi, 51) self.dataset['hist_cnts_target_subject_nitems'] = np.empty((self.dataset['subject_size'], self.dataset['n_items_size'], angle_space.size - 1))*np.nan self.dataset['hist_cnts_nontarget_subject_nitems'] = np.empty((self.dataset['subject_size'], self.dataset['n_items_size'], angle_space.size - 1))*np.nan self.dataset['pvalue_nontarget_subject_nitems'] = np.empty((self.dataset['subject_size'], self.dataset['n_items_size']))*np.nan for subject_i, subject in enumerate(np.unique(self.dataset['subject'])): for n_items_i, n_items in enumerate(np.unique(self.dataset['n_items'])): self.dataset['hist_cnts_target_subject_nitems'][subject_i, n_items_i], x, bins = utils.histogram_binspace(utils.dropnan(self.dataset['errors_subject_nitems'][subject_i, n_items_i]), bins=angle_space, norm='density') self.dataset['hist_cnts_nontarget_subject_nitems'][subject_i, n_items_i], x, bins = utils.histogram_binspace(utils.dropnan(self.dataset['errors_nontarget_subject_nitems'][subject_i, n_items_i]), bins=angle_space, norm='density') if n_items > 1: self.dataset['pvalue_nontarget_subject_nitems'][subject_i, n_items_i] = utils.V_test(utils.dropnan(self.dataset['errors_nontarget_subject_nitems'][subject_i, n_items_i]).flatten())['pvalue'] self.dataset['hist_cnts_target_nitems_stats'] = dict(mean=np.mean(self.dataset['hist_cnts_target_subject_nitems'], axis=0), std=np.std(self.dataset['hist_cnts_target_subject_nitems'], axis=0), sem=np.std(self.dataset['hist_cnts_target_subject_nitems'], axis=0)/np.sqrt(self.dataset['subject_size'])) self.dataset['hist_cnts_nontarget_nitems_stats'] = dict(mean=np.mean(self.dataset['hist_cnts_nontarget_subject_nitems'], axis=0), std=np.std(self.dataset['hist_cnts_nontarget_subject_nitems'], axis=0), sem=np.std(self.dataset['hist_cnts_nontarget_subject_nitems'], axis=0)/np.sqrt(self.dataset['subject_size']))
def plots_memory_curves(data_pbs, generator_module=None): """ Reload and plot memory curve of a Mixed code. Can use Marginal Fisher Information and fitted Mixture Model as well """ #### SETUP # savefigs = True savedata = True do_error_distrib_fits = True plot_pcolor_fit_precision_to_fisherinfo = True plot_selected_memory_curves = False plot_best_memory_curves = False plot_best_error_distrib = True colormap = None # or 'cubehelix' plt.rcParams["font.size"] = 16 # #### /SETUP print "Order parameters: ", generator_module.dict_parameters_range.keys() result_all_precisions_mean = utils.nanmean(data_pbs.dict_arrays["result_all_precisions"]["results"], axis=-1) result_all_precisions_std = utils.nanstd(data_pbs.dict_arrays["result_all_precisions"]["results"], axis=-1) result_em_fits_mean = utils.nanmean(data_pbs.dict_arrays["result_em_fits"]["results"], axis=-1) result_em_fits_std = utils.nanstd(data_pbs.dict_arrays["result_em_fits"]["results"], axis=-1) result_marginal_inv_fi_mean = utils.nanmean(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1) result_marginal_inv_fi_std = utils.nanstd(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1) result_marginal_fi_mean = utils.nanmean(1.0 / data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1) result_marginal_fi_std = utils.nanstd(1.0 / data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1) result_responses_all = data_pbs.dict_arrays["result_responses"]["results"] result_target_all = data_pbs.dict_arrays["result_target"]["results"] result_nontargets_all = data_pbs.dict_arrays["result_nontargets"]["results"] M_space = data_pbs.loaded_data["parameters_uniques"]["M"].astype(int) sigmax_space = data_pbs.loaded_data["parameters_uniques"]["sigmax"] T_space = data_pbs.loaded_data["datasets_list"][0]["T_space"] nb_repetitions = result_responses_all.shape[-1] print M_space print sigmax_space print T_space print result_all_precisions_mean.shape, result_em_fits_mean.shape, result_marginal_inv_fi_mean.shape dataio = DataIO.DataIO( output_folder=generator_module.pbs_submission_infos["simul_out_dir"] + "/outputs/", label="global_" + dataset_infos["save_output_filename"], ) ## Load Experimental data experim_datadir = os.environ.get("WORKDIR_DROP", os.path.split(load_experimental_data.__file__)[0]) data_simult = load_experimental_data.load_data_simult( data_dir=os.path.normpath(os.path.join(experim_datadir, "../../experimental_data/")), fit_mixture_model=True ) gorgo11_experimental_precision = data_simult["precision_nitems_theo"] gorgo11_experimental_kappa = np.array([data["kappa"] for _, data in data_simult["em_fits_nitems"]["mean"].items()]) gorgo11_experimental_emfits_mean = np.array( [ [data[key] for _, data in data_simult["em_fits_nitems"]["mean"].items()] for key in ["kappa", "mixt_target", "mixt_nontargets", "mixt_random"] ] ) gorgo11_experimental_emfits_std = np.array( [ [data[key] for _, data in data_simult["em_fits_nitems"]["std"].items()] for key in ["kappa", "mixt_target", "mixt_nontargets", "mixt_random"] ] ) gorgo11_experimental_emfits_sem = gorgo11_experimental_emfits_std / np.sqrt(np.unique(data_simult["subject"]).size) experim_datadir = os.environ.get("WORKDIR_DROP", os.path.split(load_experimental_data.__file__)[0]) data_bays2009 = load_experimental_data.load_data_bays09( data_dir=os.path.normpath(os.path.join(experim_datadir, "../../experimental_data/")), fit_mixture_model=True ) bays09_experimental_mixtures_mean = data_bays2009["em_fits_nitems_arrays"]["mean"] bays09_experimental_mixtures_std = data_bays2009["em_fits_nitems_arrays"]["std"] # add interpolated points for 3 and 5 items emfit_mean_intpfct = spint.interp1d(np.unique(data_bays2009["n_items"]), bays09_experimental_mixtures_mean) bays09_experimental_mixtures_mean_compatible = emfit_mean_intpfct(np.arange(1, 7)) emfit_std_intpfct = spint.interp1d(np.unique(data_bays2009["n_items"]), bays09_experimental_mixtures_std) bays09_experimental_mixtures_std_compatible = emfit_std_intpfct(np.arange(1, 7)) T_space_bays09 = np.arange(1, 6) # Boost non-targets # bays09_experimental_mixtures_mean_compatible[1] *= 1.5 # bays09_experimental_mixtures_mean_compatible[2] /= 1.5 # bays09_experimental_mixtures_mean_compatible /= np.sum(bays09_experimental_mixtures_mean_compatible, axis=0) # Compute some landscapes of fit! # dist_diff_precision_margfi = np.sum(np.abs(result_all_precisions_mean*2. - result_marginal_fi_mean[..., 0])**2., axis=-1) # dist_ratio_precision_margfi = np.sum(np.abs((result_all_precisions_mean*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1) # dist_diff_emkappa_margfi = np.sum(np.abs(result_em_fits_mean[..., 0]*2. - result_marginal_fi_mean[..., 0])**2., axis=-1) # dist_ratio_emkappa_margfi = np.sum(np.abs((result_em_fits_mean[..., 0]*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1) dist_diff_precision_experim = np.sum( np.abs(result_all_precisions_mean[..., : gorgo11_experimental_kappa.size] - gorgo11_experimental_precision) ** 2.0, axis=-1, ) dist_diff_emkappa_experim = np.sum( np.abs(result_em_fits_mean[..., 0, : gorgo11_experimental_kappa.size] - gorgo11_experimental_kappa) ** 2.0, axis=-1, ) dist_diff_em_mixtures_bays09 = np.sum( np.sum((result_em_fits_mean[..., 1:4] - bays09_experimental_mixtures_mean_compatible[1:].T) ** 2.0, axis=-1), axis=-1, ) dist_diff_modelfits_experfits_bays09 = np.sum( np.sum((result_em_fits_mean[..., :4] - bays09_experimental_mixtures_mean_compatible.T) ** 2.0, axis=-1), axis=-1 ) if do_error_distrib_fits: print "computing error distribution histograms fits" # Now try to fit histograms of errors to target/nontargets bays09_hist_target_mean = data_bays2009["hist_cnts_target_nitems_stats"]["mean"] bays09_hist_target_std = data_bays2009["hist_cnts_target_nitems_stats"]["std"] bays09_hist_nontarget_mean = data_bays2009["hist_cnts_nontarget_nitems_stats"]["mean"] bays09_hist_nontarget_std = data_bays2009["hist_cnts_nontarget_nitems_stats"]["std"] T_space_bays09_filt = np.unique(data_bays2009["n_items"]) angle_space = np.linspace(-np.pi, np.pi, bays09_hist_target_mean.shape[-1] + 1) bins_center = angle_space[:-1] + np.diff(angle_space)[0] / 2 errors_targets = utils.wrap_angles(result_responses_all - result_target_all) hist_targets_all = np.empty( (M_space.size, sigmax_space.size, T_space_bays09_filt.size, angle_space.size - 1, nb_repetitions) ) errors_nontargets = np.nan * np.empty(result_nontargets_all.shape) hist_nontargets_all = np.empty( (M_space.size, sigmax_space.size, T_space_bays09_filt.size, angle_space.size - 1, nb_repetitions) ) for M_i, M in enumerate(M_space): for sigmax_i, sigmax in enumerate(sigmax_space): for T_bays_i, T_bays in enumerate(T_space_bays09_filt): for repet_i in xrange(nb_repetitions): # Could do a nicer indexing but f**k it # Histogram errors to targets hist_targets_all[M_i, sigmax_i, T_bays_i, :, repet_i], x, bins = utils.histogram_binspace( utils.dropnan(errors_targets[M_i, sigmax_i, T_bays - 1, ..., repet_i]), bins=angle_space, norm="density", ) # Compute the error between the responses and nontargets. errors_nontargets[M_i, sigmax_i, T_bays - 1, :, :, repet_i] = utils.wrap_angles( ( result_responses_all[M_i, sigmax_i, T_bays - 1, :, repet_i, np.newaxis] - result_nontargets_all[M_i, sigmax_i, T_bays - 1, :, :, repet_i] ) ) # Histogram it hist_nontargets_all[M_i, sigmax_i, T_bays_i, :, repet_i], x, bins = utils.histogram_binspace( utils.dropnan(errors_nontargets[M_i, sigmax_i, T_bays - 1, ..., repet_i]), bins=angle_space, norm="density", ) hist_targets_mean = utils.nanmean(hist_targets_all, axis=-1).filled(np.nan) hist_targets_std = utils.nanstd(hist_targets_all, axis=-1).filled(np.nan) hist_nontargets_mean = utils.nanmean(hist_nontargets_all, axis=-1).filled(np.nan) hist_nontargets_std = utils.nanstd(hist_nontargets_all, axis=-1).filled(np.nan) # Compute distances to experimental histograms dist_diff_hist_target_bays09 = np.nansum( np.nansum((hist_targets_mean - bays09_hist_target_mean) ** 2.0, axis=-1), axis=-1 ) dist_diff_hist_nontargets_bays09 = np.nansum( np.nansum((hist_nontargets_mean - bays09_hist_nontarget_mean) ** 2.0, axis=-1), axis=-1 ) dist_diff_hist_nontargets_5_6items_bays09 = np.nansum( np.nansum((hist_nontargets_mean[:, :, -2:] - bays09_hist_nontarget_mean[-2:]) ** 2.0, axis=-1), axis=-1 ) if plot_pcolor_fit_precision_to_fisherinfo: # Check fit between precision and Experiments utils.pcolor_2d_data( dist_diff_precision_experim, log_scale=True, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", xlabel_format="%d", ) if savefigs: dataio.save_current_figure("match_precision_exper_log_pcolor_{label}_{unique_id}.pdf") utils.pcolor_2d_data( dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", xlabel_format="%d" ) if savefigs: dataio.save_current_figure("match_emkappa_model_exper_pcolor_{label}_{unique_id}.pdf") utils.pcolor_2d_data( dist_diff_em_mixtures_bays09, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", log_scale=True, xlabel_format="%d", ) if savefigs: dataio.save_current_figure("match_emmixtures_experbays09_log_pcolor_{label}_{unique_id}.pdf") utils.pcolor_2d_data( dist_diff_modelfits_experfits_bays09, log_scale=True, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", xlabel_format="%d", ) if savefigs: dataio.save_current_figure("match_diff_emfits_experbays09_pcolor_{label}_{unique_id}.pdf") if do_error_distrib_fits: utils.pcolor_2d_data( dist_diff_hist_target_bays09, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", log_scale=True, xlabel_format="%d", ) if savefigs: dataio.save_current_figure("match_hist_targets_experbays09_log_pcolor_{label}_{unique_id}.pdf") utils.pcolor_2d_data( dist_diff_hist_nontargets_bays09, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", log_scale=True, xlabel_format="%d", ) if savefigs: dataio.save_current_figure("match_hist_nontargets_experbays09_log_pcolor_{label}_{unique_id}.pdf") utils.pcolor_2d_data( dist_diff_hist_nontargets_5_6items_bays09, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", log_scale=True, xlabel_format="%d", ) if savefigs: dataio.save_current_figure( "match_hist_nontargets_6items_experbays09_log_pcolor_{label}_{unique_id}.pdf" ) # Macro plot def mem_plot_precision(sigmax_i, M_i, mem_exp_prec): ax = utils.plot_mean_std_area( T_space[: mem_exp_prec.size], mem_exp_prec, np.zeros(mem_exp_prec.size), linewidth=3, fmt="o-", markersize=8, label="Experimental data", ) ax = utils.plot_mean_std_area( T_space[: mem_exp_prec.size], result_all_precisions_mean[M_i, sigmax_i, : mem_exp_prec.size], result_all_precisions_std[M_i, sigmax_i, : mem_exp_prec.size], ax_handle=ax, linewidth=3, fmt="o-", markersize=8, label="Precision of samples", ) # ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][M_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information') # ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][M_i, sigmax_i], result_em_fits_std[..., 0][M_i, sigmax_i], ax_handle=ax, xlabel='Number of items', ylabel="Inverse variance $[rad^{-2}]$", linewidth=3, fmt='o-', markersize=8, label='Fitted kappa') ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i])) ax.legend() ax.set_xlim([0.9, mem_exp_prec.size + 0.1]) ax.set_xticks(range(1, mem_exp_prec.size + 1)) ax.set_xticklabels(range(1, mem_exp_prec.size + 1)) if savefigs: dataio.save_current_figure( "memorycurves_precision_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i]) ) def mem_plot_kappa(sigmax_i, M_i, exp_kappa_mean, exp_kappa_std=None): ax = utils.plot_mean_std_area( T_space[: exp_kappa_mean.size], exp_kappa_mean, exp_kappa_std, linewidth=3, fmt="o-", markersize=8, label="Experimental data", ) ax = utils.plot_mean_std_area( T_space[: exp_kappa_mean.size], result_em_fits_mean[..., : exp_kappa_mean.size, 0][M_i, sigmax_i], result_em_fits_std[..., : exp_kappa_mean.size, 0][M_i, sigmax_i], xlabel="Number of items", ylabel="Memory error $[rad^{-2}]$", linewidth=3, fmt="o-", markersize=8, label="Fitted kappa", ax_handle=ax, ) # ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][M_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information') ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i])) ax.legend() ax.set_xlim([0.9, exp_kappa_mean.size + 0.1]) ax.set_xticks(range(1, exp_kappa_mean.size + 1)) ax.set_xticklabels(range(1, exp_kappa_mean.size + 1)) ax.get_figure().canvas.draw() if savefigs: dataio.save_current_figure( "memorycurves_kappa_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i]) ) def em_plot(sigmax_i, M_i): f, ax = plt.subplots() ax2 = ax.twinx() # left axis, kappa ax = utils.plot_mean_std_area( T_space, result_em_fits_mean[..., 0][M_i, sigmax_i], result_em_fits_std[..., 0][M_i, sigmax_i], xlabel="Number of items", ylabel="Inverse variance $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt="o-", markersize=8, label="Fitted kappa", color="k", ) # Right axis, mixture probabilities utils.plot_mean_std_area( T_space, result_em_fits_mean[..., 1][M_i, sigmax_i], result_em_fits_std[..., 1][M_i, sigmax_i], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt="o-", markersize=8, label="Target", ) utils.plot_mean_std_area( T_space, result_em_fits_mean[..., 2][M_i, sigmax_i], result_em_fits_std[..., 2][M_i, sigmax_i], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt="o-", markersize=8, label="Nontarget", ) utils.plot_mean_std_area( T_space, result_em_fits_mean[..., 3][M_i, sigmax_i], result_em_fits_std[..., 3][M_i, sigmax_i], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt="o-", markersize=8, label="Random", ) lines, labels = ax.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax.legend(lines + lines2, labels + labels2) ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i])) ax.set_xlim([0.9, T_space.size]) ax.set_xticks(range(1, T_space.size + 1)) ax.set_xticklabels(range(1, T_space.size + 1)) f.canvas.draw() if savefigs: dataio.save_current_figure( "memorycurves_emfits_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i]) ) def em_plot_paper(sigmax_i, M_i): f, ax = plt.subplots() # Right axis, mixture probabilities utils.plot_mean_std_area( T_space_bays09, result_em_fits_mean[..., 1][M_i, sigmax_i][: T_space_bays09.size], result_em_fits_std[..., 1][M_i, sigmax_i][: T_space_bays09.size], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt="o-", markersize=5, label="Target", ) utils.plot_mean_std_area( T_space_bays09, result_em_fits_mean[..., 2][M_i, sigmax_i][: T_space_bays09.size], result_em_fits_std[..., 2][M_i, sigmax_i][: T_space_bays09.size], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt="o-", markersize=5, label="Nontarget", ) utils.plot_mean_std_area( T_space_bays09, result_em_fits_mean[..., 3][M_i, sigmax_i][: T_space_bays09.size], result_em_fits_std[..., 3][M_i, sigmax_i][: T_space_bays09.size], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt="o-", markersize=5, label="Random", ) ax.legend(prop={"size": 15}) ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i])) ax.set_xlim([1.0, T_space_bays09.size]) ax.set_ylim([0.0, 1.1]) ax.set_xticks(range(1, T_space_bays09.size + 1)) ax.set_xticklabels(range(1, T_space_bays09.size + 1)) f.canvas.draw() if savefigs: dataio.save_current_figure( "memorycurves_emfits_paper_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i]) ) def hist_errors_targets_nontargets(hists_toplot_mean, hists_toplot_std, title="", M=0, sigmax=0, yaxis_lim="auto"): f1, axes1 = plt.subplots( ncols=hists_toplot_mean.shape[-2], figsize=(hists_toplot_mean.shape[-2] * 6, 6), sharey=True ) for T_bays_i, T_bays in enumerate(T_space_bays09_filt): if not np.all(np.isnan(hists_toplot_mean[T_bays_i])): axes1[T_bays_i].bar( bins_center, hists_toplot_mean[T_bays_i], width=2.0 * np.pi / (angle_space.size - 1), align="center", yerr=hists_toplot_std[T_bays_i], ) axes1[T_bays_i].set_title("N=%d" % T_bays) # axes1[T_{}] axes1[T_bays_i].set_xlim( [bins_center[0] - np.pi / (angle_space.size - 1), bins_center[-1] + np.pi / (angle_space.size - 1)] ) if yaxis_lim == "target": axes1[T_bays_i].set_ylim([0.0, 2.0]) elif yaxis_lim == "nontarget": axes1[T_bays_i].set_ylim([0.0, 0.3]) else: axes1[T_bays_i].set_ylim([0.0, np.nanmax(hists_toplot_mean + hists_toplot_std) * 1.1]) axes1[T_bays_i].set_xticks((-np.pi, -np.pi / 2, 0, np.pi / 2.0, np.pi)) axes1[T_bays_i].set_xticklabels( (r"$-\pi$", r"$-\frac{\pi}{2}$", r"$0$", r"$\frac{\pi}{2}$", r"$\pi$"), fontsize=16 ) f1.canvas.draw() if savefigs: dataio.save_current_figure( "memorycurves_hist_%s_paper_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (title, M, sigmax) ) ################################# if plot_selected_memory_curves: selected_values = [[0.84, 0.23], [0.84, 0.19]] for current_values in selected_values: # Find the indices M_i = np.argmin(np.abs(current_values[0] - M_space)) sigmax_i = np.argmin(np.abs(current_values[1] - sigmax_space)) mem_plot_precision(sigmax_i, M_i) mem_plot_kappa(sigmax_i, M_i) if plot_best_memory_curves: # Best precision fit best_axis2_i_all = np.argmin(dist_diff_precision_experim, axis=1) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): mem_plot_precision(best_axis2_i, axis1_i, gorgo11_experimental_precision) # Best kappa fit best_axis2_i_all = np.argmin(dist_diff_emkappa_experim, axis=1) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): mem_plot_kappa( best_axis2_i, axis1_i, gorgo11_experimental_emfits_mean[0], gorgo11_experimental_emfits_std[0] ) # em_plot(best_axis2_i, axis1_i) # Best em parameters fit to Bays09 best_axis2_i_all = np.argmin(dist_diff_modelfits_experfits_bays09, axis=1) # best_axis2_i_all = np.argmin(dist_diff_em_mixtures_bays09, axis=1) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): mem_plot_kappa( best_axis2_i, axis1_i, bays09_experimental_mixtures_mean_compatible[0, : T_space_bays09.size], bays09_experimental_mixtures_std_compatible[0, : T_space_bays09.size], ) # em_plot(best_axis2_i, axis1_i) em_plot_paper(best_axis2_i, axis1_i) if plot_best_error_distrib and do_error_distrib_fits: # Best target histograms best_axis2_i_all = np.argmin(dist_diff_hist_target_bays09, axis=1) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): hist_errors_targets_nontargets( hist_targets_mean[axis1_i, best_axis2_i], hist_targets_std[axis1_i, best_axis2_i], "target", M=M_space[axis1_i], sigmax=sigmax_space[best_axis2_i], yaxis_lim="target", ) # Best nontarget histograms best_axis2_i_all = np.argmin(dist_diff_hist_nontargets_bays09, axis=1) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): hist_errors_targets_nontargets( hist_nontargets_mean[axis1_i, best_axis2_i], hist_nontargets_std[axis1_i, best_axis2_i], "nontarget", M=M_space[axis1_i], sigmax=sigmax_space[best_axis2_i], yaxis_lim="nontarget", ) all_args = data_pbs.loaded_data["args_list"] variables_to_save = [ "gorgo11_experimental_precision", "gorgo11_experimental_kappa", "bays09_experimental_mixtures_mean_compatible", "T_space", ] if savedata: dataio.save_variables_default(locals(), variables_to_save) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder="memory_curves") plt.show() return locals()
def compute_bootstrap_samples(dataset, nb_bootstrap_samples, angle_space): responses_resampled = np.empty( (np.unique(dataset['n_items']).size, nb_bootstrap_samples), dtype=np.object) error_nontargets_resampled = np.empty( (np.unique(dataset['n_items']).size, nb_bootstrap_samples), dtype=np.object) error_targets_resampled = np.empty( (np.unique(dataset['n_items']).size, nb_bootstrap_samples), dtype=np.object) hist_cnts_nontarget_bootstraps_nitems = np.empty( (np.unique(dataset['n_items']).size, nb_bootstrap_samples, angle_space.size - 1))*np.nan hist_cnts_target_bootstraps_nitems = np.empty( (np.unique(dataset['n_items']).size, nb_bootstrap_samples, angle_space.size - 1))*np.nan bootstrap_data = { 'responses_resampled': responses_resampled, 'error_nontargets_resampled': error_nontargets_resampled, 'error_targets_resampled': error_targets_resampled, 'hist_cnts_nontarget_bootstraps_nitems': hist_cnts_nontarget_bootstraps_nitems, 'hist_cnts_target_bootstraps_nitems': hist_cnts_target_bootstraps_nitems, } for n_items_i, n_items in enumerate(np.unique(dataset['n_items'])): # Data collapsed accross subjects ids_filtered = (dataset['n_items'] == n_items).flatten() if n_items > 1: # Get random bootstrap nontargets bootstrap_nontargets = utils.sample_angle( dataset['item_angle'][ids_filtered, 1:n_items].shape + (nb_bootstrap_samples, )) # Compute associated EM fits # bootstrap_results = [] for bootstrap_i in progress.ProgressDisplay(np.arange(nb_bootstrap_samples), display=progress.SINGLE_LINE): em_fit = em_circularmixture.fit( dataset['response'][ids_filtered, 0], dataset['item_angle'][ids_filtered, 0], bootstrap_nontargets[..., bootstrap_i]) # bootstrap_results.append(em_fit) # Get EM samples responses_resampled[n_items_i, bootstrap_i] = ( em_circularmixture.sample_from_fit( em_fit, dataset['item_angle'][ids_filtered, 0], bootstrap_nontargets[..., bootstrap_i])) # Compute the errors error_nontargets_resampled[n_items_i, bootstrap_i] = ( utils.wrap_angles( responses_resampled[n_items_i, bootstrap_i][:, np.newaxis] - bootstrap_nontargets[..., bootstrap_i])) error_targets_resampled[n_items_i, bootstrap_i] = ( utils.wrap_angles( responses_resampled[n_items_i, bootstrap_i] - dataset['item_angle'][ids_filtered, 0])) # Bin everything (hist_cnts_nontarget_bootstraps_nitems[n_items_i, bootstrap_i], _, _) = ( utils.histogram_binspace( utils.dropnan( error_nontargets_resampled[n_items_i, bootstrap_i]), bins=angle_space, norm='density')) (hist_cnts_target_bootstraps_nitems[n_items_i, bootstrap_i], _, _) = ( utils.histogram_binspace( utils.dropnan( error_targets_resampled[n_items_i, bootstrap_i]), bins=angle_space, norm='density')) return bootstrap_data
def plots_misbinding_logposterior(data_pbs, generator_module=None): ''' Reload 3D volume runs from PBS and plot them ''' #### SETUP # savedata = False savefigs = True plot_logpost = False plot_error = False plot_mixtmodel = True plot_hist_responses_fisherinfo = True compute_plot_bootstrap = False compute_fisher_info_perratioconj = True # mixturemodel_to_use = 'original' mixturemodel_to_use = 'allitems' # mixturemodel_to_use = 'allitems_kappafi' caching_fisherinfo_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_fisherinfo.pickle') # #### /SETUP print "Order parameters: ", generator_module.dict_parameters_range.keys() result_all_log_posterior = np.squeeze(data_pbs.dict_arrays['result_all_log_posterior']['results']) result_all_thetas = np.squeeze(data_pbs.dict_arrays['result_all_thetas']['results']) ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj'] print ratio_space print result_all_log_posterior.shape N = result_all_thetas.shape[-1] result_prob_wrong = np.zeros((ratio_space.size, N)) result_em_fits = np.empty((ratio_space.size, 6))*np.nan all_args = data_pbs.loaded_data['args_list'] fixed_means = [-np.pi*0.6, np.pi*0.6] all_angles = np.linspace(-np.pi, np.pi, result_all_log_posterior.shape[-1]) dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) plt.rcParams['font.size'] = 18 if plot_hist_responses_fisherinfo: # From cache if caching_fisherinfo_filename is not None: if os.path.exists(caching_fisherinfo_filename): # Got file, open it and try to use its contents try: with open(caching_fisherinfo_filename, 'r') as file_in: # Load and assign values cached_data = pickle.load(file_in) result_fisherinfo_ratio = cached_data['result_fisherinfo_ratio'] compute_fisher_info_perratioconj = False except IOError: print "Error while loading ", caching_fisherinfo_filename, "falling back to computing the Fisher Info" if compute_fisher_info_perratioconj: # We did not save the Fisher info, but need it if we want to fit the mixture model with fixed kappa. So recompute them using the args_dicts result_fisherinfo_ratio = np.empty(ratio_space.shape) # Invert the all_args_i -> ratio_conj direction parameters_indirections = data_pbs.loaded_data['parameters_dataset_index'] for ratio_conj_i, ratio_conj in enumerate(ratio_space): # Get index of first dataset with the current ratio_conj (no need for the others, I think) arg_index = parameters_indirections[(ratio_conj,)][0] # Now using this dataset, reconstruct a RandomFactorialNetwork and compute the fisher info curr_args = all_args[arg_index] curr_args['stimuli_generation'] = lambda T: np.linspace(-np.pi*0.6, np.pi*0.6, T) (random_network, data_gen, stat_meas, sampler) = launchers.init_everything(curr_args) # Theo Fisher info result_fisherinfo_ratio[ratio_conj_i] = sampler.estimate_fisher_info_theocov() del curr_args['stimuli_generation'] # Save everything to a file, for faster later plotting if caching_fisherinfo_filename is not None: try: with open(caching_fisherinfo_filename, 'w') as filecache_out: data_cache = dict(result_fisherinfo_ratio=result_fisherinfo_ratio) pickle.dump(data_cache, filecache_out, protocol=2) except IOError: print "Error writing out to caching file ", caching_fisherinfo_filename # Now plots. Do histograms of responses (around -pi/6 and pi/6), add Von Mises derived from Theo FI on top, and vertical lines for the correct target/nontarget angles. for ratio_conj_i, ratio_conj in enumerate(ratio_space): # Histogram ax = utils.hist_angular_data(result_all_thetas[ratio_conj_i], bins=100, title='ratio %.2f, fi %.0f' % (ratio_conj, result_fisherinfo_ratio[ratio_conj_i])) bar_heights, _, _ = utils.histogram_binspace(result_all_thetas[ratio_conj_i], bins=100, norm='density') # Add Fisher info prediction on top x = np.linspace(-np.pi, np.pi, 1000) if result_fisherinfo_ratio[ratio_conj_i] < 700: # Von Mises PDF utils.plot_vonmises_pdf(x, utils.stddev_to_kappa(1./result_fisherinfo_ratio[ratio_conj_i]**0.5), mu=fixed_means[-1], ax_handle=ax, linewidth=3, color='r', scale=np.max(bar_heights), fmt='-') else: # Switch to Gaussian instead utils.plot_normal_pdf(x, mu=fixed_means[-1], std=1./result_fisherinfo_ratio[ratio_conj_i]**0.5, ax_handle=ax, linewidth=3, color='r', scale=np.max(bar_heights), fmt='-') # ax.set_xticks([]) # ax.set_yticks([]) # Add vertical line to correct target/nontarget ax.axvline(x=fixed_means[0], color='g', linewidth=2) ax.axvline(x=fixed_means[1], color='r', linewidth=2) ax.get_figure().canvas.draw() if savefigs: # plt.tight_layout() dataio.save_current_figure('results_misbinding_histresponses_vonmisespdf_ratioconj%.2f{label}_{unique_id}.pdf' % (ratio_conj)) if plot_logpost: for ratio_conj_i, ratio_conj in enumerate(ratio_space): # ax = utils.plot_mean_std_area(all_angles, nanmean(result_all_log_posterior[ratio_conj_i], axis=0), nanstd(result_all_log_posterior[ratio_conj_i], axis=0)) # ax.set_xlim((-np.pi, np.pi)) # ax.set_xticks((-np.pi, -np.pi / 2, 0, np.pi / 2., np.pi)) # ax.set_xticklabels((r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$')) # ax.set_yticks(()) # ax.get_figure().canvas.draw() # if savefigs: # dataio.save_current_figure('results_misbinding_logpost_ratioconj%.2f_{label}_global_{unique_id}.pdf' % ratio_conj) # Compute the probability of answering wrongly (from fitting mixture distrib onto posterior) for n in xrange(result_all_log_posterior.shape[1]): result_prob_wrong[ratio_conj_i, n], _, _ = utils.fit_gaussian_mixture_fixedmeans(all_angles, np.exp(result_all_log_posterior[ratio_conj_i, n]), fixed_means=fixed_means, normalise=True, return_fitted_data=False, should_plot=False) # ax = utils.plot_mean_std_area(ratio_space, nanmean(result_prob_wrong, axis=-1), nanstd(result_prob_wrong, axis=-1)) plt.figure() plt.plot(ratio_space, utils.nanmean(result_prob_wrong, axis=-1)) # ax.get_figure().canvas.draw() if savefigs: dataio.save_current_figure('results_misbinding_probwrongpost_allratioconj_{label}_global_{unique_id}.pdf') if plot_error: ## Compute Standard deviation/precision from samples and plot it as a function of ratio_conj stats = utils.compute_mean_std_circular_data(utils.wrap_angles(result_all_thetas - fixed_means[1]).T) f = plt.figure() plt.plot(ratio_space, stats['std']) plt.ylabel('Standard deviation [rad]') if savefigs: dataio.save_current_figure('results_misbinding_stddev_allratioconj_{label}_global_{unique_id}.pdf') f = plt.figure() plt.plot(ratio_space, utils.compute_angle_precision_from_std(stats['std'], square_precision=False), linewidth=2) plt.ylabel('Precision [$1/rad$]') plt.xlabel('Proportion of conjunctive units') plt.grid() if savefigs: dataio.save_current_figure('results_misbinding_precision_allratioconj_{label}_global_{unique_id}.pdf') ## Compute the probability of misbinding # 1) Just count samples < 0 / samples tot # 2) Fit a mixture model, average over mixture probabilities prob_smaller0 = np.sum(result_all_thetas <= 1, axis=1)/float(result_all_thetas.shape[1]) em_centers = np.zeros((ratio_space.size, 2)) em_covs = np.zeros((ratio_space.size, 2)) em_pk = np.zeros((ratio_space.size, 2)) em_ll = np.zeros(ratio_space.size) for ratio_conj_i, ratio_conj in enumerate(ratio_space): cen_lst, cov_lst, em_pk[ratio_conj_i], em_ll[ratio_conj_i] = pygmm.em(result_all_thetas[ratio_conj_i, np.newaxis].T, K = 2, max_iter = 400, init_kw={'cluster_init':'fixed', 'fixed_means': fixed_means}) em_centers[ratio_conj_i] = np.array(cen_lst).flatten() em_covs[ratio_conj_i] = np.array(cov_lst).flatten() # print em_centers # print em_covs # print em_pk f = plt.figure() plt.plot(ratio_space, prob_smaller0) plt.ylabel('Misbound proportion') if savefigs: dataio.save_current_figure('results_misbinding_countsmaller0_allratioconj_{label}_global_{unique_id}.pdf') f = plt.figure() plt.plot(ratio_space, np.max(em_pk, axis=-1), 'g', linewidth=2) plt.ylabel('Mixture proportion, correct') plt.xlabel('Proportion of conjunctive units') plt.grid() if savefigs: dataio.save_current_figure('results_misbinding_emmixture_allratioconj_{label}_global_{unique_id}.pdf') # Put everything on one figure f = plt.figure(figsize=(10, 6)) norm_for_plot = lambda x: (x - np.min(x))/np.max((x - np.min(x))) plt.plot(ratio_space, norm_for_plot(stats['std']), ratio_space, norm_for_plot(utils.compute_angle_precision_from_std(stats['std'], square_precision=False)), ratio_space, norm_for_plot(prob_smaller0), ratio_space, norm_for_plot(em_pk[:, 1]), ratio_space, norm_for_plot(em_pk[:, 0])) plt.legend(('Std dev', 'Precision', 'Prob smaller 1', 'Mixture proportion correct', 'Mixture proportion misbinding')) # plt.plot(ratio_space, norm_for_plot(compute_angle_precision_from_std(stats['std'], square_precision=False)), ratio_space, norm_for_plot(em_pk[:, 1]), linewidth=2) # plt.legend(('Precision', 'Mixture proportion correct'), loc='best') plt.grid() if savefigs: dataio.save_current_figure('results_misbinding_allmetrics_allratioconj_{label}_global_{unique_id}.pdf') if plot_mixtmodel: # Fit Paul's model target_angle = np.ones(N)*fixed_means[1] nontarget_angles = np.ones((N, 1))*fixed_means[0] for ratio_conj_i, ratio_conj in enumerate(ratio_space): print "Ratio: ", ratio_conj responses = result_all_thetas[ratio_conj_i] if mixturemodel_to_use == 'allitems_kappafi': curr_params_fit = em_circularmixture_allitems_kappafi.fit(responses, target_angle, nontarget_angles, kappa=result_fisherinfo_ratio[ratio_conj_i]) elif mixturemodel_to_use == 'allitems': curr_params_fit = em_circularmixture_allitems_uniquekappa.fit(responses, target_angle, nontarget_angles) else: curr_params_fit = em_circularmixture.fit(responses, target_angle, nontarget_angles) result_em_fits[ratio_conj_i] = [curr_params_fit['kappa'], curr_params_fit['mixt_target']] + utils.arrnum_to_list(curr_params_fit['mixt_nontargets']) + [curr_params_fit[key] for key in ('mixt_random', 'train_LL', 'bic')] print curr_params_fit if False: f, ax = plt.subplots() ax2 = ax.twinx() # left axis, kappa ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 0], 0*result_em_fits[:, 0], xlabel='Proportion of conjunctive units', ylabel="Inverse variance $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa', color='k') # Right axis, mixture probabilities utils.plot_mean_std_area(ratio_space, result_em_fits[:, 1], 0*result_em_fits[:, 1], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Target') utils.plot_mean_std_area(ratio_space, result_em_fits[:, 2], 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Nontarget') utils.plot_mean_std_area(ratio_space, result_em_fits[:, 3], 0*result_em_fits[:, 3], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Random') lines, labels = ax.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax.legend(lines + lines2, labels + labels2, fontsize=12, loc='right') # ax.set_xlim([0.9, 5.1]) # ax.set_xticks(range(1, 6)) # ax.set_xticklabels(range(1, 6)) plt.grid() f.canvas.draw() if True: # Mixture probabilities ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 1], 0*result_em_fits[:, 1], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", linewidth=3, fmt='-', markersize=8, label='Target') utils.plot_mean_std_area(ratio_space, result_em_fits[:, 2], 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='-', markersize=8, label='Nontarget') utils.plot_mean_std_area(ratio_space, result_em_fits[:, 3], 0*result_em_fits[:, 3], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='-', markersize=8, label='Random') ax.legend(loc='right') # ax.set_xlim([0.9, 5.1]) # ax.set_xticks(range(1, 6)) # ax.set_xticklabels(range(1, 6)) plt.grid() if savefigs: dataio.save_current_figure('results_misbinding_emmixture_allratioconj_{label}_global_{unique_id}.pdf') if True: # Kappa # ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 0], 0*result_em_fits[:, 0], xlabel='Proportion of conjunctive units', ylabel="$\kappa [rad^{-2}]$", linewidth=3, fmt='-', markersize=8, label='Kappa') ax = utils.plot_mean_std_area(ratio_space, utils.kappa_to_stddev(result_em_fits[:, 0]), 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Standard deviation [rad]", linewidth=3, fmt='-', markersize=8, label='Mixture model $\kappa$') # Add Fisher Info theo ax = utils.plot_mean_std_area(ratio_space, utils.kappa_to_stddev(result_fisherinfo_ratio), 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Standard deviation [rad]", linewidth=3, fmt='-', markersize=8, label='Fisher Information', ax_handle=ax) ax.legend(loc='best') # ax.set_xlim([0.9, 5.1]) # ax.set_xticks(range(1, 6)) # ax.set_xticklabels(range(1, 6)) plt.grid() if savefigs: dataio.save_current_figure('results_misbinding_kappa_allratioconj_{label}_global_{unique_id}.pdf') if compute_plot_bootstrap: ## Compute the bootstrap pvalue for each ratio # use the bootstrap CDF from mixed runs, not the exact current ones, not sure if good idea. bootstrap_to_load = 1 if bootstrap_to_load == 1: cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap_mixed_from_bootstrapnontargets.pickle') bootstrap_ecdf_sum_label = 'bootstrap_ecdf_allitems_sum_sigmax_T' bootstrap_ecdf_all_label = 'bootstrap_ecdf_allitems_all_sigmax_T' elif bootstrap_to_load == 2: cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap_misbinding_mixed.pickle') bootstrap_ecdf_sum_label = 'bootstrap_ecdf_allitems_sum_ratioconj' bootstrap_ecdf_all_label = 'bootstrap_ecdf_allitems_all_ratioconj' try: with open(cache_bootstrap_fn, 'r') as file_in: # Load and assign values cached_data = pickle.load(file_in) assert bootstrap_ecdf_sum_label in cached_data assert bootstrap_ecdf_all_label in cached_data should_fit_bootstrap = False except IOError: print "Error while loading ", cache_bootstrap_fn # Select the ECDF to use if bootstrap_to_load == 1: sigmax_i = 3 # corresponds to sigmax = 2, input here. T_i = 1 # two possible targets here. bootstrap_ecdf_sum_used = cached_data[bootstrap_ecdf_sum_label][sigmax_i][T_i]['ecdf'] bootstrap_ecdf_all_used = cached_data[bootstrap_ecdf_all_label][sigmax_i][T_i]['ecdf'] elif bootstrap_to_load == 2: ratio_conj_i = 4 bootstrap_ecdf_sum_used = cached_data[bootstrap_ecdf_sum_label][ratio_conj_i]['ecdf'] bootstrap_ecdf_all_used = cached_data[bootstrap_ecdf_all_label][ratio_conj_i]['ecdf'] result_pvalue_bootstrap_sum = np.empty(ratio_space.size)*np.nan result_pvalue_bootstrap_all = np.empty((ratio_space.size, nontarget_angles.shape[-1]))*np.nan for ratio_conj_i, ratio_conj in enumerate(ratio_space): print "Ratio: ", ratio_conj responses = result_all_thetas[ratio_conj_i] bootstrap_allitems_nontargets_allitems_uniquekappa = em_circularmixture_allitems_uniquekappa.bootstrap_nontarget_stat(responses, target_angle, nontarget_angles, sumnontargets_bootstrap_ecdf=bootstrap_ecdf_sum_used, allnontargets_bootstrap_ecdf=bootstrap_ecdf_all_used) result_pvalue_bootstrap_sum[ratio_conj_i] = bootstrap_allitems_nontargets_allitems_uniquekappa['p_value'] result_pvalue_bootstrap_all[ratio_conj_i] = bootstrap_allitems_nontargets_allitems_uniquekappa['allnontarget_p_value'] ## Plots # f, ax = plt.subplots() # ax.plot(ratio_space, result_pvalue_bootstrap_all, linewidth=2) # if savefigs: # dataio.save_current_figure("pvalue_bootstrap_all_ratioconj_{label}_{unique_id}.pdf") f, ax = plt.subplots() ax.plot(ratio_space, result_pvalue_bootstrap_sum, linewidth=2) plt.grid() if savefigs: dataio.save_current_figure("pvalue_bootstrap_sum_ratioconj_{label}_{unique_id}.pdf") # plt.figure() # plt.plot(ratio_MMlower, results_filtered_smoothed/np.max(results_filtered_smoothed, axis=0), linewidth=2) # plt.plot(ratio_MMlower[np.argmax(results_filtered_smoothed, axis=0)], np.ones(results_filtered_smoothed.shape[-1]), 'ro', markersize=10) # plt.grid() # plt.ylim((0., 1.1)) # plt.subplots_adjust(right=0.8) # plt.legend(['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='center right', bbox_to_anchor=(1.3, 0.5)) # plt.xticks(np.linspace(0, 1.0, 5)) variables_to_save = ['target_angle', 'nontarget_angles'] if savedata: dataio.save_variables_default(locals(), variables_to_save) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='misbindings') plt.show() return locals()
def plot_bootstrap_randomsamples(): ''' Do histograms with random samples from bootstrap nontarget estimates ''' dataio = DataIO(label='plotpaper_bootstrap_randomized') nb_bootstrap_samples = 200 use_precomputed = True angle_space = np.linspace(-np.pi, np.pi, 51) bins_center = angle_space[:-1] + np.diff(angle_space)[0]/2 data_bays2009 = load_experimental_data.load_data_bays09(fit_mixture_model=True) ## Super long simulation, use precomputed data maybe? if use_precomputed: data = pickle.load(open('/Users/loicmatthey/Dropbox/UCL/1-phd/Work/Visual_working_memory/code/git-bayesian-visual-working-memory/Data/cache_randomized_bootstrap_samples_plots_paper_theo_plotbootstrapsamples/bootstrap_histo_katz.npy', 'r')) responses_resampled = data['responses_resampled'] error_nontargets_resampled = data['error_nontargets_resampled'] error_targets_resampled = data['error_targets_resampled'] hist_cnts_nontarget_bootstraps_nitems = data['hist_cnts_nontarget_bootstraps_nitems'] hist_cnts_target_bootstraps_nitems = data['hist_cnts_target_bootstraps_nitems'] else: responses_resampled = np.empty((np.unique(data_bays2009['n_items']).size, nb_bootstrap_samples), dtype=np.object) error_nontargets_resampled = np.empty((np.unique(data_bays2009['n_items']).size, nb_bootstrap_samples), dtype=np.object) error_targets_resampled = np.empty((np.unique(data_bays2009['n_items']).size, nb_bootstrap_samples), dtype=np.object) hist_cnts_nontarget_bootstraps_nitems = np.empty((np.unique(data_bays2009['n_items']).size, nb_bootstrap_samples, angle_space.size - 1))*np.nan hist_cnts_target_bootstraps_nitems = np.empty((np.unique(data_bays2009['n_items']).size, nb_bootstrap_samples, angle_space.size - 1))*np.nan for n_items_i, n_items in enumerate(np.unique(data_bays2009['n_items'])): # Data collapsed accross subjects ids_filtered = (data_bays2009['n_items'] == n_items).flatten() if n_items > 1: # Get random bootstrap nontargets bootstrap_nontargets = utils.sample_angle(data_bays2009['item_angle'][ids_filtered, 1:n_items].shape + (nb_bootstrap_samples, )) # Compute associated EM fits bootstrap_results = [] for bootstrap_i in progress.ProgressDisplay(np.arange(nb_bootstrap_samples), display=progress.SINGLE_LINE): em_fit = em_circularmixture_allitems_uniquekappa.fit(data_bays2009['response'][ids_filtered, 0], data_bays2009['item_angle'][ids_filtered, 0], bootstrap_nontargets[..., bootstrap_i]) bootstrap_results.append(em_fit) # Get EM samples responses_resampled[n_items_i, bootstrap_i] = em_circularmixture_allitems_uniquekappa.sample_from_fit(em_fit, data_bays2009['item_angle'][ids_filtered, 0], bootstrap_nontargets[..., bootstrap_i]) # Compute the errors error_nontargets_resampled[n_items_i, bootstrap_i] = utils.wrap_angles(responses_resampled[n_items_i, bootstrap_i][:, np.newaxis] - bootstrap_nontargets[..., bootstrap_i]) error_targets_resampled[n_items_i, bootstrap_i] = utils.wrap_angles(responses_resampled[n_items_i, bootstrap_i] - data_bays2009['item_angle'][ids_filtered, 0]) # Bin everything hist_cnts_nontarget_bootstraps_nitems[n_items_i, bootstrap_i], x, bins = utils.histogram_binspace(utils.dropnan(error_nontargets_resampled[n_items_i, bootstrap_i]), bins=angle_space, norm='density') hist_cnts_target_bootstraps_nitems[n_items_i, bootstrap_i], x, bins = utils.histogram_binspace(utils.dropnan(error_targets_resampled[n_items_i, bootstrap_i]), bins=angle_space, norm='density') # Now show average histogram hist_cnts_target_bootstraps_nitems_mean = np.mean(hist_cnts_target_bootstraps_nitems, axis=-2) hist_cnts_target_bootstraps_nitems_std = np.std(hist_cnts_target_bootstraps_nitems, axis=-2) hist_cnts_target_bootstraps_nitems_sem = hist_cnts_target_bootstraps_nitems_std/np.sqrt(hist_cnts_target_bootstraps_nitems.shape[1]) hist_cnts_nontarget_bootstraps_nitems_mean = np.mean(hist_cnts_nontarget_bootstraps_nitems, axis=-2) hist_cnts_nontarget_bootstraps_nitems_std = np.std(hist_cnts_nontarget_bootstraps_nitems, axis=-2) hist_cnts_nontarget_bootstraps_nitems_sem = hist_cnts_nontarget_bootstraps_nitems_std/np.sqrt(hist_cnts_target_bootstraps_nitems.shape[1]) f1, axes1 = plt.subplots(ncols=np.unique(data_bays2009['n_items']).size-1, figsize=((np.unique(data_bays2009['n_items']).size-1)*6, 6), sharey=True) for n_items_i, n_items in enumerate(np.unique(data_bays2009['n_items'])): if n_items>1: utils.plot_mean_std_area(bins_center, hist_cnts_nontarget_bootstraps_nitems_mean[n_items_i], hist_cnts_nontarget_bootstraps_nitems_sem[n_items_i], ax_handle=axes1[n_items_i-1], color='k') # Now add the Data histograms axes1[n_items_i-1].bar(bins_center, data_bays2009['hist_cnts_nontarget_nitems_stats']['mean'][n_items_i], width=2.*np.pi/(angle_space.size-1), align='center', yerr=data_bays2009['hist_cnts_nontarget_nitems_stats']['sem'][n_items_i]) # axes4[n_items_i-1].set_title('N=%d' % n_items) axes1[n_items_i-1].set_xlim([bins_center[0]-np.pi/(angle_space.size-1), bins_center[-1]+np.pi/(angle_space.size-1)]) # axes3[n_items_i-1].set_ylim([0., 2.0]) axes1[n_items_i-1].set_xticks((-np.pi, -np.pi/2, 0, np.pi/2., np.pi)) axes1[n_items_i-1].set_xticklabels((r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'), fontsize=16) # axes1[n_items_i-1].bar(bins_center, hist_cnts_nontarget_bootstraps_nitems_mean[n_items_i], width=2.*np.pi/(angle_space.size-1), align='center', yerr=hist_cnts_nontarget_bootstraps_nitems_std[n_items_i]) axes1[n_items_i-1].get_figure().canvas.draw() if dataio is not None: plt.tight_layout() dataio.save_current_figure("hist_error_nontarget_persubj_{label}_{unique_id}.pdf") if False: f2, axes2 = plt.subplots(ncols=np.unique(data_bays2009['n_items']).size-1, figsize=((np.unique(data_bays2009['n_items']).size-1)*6, 6), sharey=True) for n_items_i, n_items in enumerate(np.unique(data_bays2009['n_items'])): utils.plot_mean_std_area(bins_center, hist_cnts_target_bootstraps_nitems_mean[n_items_i], hist_cnts_target_bootstraps_nitems_std[n_items_i], ax_handle=axes2[n_items_i-1]) # axes2[n_items_i-1].bar(bins_center, hist_cnts_target_bootstraps_nitems_mean[n_items_i], width=2.*np.pi/(angle_space.size-1), align='center', yerr=hist_cnts_target_bootstraps_nitems_std[n_items_i]) return locals()