def __plot_memcurves(self, model_em_fits, suptitle_text=None, ax=None): ''' Nice plot for the memory fidelity, as in Fig6 of the paper theo Changes to using the subject fits if FitExperimentAllTSubject used. ''' T_space = self.fit_exp.T_space data_em_fits = self.fit_exp.get_em_fits_arrays() if ax is None: _, ax = plt.subplots() else: ax.hold(False) ax = utils.plot_mean_std_area( T_space, data_em_fits['mean'][0], data_em_fits['std'][0], linewidth=3, fmt='o-', markersize=8, label='Data', ax_handle=ax ) ax.hold(True) ax = utils.plot_mean_std_area( T_space, model_em_fits['mean'][..., 0], model_em_fits['std'][..., 0], xlabel='Number of items', ylabel="Memory fidelity $[rad^{-2}]$", linewidth=3, fmt='o-', markersize=8, label='Model', ax_handle=ax ) ax.legend(loc='upper right', bbox_to_anchor=(1., 1.) ) ax.set_xlim([0.9, T_space.max()+0.1]) ax.set_xticks(range(1, T_space.max()+1)) ax.set_xticklabels(range(1, T_space.max()+1)) if suptitle_text: ax.set_title(suptitle_text) # ax.get_figure().suptitle(suptitle_text) ax.hold(False) ax.get_figure().canvas.draw() return ax
def __plot_memcurves(self, model_em_fits, suptitle_text=None, ax=None): """ Nice plot for the memory fidelity, as in Fig6 of the paper theo """ T_space = self.fit_exp.T_space data_em_fits = self.fit_exp.experimental_dataset["em_fits_nitems_arrays"] if ax is None: _, ax = plt.subplots() else: ax.hold(False) ax = utils.plot_mean_std_area( T_space, data_em_fits["mean"][0], data_em_fits["std"][0], linewidth=3, fmt="o-", markersize=8, label="Experimental data", ax_handle=ax, ) ax.hold(True) ax = utils.plot_mean_std_area( T_space, model_em_fits["mean"][..., 0], model_em_fits["std"][..., 0], xlabel="Number of items", ylabel="Memory error $[rad^{-2}]$", linewidth=3, fmt="o-", markersize=8, label="Fitted kappa", ax_handle=ax, ) ax.legend(prop={"size": 15}, loc="center right", bbox_to_anchor=(1.1, 0.5)) ax.set_xlim([0.9, T_space.max() + 0.1]) ax.set_xticks(range(1, T_space.max() + 1)) ax.set_xticklabels(range(1, T_space.max() + 1)) if suptitle_text: ax.get_figure().suptitle(suptitle_text) ax.get_figure().canvas.draw() return ax
def mem_plot_kappa(sigmax_i, ratiohier_i, T_space_exp, exp_kappa_mean, exp_kappa_std=None): ax = utils.plot_mean_std_area(T_space_exp, exp_kappa_mean, exp_kappa_std, linewidth=3, fmt='o-', markersize=8, label='Experimental data') ax = utils.plot_mean_std_area(T_space[:T_space_exp.max()], result_em_fits_mean[..., :T_space_exp.max(), 0][ratiohier_i, sigmax_i], result_em_fits_std[..., :T_space_exp.max(), 0][ratiohier_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.set_title('ratio_hier %.2f, sigmax %.2f' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i])) ax.legend() ax.set_xlim([0.9, T_space_exp.max()+0.1]) ax.set_xticks(range(1, T_space_exp.max()+1)) ax.set_xticklabels(range(1, T_space_exp.max()+1)) ax.get_figure().canvas.draw() if savefigs: dataio.save_current_figure('memorycurves_kappa_ratiohier%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
def mem_plot_kappa(sigmax_i, ratioconj_i): ax = utils.plot_mean_std_area(T_space, memory_experimental_kappa, memory_experimental_kappa_std, linewidth=3, fmt='o-', markersize=8, label='Experimental data') ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Memory error $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa') # ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][ratioconj_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][ratioconj_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information') ax.set_title('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) ax.legend() ax.set_xlim([0.9, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) if savefigs: dataio.save_current_figure('memorycurves_kappa_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
def plot_precision_rcscale(ax=None): if ax is not None: plt.figure(ax.get_figure().number) ax.hold(False) # Curve of precision evolution. ax = utils.plot_mean_std_area(rcscale_space, result_precision_stats['mean'], result_precision_stats['std'], linewidth=3, fmt='o-', markersize=8, label='Precision', ax_handle=ax) ax.hold(True) ax.axvline(x=optimal_scale, color='r', linewidth=3) ax.axvline(x=optimal_scale_corrected, color='k', linewidth=3) ax.legend() ax.set_title("Precision {code_type} {M} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters'])) ax.set_xlim(rcscale_space.min(), rcscale_space.max()) ax.set_ylim(bottom=0.0) ax.get_figure().canvas.draw() dataio.save_current_figure('precision_rcscale_{code_type}_M{M}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters'])) return ax
def mem_plot_kappa(sigmax_i, M_i, experim_data_mean, experim_data_std=None): ax = utils.plot_mean_std_area(T_space, experim_data_mean, experim_data_std, linewidth=3, fmt='o-', markersize=8, label='Experimental data') 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') # 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, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) 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 mem_plot_precision(sigmax_i, ratiohier_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[ratiohier_i, sigmax_i, :mem_exp_prec.size], result_all_precisions_std[ratiohier_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][ratiohier_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][ratiohier_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][ratiohier_i, sigmax_i], result_em_fits_std[..., 0][ratiohier_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('ratio_hier %.2f, sigmax %.2f' % (ratiohier_space[ratiohier_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_ratiohier%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
def em_plot(sigmax_i, ratioconj_i): # TODO finish checking this up. f, ax = plt.subplots() ax2 = ax.twinx() # left axis, kappa ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_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][ratioconj_i, sigmax_i], result_em_fits_std[..., 1][ratioconj_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][ratioconj_i, sigmax_i], result_em_fits_std[..., 2][ratioconj_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][ratioconj_i, sigmax_i], result_em_fits_std[..., 3][ratioconj_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('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) ax.set_xlim([0.9, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) f.canvas.draw() if savefigs: dataio.save_current_figure('memorycurves_emfits_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
def sigmaoutput_plot_mixtures(sigmaoutput_space, result_em_fits_mean, result_em_fits_std, exp_name='', ax=None): if ax is None: _, ax = plt.subplots() if ax is not None: plt.figure(ax.get_figure().number) ax.hold(False) # mixture probabilities print result_em_fits_mean[..., 1] result_em_fits_mean[np.isnan(result_em_fits_mean)] = 0.0 result_em_fits_std[np.isnan(result_em_fits_std)] = 0.0 utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 1], result_em_fits_std[..., 1], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target') ax.hold(True) utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 2], result_em_fits_std[..., 2], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget') utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 3], result_em_fits_std[..., 3], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Random') ax.legend(prop={'size':15}) ax.set_title("{{exp_name}} {T} {M} {ratio_conj:.2f} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']).format(exp_name=exp_name)) ax.set_ylim([0.0, 1.1]) ax.get_figure().canvas.draw() dataio.save_current_figure('memorycurves_emfits_%s_T{T}_M{M}_ratio{ratio_conj}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']) % (exp_name)) return ax
def plot_mixtures_rcscale(ax=None): if ax is None: _, ax = plt.subplots() if ax is not None: plt.figure(ax.get_figure().number) ax.hold(False) result_em_fits_stats['mean'][np.isnan(result_em_fits_stats['mean'])] = 0.0 result_em_fits_stats['std'][np.isnan(result_em_fits_stats['std'])] = 0.0 utils.plot_mean_std_area(rcscale_space, result_em_fits_stats['mean'][..., 1], result_em_fits_stats['std'][..., 1], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target') ax.hold(True) utils.plot_mean_std_area(rcscale_space, result_em_fits_stats['mean'][..., 2], result_em_fits_stats['std'][..., 2], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget') utils.plot_mean_std_area(rcscale_space, result_em_fits_stats['mean'][..., 3], result_em_fits_stats['std'][..., 3], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Random') ax.axvline(x=optimal_scale, color='r', linewidth=3) ax.axvline(x=optimal_scale_corrected, color='k', linewidth=3) ax.set_xlim(rcscale_space.min(), rcscale_space.max()) ax.set_ylim(bottom=0.0, top=1.0) ax.legend(prop={'size':15}) ax.set_title("mixts {code_type} {M} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters'])) ax.get_figure().canvas.draw() dataio.save_current_figure('em_mixts_rcscale_{code_type}_M{M}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters'])) return ax
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 _plot_emmixture_mean_error(T_space, mean, yerror, ax=None, title='', **args): ''' Main plotting function to show the evolution of an EM Mixture. ''' if ax is None: f, ax = plt.subplots() utils.plot_mean_std_area( T_space, mean, np.ma.masked_invalid(yerror).filled(0.0), ax_handle=ax, linewidth=3, markersize=8, **args) # ax.legend(prop={'size': 15}, loc='best') if title: ax.set_title('Mixture prop: %s' % title) ax.set_xlim([0.9, T_space.max() + 0.1]) ax.set_ylim([0.0, 1.01]) ax.set_xticks(range(1, T_space.max()+1)) ax.set_xticklabels(range(1, T_space.max()+1)) ax.get_figure().canvas.draw() return ax
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 sigmaoutput_plot_kappa(sigmaoutput_space, result_em_fits_mean, result_em_fits_std=None, exp_name='', ax=None): if ax is not None: plt.figure(ax.get_figure().number) ax.hold(False) ax = utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 0], result_em_fits_std[..., 0], xlabel='sigma output', ylabel='Memory fidelity', linewidth=3, fmt='o-', markersize=8, label='Noise output effect', ax_handle=ax) ax.hold(True) ax.set_title("{{exp_name}} {T} {M} {ratio_conj:.2f} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']).format(exp_name=exp_name)) ax.legend() # ax.set_xlim([0.9, T_space_exp.max()+0.1]) # ax.set_xticks(range(1, T_space_exp.max()+1)) # ax.set_xticklabels(range(1, T_space_exp.max()+1)) ax.get_figure().canvas.draw() dataio.save_current_figure('noiseoutput_kappa_%s_T{T}_M{M}_ratio{ratio_conj}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']) % (exp_name)) return ax
def em_plot_paper(sigmax_i, M_i): f, ax = plt.subplots() # 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=ax, linewidth=3, fmt='o-', markersize=5, 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=ax, linewidth=3, fmt='o-', markersize=5, 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=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, 5.0]) ax.set_ylim([0.0, 1.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) f.canvas.draw() if savefigs: dataio.save_current_figure('memorycurves_emfits_paper_M%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (M_space[M_i], sigmax_space[sigmax_i]))
def em_plot_paper(sigmax_i, ratiohier_i): f, ax = plt.subplots() # mixture probabilities utils.plot_mean_std_area(bays09_T_space_interp, result_em_fits_mean[..., 1][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], result_em_fits_std[..., 1][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target') utils.plot_mean_std_area(bays09_T_space_interp, result_em_fits_mean[..., 2][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], result_em_fits_std[..., 2][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget') utils.plot_mean_std_area(bays09_T_space_interp, result_em_fits_mean[..., 3][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], result_em_fits_std[..., 3][ratiohier_i, sigmax_i][:bays09_T_space_interp.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('ratio_hier %.2f, sigmax %.2f' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i])) ax.set_xlim([1.0, bays09_T_space_interp.size]) ax.set_ylim([0.0, 1.1]) ax.set_xticks(range(1, bays09_T_space_interp.size+1)) ax.set_xticklabels(range(1, bays09_T_space_interp.size+1)) f.canvas.draw() if savefigs: dataio.save_current_figure('memorycurves_emfits_paper_ratiohier%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
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 plot_pcolor_fit_precision_to_fisherinfo = False plot_selected_memory_curves = False plot_best_memory_curves = False plot_subplots_persigmax = 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(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) # ratio_space, sigmax_space, T, 5 result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_marginal_inv_fi_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) result_marginal_inv_fi_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) result_marginal_fi_mean = utils.nanmean(np.squeeze(1./data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) result_marginal_fi_std = utils.nanstd(np.squeeze(1./data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) ratioconj_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj'] sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax'] T_space = data_pbs.loaded_data['datasets_list'][0]['T_space'] print ratioconj_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 data_simult = load_experimental_data.load_data_simult(data_dir=os.path.normpath(os.path.join(os.path.split(load_experimental_data.__file__)[0], '../../experimental_data/')), fit_mixture_model=True) memory_experimental_precision = data_simult['precision_nitems_theo'] memory_experimental_kappa = np.array([data['kappa'] for _, data in data_simult['em_fits_nitems']['mean'].items()]) memory_experimental_kappa_std = np.array([data['kappa'] for _, data in data_simult['em_fits_nitems']['std'].items()]) 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'] # add interpolated points for 3 and 5 items bays3 = (bays09_experimental_mixtures_mean[:, 2] + bays09_experimental_mixtures_mean[:, 1])/2. bays5 = (bays09_experimental_mixtures_mean[:, -1] + bays09_experimental_mixtures_mean[:, -2])/2. bays09_experimental_mixtures_mean_compatible = np.c_[bays09_experimental_mixtures_mean[:,:2], bays3, bays09_experimental_mixtures_mean[:, 2], bays5] # 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 - memory_experimental_precision)**2., axis=-1) dist_diff_emkappa_experim = np.sum(np.abs(result_em_fits_mean[..., 0] - memory_experimental_kappa)**2., axis=-1) dist_diff_precision_experim_1item = np.abs(result_all_precisions_mean[..., 0] - memory_experimental_precision[0])**2. dist_diff_precision_experim_2item = np.abs(result_all_precisions_mean[..., 1] - memory_experimental_precision[1])**2. dist_diff_precision_margfi_1item = np.abs(result_all_precisions_mean[..., 0]*2. - result_marginal_fi_mean[..., 0, 0])**2. dist_diff_emkappa_experim_1item = np.abs(result_em_fits_mean[..., 0, 0] - memory_experimental_kappa[0])**2. dist_diff_margfi_experim_1item = np.abs(result_marginal_fi_mean[..., 0, 0] - memory_experimental_precision[0])**2. dist_diff_emkappa_mixtures_bays09 = np.sum(np.sum((result_em_fits_mean[..., 1:4] - bays09_experimental_mixtures_mean_compatible[1:].T)**2., 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., axis=-1), axis=-1) if plot_pcolor_fit_precision_to_fisherinfo: # Check fit between precision and fisher info utils.pcolor_2d_data(dist_diff_precision_margfi, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_precision_margfi_log_pcolor_{label}_{unique_id}.pdf') # utils.pcolor_2d_data(dist_diff_precision_margfi, x=ratioconj_space, y=sigmax_space[2:], xlabel='ratio conj', ylabel='sigmax') # if savefigs: # dataio.save_current_figure('match_precision_margfi_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_ratio_precision_margfi, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True) if savefigs: dataio.save_current_figure('match_ratio_precision_margfi_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_emkappa_margfi, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True) if savefigs: dataio.save_current_figure('match_diff_emkappa_margfi_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_ratio_emkappa_margfi, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True) if savefigs: dataio.save_current_figure('match_ratio_emkappa_margfi_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_precision_experim, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True) if savefigs: dataio.save_current_figure('match_diff_precision_experim_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_emkappa_experim, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True) if savefigs: dataio.save_current_figure('match_diff_emkappa_experim_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_precision_margfi*dist_diff_emkappa_margfi*dist_diff_precision_experim*dist_diff_emkappa_experim, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True) if savefigs: dataio.save_current_figure('match_bigmultiplication_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_precision_margfi_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_diff_precision_margfi_1item_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_precision_experim_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_diff_precision_experim_1item_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_emkappa_experim_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_diff_emkappa_experim_1item_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_margfi_experim_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_diff_margfi_experim_1item_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_precision_experim_2item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_diff_precision_experim_2item_log_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_emkappa_mixtures_bays09, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_diff_mixtures_experbays09_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_diff_modelfits_experfits_bays09, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_diff_emfits_experbays09_pcolor_{label}_{unique_id}.pdf') # Macro plot def mem_plot_precision(sigmax_i, ratioconj_i): ax = utils.plot_mean_std_area(T_space, memory_experimental_precision, np.zeros(T_space.size), linewidth=3, fmt='o-', markersize=8, label='Experimental data') ax = utils.plot_mean_std_area(T_space, result_all_precisions_mean[ratioconj_i, sigmax_i], result_all_precisions_std[ratioconj_i, sigmax_i], 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][ratioconj_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][ratioconj_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][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_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('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) ax.legend() ax.set_xlim([0.9, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) if savefigs: dataio.save_current_figure('memorycurves_precision_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) def mem_plot_kappa(sigmax_i, ratioconj_i): ax = utils.plot_mean_std_area(T_space, memory_experimental_kappa, memory_experimental_kappa_std, linewidth=3, fmt='o-', markersize=8, label='Experimental data') ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Memory error $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa') # ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][ratioconj_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][ratioconj_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information') ax.set_title('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) ax.legend() ax.set_xlim([0.9, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) if savefigs: dataio.save_current_figure('memorycurves_kappa_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) def em_plot(sigmax_i, ratioconj_i): # TODO finish checking this up. f, ax = plt.subplots() ax2 = ax.twinx() # left axis, kappa ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_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][ratioconj_i, sigmax_i], result_em_fits_std[..., 1][ratioconj_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][ratioconj_i, sigmax_i], result_em_fits_std[..., 2][ratioconj_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][ratioconj_i, sigmax_i], result_em_fits_std[..., 3][ratioconj_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('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) ax.set_xlim([0.9, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) f.canvas.draw() if savefigs: dataio.save_current_figure('memorycurves_emfits_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) def em_plot_paper(sigmax_i, ratioconj_i): f, ax = plt.subplots() # Right axis, mixture probabilities utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 1][ratioconj_i, sigmax_i], result_em_fits_std[..., 1][ratioconj_i, sigmax_i], 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, result_em_fits_mean[..., 2][ratioconj_i, sigmax_i], result_em_fits_std[..., 2][ratioconj_i, sigmax_i], 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, result_em_fits_mean[..., 3][ratioconj_i, sigmax_i], result_em_fits_std[..., 3][ratioconj_i, sigmax_i], 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('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) ax.set_xlim([1.0, 5.0]) ax.set_ylim([0.0, 1.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) f.canvas.draw() if savefigs: dataio.save_current_figure('memorycurves_emfits_paper_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i])) if plot_selected_memory_curves: selected_values = [[0.84, 0.23], [0.84, 0.19]] for current_values in selected_values: # Find the indices ratioconj_i = np.argmin(np.abs(current_values[0] - ratioconj_space)) sigmax_i = np.argmin(np.abs(current_values[1] - sigmax_space)) mem_plot_precision(sigmax_i, ratioconj_i) mem_plot_kappa(sigmax_i, ratioconj_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) # 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) 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) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): mem_plot_kappa(best_axis2_i, axis1_i) # em_plot(best_axis2_i, axis1_i) em_plot_paper(best_axis2_i, axis1_i) if plot_subplots_persigmax: # Do subplots with ratio_conj on x, one plot per T and different figures per sigmax. Looks a bit like reloader_hierarchical_constantMMlower_maxlik_allresponses_221213.py sigmax_selected_indices = np.array([np.argmin((sigmax_space - sigmax_select)**2.) for sigmax_select in [0.1, 0.2, 0.3, 0.4, 0.5]]) for sigmax_select_i in sigmax_selected_indices: # two plots per sigmax. f, axes = plt.subplots(nrows=T_space[-1], ncols=1, sharex=True, figsize=(10, 12)) # all_lines_bis = [] for T_i, T in enumerate(T_space): # Plot EM mixtures utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 1], result_em_fits_std[:, sigmax_select_i, T_i, 1], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5) utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 2], result_em_fits_std[:, sigmax_select_i, T_i, 2], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5) utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 3], result_em_fits_std[:, sigmax_select_i, T_i, 3], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5) # ratio_MMlower_space, result_emfits_filtered[:, i, 1:4], 0*result_emfits_filtered[:, i, 1:4], ax_handle=axes[T_i], linewidth=2) #, color=all_lines[T_i].get_color()) # curr_lines = axes[T_i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[T_i].get_color()) axes[T_i].grid() axes[T_i].set_xticks(np.linspace(0., 1.0, 5)) axes[T_i].set_xlim((0.0, 1.0)) # axes[T_i].set_yticks([]) # axes[T_i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1)) axes[T_i].set_ylim((0.0, 1.05)) axes[T_i].locator_params(axis='y', tight=True, nbins=4) # all_lines_bis.extend(curr_lines) axes[0].set_title('Sigmax: %.3f' % sigmax_space[sigmax_select_i]) axes[-1].set_xlabel('Proportion of conjunctive units') if savefigs: dataio.save_current_figure('results_subplots_emmixtures_sigmax%.2f_{label}_global_{unique_id}.pdf' % sigmax_space[sigmax_select_i]) f, axes = plt.subplots(nrows=T_space[-1], ncols=1, sharex=True, figsize=(10, 12)) # Kappa kappa for T_i, T in enumerate(T_space): # Plot kappa mixture utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 0], result_em_fits_std[:, sigmax_select_i, T_i, 0], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5) # utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 0], 0*result_emfits_filtered[:, i, 0], ax_handle=axes[T_i], linewidth=2) #, color=all_lines[T_i].get_color()) # curr_lines = axes[T_i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[T_i].get_color()) axes[T_i].grid() axes[T_i].set_xticks(np.linspace(0., 1.0, 5)) axes[T_i].set_xlim((0.0, 1.0)) # axes[T_i].set_yticks([]) # axes[T_i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1)) # axes[T_i].set_ylim((0.0, 1.0)) axes[T_i].locator_params(axis='y', tight=True, nbins=4) # all_lines_bis.extend(curr_lines) axes[0].set_title('Sigmax: %.3f' % sigmax_space[sigmax_select_i]) axes[-1].set_xlabel('Proportion of conjunctive units') # f.subplots_adjust(right=0.75) # plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5)) if savefigs: dataio.save_current_figure('results_subplots_emkappa_sigmax%.2f_{label}_global_{unique_id}.pdf' % sigmax_space[sigmax_select_i]) all_args = data_pbs.loaded_data['args_list'] variables_to_save = ['memory_experimental_precision', 'memory_experimental_kappa', 'bays09_experimental_mixtures_mean_compatible'] 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 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 check_precision_sensitivity_determ(): ''' Let's construct a situation where we have one Von Mises component and one random component. See how the random component affects the basic precision estimator we use elsewhere. ''' N = 1000 kappa_space = np.array([3., 10., 20.]) # kappa_space = np.array([3.]) nb_repeats = 20 ratio_to_kappa = False savefigs = True precision_nb_samples = 101 N_rnd_space = np.linspace(0, N/2, precision_nb_samples).astype(int) precision_all = np.zeros((N_rnd_space.size, nb_repeats)) kappa_estimated_all = np.zeros((N_rnd_space.size, nb_repeats)) precision_squared_all = np.zeros((N_rnd_space.size, nb_repeats)) kappa_mixtmodel_all = np.zeros((N_rnd_space.size, nb_repeats)) mixtmodel_all = np.zeros((N_rnd_space.size, nb_repeats, 2)) dataio = DataIO.DataIO() target_samples = np.zeros(N) for kappa in kappa_space: true_kappa = kappa*np.ones(N_rnd_space.size) # First sample all as von mises samples_all = spst.vonmises.rvs(kappa, size=(N_rnd_space.size, nb_repeats, N)) for repeat in progress.ProgressDisplay(xrange(nb_repeats)): for i, N_rnd in enumerate(N_rnd_space): samples = samples_all[i, repeat] # Then set K of them to random [-np.pi, np.pi] values. samples[np.random.randint(N, size=N_rnd)] = utils.sample_angle(N_rnd) # Estimate precision from those samples. precision_all[i, repeat] = utils.compute_precision_samples(samples, square_precision=False, remove_chance_level=False) precision_squared_all[i, repeat] = utils.compute_precision_samples(samples, square_precision=True) # convert circular std dev back to kappa kappa_estimated_all[i, repeat] = utils.stddev_to_kappa(1./precision_all[i, repeat]) # Fit mixture model params_fit = em_circularmixture.fit(samples, target_samples) kappa_mixtmodel_all[i, repeat] = params_fit['kappa'] mixtmodel_all[i, repeat] = params_fit['mixt_target'], params_fit['mixt_random'] print "%d/%d N_rnd: %d, Kappa: %.3f, precision: %.3f, kappa_tilde: %.3f, precision^2: %.3f, kappa_mixtmod: %.3f" % (repeat, nb_repeats, N_rnd, kappa, precision_all[i, repeat], kappa_estimated_all[i, repeat], precision_squared_all[i, repeat], kappa_mixtmodel_all[i, repeat]) if ratio_to_kappa: precision_all /= kappa precision_squared_all /= kappa kappa_estimated_all /= kappa true_kappa /= kappa f, ax = plt.subplots() ax.plot(N_rnd_space/float(N), true_kappa, 'k-', linewidth=3, label='Kappa_true') utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(precision_all, axis=-1), np.std(precision_all, axis=-1), ax_handle=ax, label='precision') utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(precision_squared_all, axis=-1), np.std(precision_squared_all, axis=-1), ax_handle=ax, label='precision^2') utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(kappa_estimated_all, axis=-1), np.std(kappa_estimated_all, axis=-1), ax_handle=ax, label='kappa_tilde') utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(kappa_mixtmodel_all, axis=-1), np.std(kappa_mixtmodel_all, axis=-1), ax_handle=ax, label='kappa mixt model') ax.legend() ax.set_title('Effect of random samples on precision. kappa: %.2f. ratiokappa %s' % (kappa, ratio_to_kappa)) ax.set_xlabel('Proportion random samples. N tot %d' % N) ax.set_ylabel('Kappa/precision (not same units)') f.canvas.draw() if savefigs: dataio.save_current_figure("precision_sensitivity_kappa%dN%d_{unique_id}.pdf" % (kappa, N)) # Do another plot, with kappa and mixt_target/mixt_random. Use left/right axis separately f, ax = plt.subplots() ax2 = ax.twinx() # left axis, kappa ax.plot(N_rnd_space/float(N), true_kappa, 'k-', linewidth=3, label='kappa true') utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(kappa_mixtmodel_all, axis=-1), np.std(kappa_mixtmodel_all, axis=-1), ax_handle=ax, label='kappa') # Right axis, mixture probabilities utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(mixtmodel_all[..., 0], axis=-1), np.std(mixtmodel_all[..., 0], axis=-1), ax_handle=ax2, label='mixt target', color='r') utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(mixtmodel_all[..., 1], axis=-1), np.std(mixtmodel_all[..., 1], axis=-1), ax_handle=ax2, label='mixt random', color='g') ax.set_title('Mixture model parameters evolution. kappa: %.2f, ratiokappa %s' % (kappa, ratio_to_kappa)) ax.set_xlabel('Proportion random samples. N tot %d' % N) ax.set_ylabel('Kappa') ax2.set_ylabel('Mixture proportions') lines, labels = ax.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax.legend(lines + lines2, labels + labels2) if savefigs: dataio.save_current_figure("precision_sensitivity_mixtmodel_kappa%dN%d_{unique_id}.pdf" % (kappa, N)) return locals()
def plots_specific_stimuli_hierarchical(data_pbs, generator_module=None): ''' Reload and plot behaviour of mixed population code on specific Stimuli of 3 items. ''' #### SETUP # savefigs = True savedata = True plot_per_min_dist_all = True specific_plots_paper = 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(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) nb_repetitions = np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']).shape[-1] enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance'] sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax'] MMlower_valid_space = data_pbs.loaded_data['datasets_list'][0]['MMlower_valid_space'] ratio_space = MMlower_valid_space[:, 0]/float(np.sum(MMlower_valid_space[0])) print enforce_min_distance_space print sigmax_space print MMlower_valid_space print result_all_precisions_mean.shape, result_em_fits_mean.shape dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) if plot_per_min_dist_all: # Do one plot per min distance. for min_dist_i, min_dist in enumerate(enforce_min_distance_space): # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist) if savefigs: dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True) if savefigs: dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated model precision utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated Target, nontarget and random mixture components, in multiple subplots _, axes = plt.subplots(1, 3, figsize=(18, 6)) plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5) if savefigs: dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot Log-likelihood of Mixture model, sanity check utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) if specific_plots_paper: # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.09 target_mindist_medium = 0.36 target_mindist_high = 1.5 sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) ## Do for each distance # for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]: for min_dist_i in xrange(enforce_min_distance_space.size): # Plot precision utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa fitted utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa-stddev fitted. Easier to visualize utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot LLH utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot mixture parameters utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T) plt.ylim([0.0, 1.1]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot mixture parameters, SEM utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions)) plt.ylim([0.0, 1.1]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) all_args = data_pbs.loaded_data['args_list'] variables_to_save = ['result_all_precisions_mean', 'result_em_fits_mean', 'result_all_precisions_std', 'result_em_fits_std', 'result_em_kappastddev_mean', 'result_em_kappastddev_std', 'enforce_min_distance_space', 'sigmax_space', 'ratio_space', 'all_args'] if savedata: dataio.save_variables(variables_to_save, locals()) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli') plt.show() return locals()
def plots_specific_stimuli_mixed(data_pbs, generator_module=None): ''' Reload and plot behaviour of mixed population code on specific Stimuli of 3 items. ''' #### SETUP # savefigs = True savedata = True plot_per_min_dist_all = False specific_plots_paper = False plots_emfit_allitems = False plot_min_distance_effect = True compute_bootstraps = False should_fit_allitems_model = True # caching_emfit_filename = None mixturemodel_to_use = 'allitems_uniquekappa' # caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfitallitems_uniquekappa.pickle') # mixturemodel_to_use = 'allitems_fikappa' caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfit%s.pickle' % mixturemodel_to_use) compute_fisher_info_perratioconj = True caching_fisherinfo_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_fisherinfo.pickle') colormap = None # or 'cubehelix' plt.rcParams['font.size'] = 16 # #### /SETUP print "Order parameters: ", generator_module.dict_parameters_range.keys() all_args = data_pbs.loaded_data['args_list'] result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results']) result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results']) result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results']) nb_repetitions = result_responses_all.shape[-1] K = result_nontargets_all.shape[-2] N = result_responses_all.shape[-2] enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance'] sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax'] ratio_space = data_pbs.loaded_data['datasets_list'][0]['ratio_space'] print enforce_min_distance_space print sigmax_space print ratio_space print result_all_precisions_mean.shape, result_em_fits_mean.shape print result_responses_all.shape dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) # Reload cached emfitallitems if caching_emfit_filename is not None: if os.path.exists(caching_emfit_filename): # Got file, open it and try to use its contents try: with open(caching_emfit_filename, 'r') as file_in: # Load and assign values print "Reloader EM fits from cache", caching_emfit_filename cached_data = pickle.load(file_in) result_emfitallitems = cached_data['result_emfitallitems'] mixturemodel_used = cached_data.get('mixturemodel_used', '') if mixturemodel_used != mixturemodel_to_use: print "warning, reloaded model used a different mixture model class" should_fit_allitems_model = False except IOError: print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits" # Load the Fisher Info from cache if exists. If not, compute it. 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_mindist_sigmax_ratio = cached_data['result_fisherinfo_mindist_sigmax_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_mindist_sigmax_ratio = np.empty((enforce_min_distance_space.size, sigmax_space.size, ratio_space.size)) # Invert the all_args_i -> min_dist, sigmax indexing parameters_indirections = data_pbs.loaded_data['parameters_dataset_index'] # min_dist_i, sigmax_level_i, ratio_i for min_dist_i, min_dist in enumerate(enforce_min_distance_space): for sigmax_i, sigmax in enumerate(sigmax_space): # Get index of first dataset with the current (min_dist, sigmax) (no need for the others, I think) arg_index = parameters_indirections[(min_dist, sigmax)][0] # Now using this dataset, reconstruct a RandomFactorialNetwork and compute the fisher info curr_args = all_args[arg_index] for ratio_conj_i, ratio_conj in enumerate(ratio_space): # Update param curr_args['ratio_conj'] = ratio_conj # curr_args['stimuli_generation'] = 'specific_stimuli' (_, _, _, sampler) = launchers.init_everything(curr_args) # Theo Fisher info result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_i, ratio_conj_i] = sampler.estimate_fisher_info_theocov() print "Min dist: %.2f, Sigmax: %.2f, Ratio: %.2f: %.3f" % (min_dist, sigmax, ratio_conj, result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_i, ratio_conj_i]) # 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_mindist_sigmax_ratio=result_fisherinfo_mindist_sigmax_ratio) pickle.dump(data_cache, filecache_out, protocol=2) except IOError: print "Error writing out to caching file ", caching_fisherinfo_filename if plot_per_min_dist_all: # Do one plot per min distance. for min_dist_i, min_dist in enumerate(enforce_min_distance_space): # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist) if savefigs: dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True) if savefigs: dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated model precision utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated Target, nontarget and random mixture components, in multiple subplots _, axes = plt.subplots(1, 3, figsize=(18, 6)) plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5) if savefigs: dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot Log-likelihood of Mixture model, sanity check utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) if specific_plots_paper: # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.15 target_mindist_medium = 0.36 target_mindist_high = 1.5 sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) ## Do for each distance # for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]: for min_dist_i in xrange(enforce_min_distance_space.size): # Plot precision if False: utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])]) if savefigs: dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa fitted ax_handle = utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa') # Add distance between items in kappa units dist_items_kappa = utils.stddev_to_kappa(enforce_min_distance_space[min_dist_i]) ax_handle.plot(ratio_space, dist_items_kappa*np.ones(ratio_space.size), 'k--', linewidth=3) plt.ylim([-0.1, np.max((np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]), 1.1*dist_items_kappa))]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa-stddev fitted. Easier to visualize ax_handle = utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev') # Add distance between items in std dev units dist_items_std = (enforce_min_distance_space[min_dist_i]) ax_handle.plot(ratio_space, dist_items_std*np.ones(ratio_space.size), 'k--', linewidth=3) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) plt.ylim([0, 1.1*np.max((np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i]), dist_items_std))]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) if False: # Plot LLH utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot mixture parameters, std utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T) plt.ylim([0.0, 1.1]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Mixture parameters, SEM utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions)) plt.ylim([0.0, 1.1]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) if plots_emfit_allitems: # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.15 target_mindist_medium = 0.36 target_mindist_high = 1.5 sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]) if should_fit_allitems_model: # kappa, mixt_target, mixt_nontargets (K), mixt_random, LL, bic # result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan result_emfitallitems = np.empty((enforce_min_distance_space.size, ratio_space.size, K+5))*np.nan ## Do for each distance # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space): for min_dist_i in xrange(enforce_min_distance_space.size): # Fit the mixture model for ratio_i, ratio in enumerate(ratio_space): print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i] if mixturemodel_to_use == 'allitems_uniquekappa': em_fit = em_circularmixture_allitems_uniquekappa.fit( result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K))) elif mixturemodel_to_use == 'allitems_fikappa': em_fit = em_circularmixture_allitems_kappafi.fit(result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)), kappa=result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_level_i, ratio_i]) else: raise ValueError("Wrong mixturemodel_to_use, %s" % mixturemodel_to_use) result_emfitallitems[min_dist_i, ratio_i] = [em_fit['kappa'], em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')] # Save everything to a file, for faster later plotting if caching_emfit_filename is not None: try: with open(caching_emfit_filename, 'w') as filecache_out: data_em = dict(result_emfitallitems=result_emfitallitems, target_sigmax=target_sigmax) pickle.dump(data_em, filecache_out, protocol=2) except IOError: print "Error writing out to caching file ", caching_emfit_filename ## Plots now, for each distance! # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space): for min_dist_i in xrange(enforce_min_distance_space.size): # Plot now _, ax = plt.subplots() ax.plot(ratio_space, result_emfitallitems[min_dist_i, :, 1:5], linewidth=3) plt.ylim([0.0, 1.1]) plt.legend(['Target', 'Nontarget 1', 'Nontarget 2', 'Random'], loc='upper left') if savefigs: dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) if plot_min_distance_effect: conj_receptive_field_size = 2.*np.pi/((all_args[0]['M']*ratio_space)**0.5) target_vs_nontargets_mindist_ratio = result_emfitallitems[..., 1]/np.sum(result_emfitallitems[..., 1:4], axis=-1) nontargetsmean_vs_targnontarg_mindist_ratio = np.mean(result_emfitallitems[..., 2:4]/np.sum(result_emfitallitems[..., 1:4], axis=-1)[..., np.newaxis], axis=-1) for ratio_conj_i, ratio_conj in enumerate(ratio_space): # Do one plot per ratio, putting the receptive field size on each f, ax = plt.subplots() ax.plot(enforce_min_distance_space[1:], target_vs_nontargets_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='target mixture') ax.plot(enforce_min_distance_space[1:], nontargetsmean_vs_targnontarg_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='non-target mixture') # ax.plot(enforce_min_distance_space[1:], result_emfitallitems[1:, ratio_conj_i, 1:5], linewidth=3) ax.axvline(x=conj_receptive_field_size[ratio_conj_i]/2., color='k', linestyle='--', linewidth=2) ax.axvline(x=conj_receptive_field_size[ratio_conj_i]*2., color='r', linestyle='--', linewidth=2) plt.legend(loc='upper left') plt.grid() # ax.set_xlabel('Stimuli separation') # ax.set_ylabel('Ratio Target to Non-targets') plt.axis('tight') ax.set_ylim([0.0, 1.0]) ax.set_xlim([enforce_min_distance_space[1:].min(), enforce_min_distance_space[1:].max()]) if savefigs: dataio.save_current_figure('ratio%.2f_mindistpred_ratiotargetnontarget_{label}_{unique_id}.pdf' % ratio_conj) if compute_bootstraps: ## Bootstrap evaluation # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.15 target_mindist_medium = 0.5 target_mindist_high = 1. sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) # cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap.pickle') cache_bootstrap_fn = '/Users/loicmatthey/Dropbox/UCL/1-phd/Work/Visual_working_memory/code/git-bayesian-visual-working-memory/Experiments/specific_stimuli/specific_stimuli_corrected_mixed_sigmaxmindistance_autoset_repetitions5mult_collectall_281113_outputs/cache_bootstrap.pickle' try: with open(cache_bootstrap_fn, 'r') as file_in: # Load and assign values cached_data = pickle.load(file_in) bootstrap_ecdf_bays_sigmax_T = cached_data['bootstrap_ecdf_bays_sigmax_T'] bootstrap_ecdf_allitems_sum_sigmax_T = cached_data['bootstrap_ecdf_allitems_sum_sigmax_T'] bootstrap_ecdf_allitems_all_sigmax_T = cached_data['bootstrap_ecdf_allitems_all_sigmax_T'] should_fit_bootstrap = False except IOError: print "Error while loading ", cache_bootstrap_fn ratio_i = 0 # bootstrap_allitems_nontargets_allitems_uniquekappa = em_circularmixture_allitems_uniquekappa.bootstrap_nontarget_stat( # result_responses_all[min_dist_level_low_i, sigmax_level_i, ratio_i].flatten(), # result_target_all[min_dist_level_low_i, sigmax_level_i, ratio_i].flatten(), # result_nontargets_all[min_dist_level_low_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)), # sumnontargets_bootstrap_ecdf=bootstrap_ecdf_allitems_sum_sigmax_T[sigmax_level_i][K]['ecdf'], # allnontargets_bootstrap_ecdf=bootstrap_ecdf_allitems_all_sigmax_T[sigmax_level_i][K]['ecdf'] # TODO FINISH HERE variables_to_save = ['nb_repetitions'] if savedata: dataio.save_variables_default(locals(), variables_to_save) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli') plt.show() return locals()
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_mixtcurves(self, model_em_fits, suptitle_text=None, ax=None): """ Similar kind of plot, but showing the mixture proportions, as in Figure13 """ T_space = self.fit_exp.T_space data_em_fits = self.fit_exp.experimental_dataset["em_fits_nitems_arrays"] if ax is None: _, ax = plt.subplots() else: ax.hold(False) model_em_fits["mean"][np.isnan(model_em_fits["mean"])] = 0.0 model_em_fits["std"][np.isnan(model_em_fits["std"])] = 0.0 # Show model fits utils.plot_mean_std_area( T_space, model_em_fits["mean"][..., 1], model_em_fits["std"][..., 1], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt="o-", markersize=5, label="Target", ) ax.hold(True) utils.plot_mean_std_area( T_space, model_em_fits["mean"][..., 2], model_em_fits["std"][..., 2], 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, model_em_fits["mean"][..., 3], model_em_fits["std"][..., 3], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt="o-", markersize=5, label="Random", ) # Now data utils.plot_mean_std_area( T_space, data_em_fits["mean"][0], data_em_fits["std"][0], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=2, fmt="o:", markersize=5, label="Data target", ) utils.plot_mean_std_area( T_space, data_em_fits["mean"][1], data_em_fits["std"][1], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=2, fmt="o:", markersize=5, label="Data nontarget", ) utils.plot_mean_std_area( T_space, data_em_fits["mean"][2], data_em_fits["std"][2], xlabel="Number of items", ylabel="Mixture probabilities", ax_handle=ax, linewidth=2, fmt="o:", markersize=5, label="Data random", ) ax.legend(prop={"size": 15}, loc="center right", bbox_to_anchor=(1.1, 0.5)) ax.set_xlim([0.9, T_space.max() + 0.1]) ax.set_ylim([0.0, 1.1]) ax.set_xticks(range(1, T_space.max() + 1)) ax.set_xticklabels(range(1, T_space.max() + 1)) if suptitle_text: ax.get_figure().suptitle(suptitle_text) ax.get_figure().canvas.draw() return ax
def plots_specific_stimuli_hierarchical(data_pbs, generator_module=None): ''' Reload and plot behaviour of mixed population code on specific Stimuli of 3 items. ''' #### SETUP # savefigs = True savedata = True plot_per_min_dist_all = False specific_plots_paper = False plots_emfit_allitems = False plot_min_distance_effect = True should_fit_allitems_model = True # caching_emfit_filename = None caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfitallitems_uniquekappa.pickle') 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(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results']) result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results']) result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results']) all_args = data_pbs.loaded_data['args_list'] nb_repetitions = np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']).shape[-1] print nb_repetitions nb_repetitions = result_responses_all.shape[-1] print nb_repetitions K = result_nontargets_all.shape[-2] N = result_responses_all.shape[-2] enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance'] sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax'] MMlower_valid_space = data_pbs.loaded_data['datasets_list'][0]['MMlower_valid_space'] ratio_space = MMlower_valid_space[:, 0]/float(np.sum(MMlower_valid_space[0])) print enforce_min_distance_space print sigmax_space print MMlower_valid_space print result_all_precisions_mean.shape, result_em_fits_mean.shape dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) # Relaod cached emfitallitems if caching_emfit_filename is not None: if os.path.exists(caching_emfit_filename): # Got file, open it and try to use its contents try: with open(caching_emfit_filename, 'r') as file_in: # Load and assign values cached_data = pickle.load(file_in) result_emfitallitems = cached_data['result_emfitallitems'] should_fit_allitems_model = False except IOError: print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits" if plot_per_min_dist_all: # Do one plot per min distance. for min_dist_i, min_dist in enumerate(enforce_min_distance_space): # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist) if savefigs: dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True) if savefigs: dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated model precision (kappa) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated Target, nontarget and random mixture components, in multiple subplots _, axes = plt.subplots(1, 3, figsize=(18, 6)) plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5) if savefigs: dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot Log-likelihood of Mixture model, sanity check utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) if specific_plots_paper: # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.09 target_mindist_medium = 0.36 target_mindist_high = 1.5 sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) ## Do for each distance # for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]: for min_dist_i in xrange(enforce_min_distance_space.size): # Plot precision if False: utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])]) if savefigs: dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa fitted ax_handle = utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa') # Add distance between items in kappa units dist_items_kappa = utils.stddev_to_kappa(enforce_min_distance_space[min_dist_i]) ax_handle.plot(ratio_space, dist_items_kappa*np.ones(ratio_space.size), 'k--', linewidth=3) plt.ylim([-0.1, np.max((np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]), 1.1*dist_items_kappa))]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa-stddev fitted. Easier to visualize ax_handle = utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev') # Add distance between items in std dev units dist_items_std = (enforce_min_distance_space[min_dist_i]) ax_handle.plot(ratio_space, dist_items_std*np.ones(ratio_space.size), 'k--', linewidth=3) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) plt.ylim([0, 1.1*np.max((np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i]), dist_items_std))]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) if False: # Plot LLH utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot mixture parameters, std utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T) plt.ylim([0.0, 1.1]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Mixture parameters, SEM utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions)) plt.ylim([0.0, 1.1]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) if plots_emfit_allitems: # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.15 target_mindist_medium = 0.36 target_mindist_high = 1.5 sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]) if should_fit_allitems_model: # kappa, mixt_target, mixt_nontargets (K), mixt_random, LL, bic # result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan result_emfitallitems = np.empty((enforce_min_distance_space.size, ratio_space.size, K+5))*np.nan ## Do for each distance # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space): for min_dist_i in xrange(enforce_min_distance_space.size): # Fit the mixture model for ratio_i, ratio in enumerate(ratio_space): print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i] em_fit = em_circularmixture_allitems_uniquekappa.fit( result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K))) result_emfitallitems[min_dist_i, ratio_i] = [em_fit['kappa'], em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')] # Save everything to a file, for faster later plotting if caching_emfit_filename is not None: try: with open(caching_emfit_filename, 'w') as filecache_out: data_em = dict(result_emfitallitems=result_emfitallitems) pickle.dump(data_em, filecache_out, protocol=2) except IOError: print "Error writing out to caching file ", caching_emfit_filename ## Plots now, for each distance! # for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space): for min_dist_i in xrange(enforce_min_distance_space.size): # Plot now _, ax = plt.subplots() ax.plot(ratio_space, result_emfitallitems[min_dist_i, :, 1:5], linewidth=3) plt.ylim([0.0, 1.1]) plt.legend(['Target', 'Nontarget 1', 'Nontarget 2', 'Random'], loc='upper left') if savefigs: dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) if plot_min_distance_effect: conj_receptive_field_size = 2.*np.pi/((all_args[0]['M']*ratio_space)**0.5) target_vs_nontargets_mindist_ratio = result_emfitallitems[..., 1]/np.sum(result_emfitallitems[..., 1:4], axis=-1) nontargetsmean_vs_targnontarg_mindist_ratio = np.mean(result_emfitallitems[..., 2:4]/np.sum(result_emfitallitems[..., 1:4], axis=-1)[..., np.newaxis], axis=-1) for ratio_conj_i, ratio_conj in enumerate(ratio_space): # Do one plot per ratio, putting the receptive field size on each f, ax = plt.subplots() ax.plot(enforce_min_distance_space[1:], target_vs_nontargets_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='target mixture') ax.plot(enforce_min_distance_space[1:], nontargetsmean_vs_targnontarg_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='non-target mixture') # ax.plot(enforce_min_distance_space[1:], result_emfitallitems[1:, ratio_conj_i, 1:5], linewidth=3) ax.axvline(x=conj_receptive_field_size[ratio_conj_i]/2., color='k', linestyle='--', linewidth=2) ax.axvline(x=conj_receptive_field_size[ratio_conj_i]*2., color='r', linestyle='--', linewidth=2) plt.legend(loc='upper left') plt.grid() # ax.set_xlabel('Stimuli separation') # ax.set_ylabel('Ratio Target to Non-targets') plt.axis('tight') ax.set_ylim([0.0, 1.0]) ax.set_xlim([enforce_min_distance_space[1:].min(), enforce_min_distance_space[1:].max()]) if savefigs: dataio.save_current_figure('ratio%.2f_mindistpred_ratiotargetnontarget_{label}_{unique_id}.pdf' % ratio_conj) variables_to_save = ['nb_repetitions'] if savedata: dataio.save_variables_default(locals(), variables_to_save) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli') plt.show() return locals()
def plots_3dvolume_hierarchical_M_Mlayerone(data_pbs, generator_module=None): ''' Reload 3D volume runs from PBS and plot them ''' #### SETUP # savefigs = True savedata = False plots_pcolors = False plots_singleaxe = False plots_multipleaxes = True plots_multipleaxes_emfits = True load_fit_mixture_model = True # caching_emfit_filename = None caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_emfit.pickle') plt.rcParams['font.size'] = 16 # #### /SETUP dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) print "Order parameters: ", generator_module.dict_parameters_range.keys() results_precision_constant_M_Mlower = np.squeeze(utils.nanmean(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1)) results_precision_constant_M_Mlower_std = np.squeeze(utils.nanstd(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1)) results_responses = np.squeeze(data_pbs.dict_arrays['result_responses']['results']) results_targets = np.squeeze(data_pbs.dict_arrays['result_targets']['results']) results_nontargets = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results']) results_emfits_M_T = np.squeeze(data_pbs.dict_arrays['results_emfits_M_T']['results']) M_space = data_pbs.loaded_data['parameters_uniques']['M'] M_layer_one_space = data_pbs.loaded_data['parameters_uniques']['M_layer_one'] ratio_MMlower_space = M_space/generator_module.filtering_function_parameters['target_M_total'] filtering_indices = (np.arange(M_space.size), np.arange(-M_layer_one_space.size, 0)[::-1]) T = results_precision_constant_M_Mlower.shape[-1] T_space = np.arange(T) print M_space print M_layer_one_space print results_precision_constant_M_Mlower.shape # print results_precision_constant_M_Mlower T = results_precision_constant_M_Mlower.shape[-1] results_precision_filtered = results_precision_constant_M_Mlower[filtering_indices] results_precision_filtered_std = results_precision_constant_M_Mlower_std[filtering_indices] results_responses_filtered = results_responses[filtering_indices] results_targets_filtered = results_targets[filtering_indices] results_nontargets_filtered = results_nontargets[filtering_indices] results_emfits_M_T_filtered = results_emfits_M_T[filtering_indices] results_precision_filtered_smoothed = np.apply_along_axis(smooth, 0, results_precision_filtered, *(10, 'bartlett')) if load_fit_mixture_model: # Fit the mixture model on the samples if caching_emfit_filename is not None: if os.path.exists(caching_emfit_filename): # Got file, open it and try to use its contents try: with open(caching_emfit_filename, 'r') as file_in: # Load and assign values cached_data = pickle.load(file_in) result_emfits_filtered = cached_data['result_emfits_filtered'] print "Loading from cache file %s" % caching_emfit_filename load_fit_mixture_model = False except IOError: print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits" load_fit_mixture_model = False if load_fit_mixture_model: result_emfits_filtered = np.nan*np.empty((ratio_MMlower_space.size, T, 5)) # Fit EM model print "fitting EM model" for ratio_MMlower_i, ratio_MMlower in enumerate(ratio_MMlower_space): for T_i in T_space: if np.any(~np.isnan(results_responses_filtered[ratio_MMlower_i, T_i])): print "ratio MM, T:", ratio_MMlower, T_i+1 curr_em_fits = em_circularmixture_allitems_uniquekappa.fit(results_responses_filtered[ratio_MMlower_i, T_i], results_targets_filtered[ratio_MMlower_i, T_i], results_nontargets_filtered[ratio_MMlower_i, T_i, :, :T_i]) curr_em_fits['mixt_nontargets_sum'] = np.sum(curr_em_fits['mixt_nontargets']) result_emfits_filtered[ratio_MMlower_i, T_i] = [curr_em_fits[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL')] # Save everything to a file, for faster later plotting if caching_emfit_filename is not None: try: with open(caching_emfit_filename, 'w') as filecache_out: data_emfit = dict(result_emfits_filtered=result_emfits_filtered) pickle.dump(data_emfit, filecache_out, protocol=2) print "cache file %s written" % caching_emfit_filename except IOError: print "Error writing out to caching file ", caching_emfit_filename if plots_pcolors: utils.pcolor_2d_data(results_precision_filtered, log_scale=True, x=ratio_MMlower_space, y=np.arange(1, T+1), xlabel="$\\frac{M}{M+M_{layer one}}$", ylabel='$T$', ticks_interpolate=10) plt.plot(np.argmax(results_precision_filtered, axis=0), np.arange(results_precision_filtered.shape[-1]), 'ko', markersize=10) if savefigs: dataio.save_current_figure('results_2dlog_{label}_global_{unique_id}.pdf') utils.pcolor_2d_data(results_precision_filtered/np.max(results_precision_filtered, axis=0), x=ratio_MMlower_space, y=np.arange(1, T+1), xlabel="$\\frac{M}{M+M_{layer one}}$", ylabel='$T$', ticks_interpolate=10) plt.plot(np.argmax(results_precision_filtered, axis=0), np.arange(results_precision_filtered.shape[-1]), 'ko', markersize=10) if savefigs: dataio.save_current_figure('results_2dnorm_{label}_global_{unique_id}.pdf') utils.pcolor_2d_data(results_precision_filtered_smoothed/np.max(results_precision_filtered_smoothed, axis=0), x=ratio_MMlower_space, y=np.arange(1, T+1), xlabel="$\\frac{M}{M+M_{layer one}}$", ylabel='$T$', ticks_interpolate=10) plt.plot(np.argmax(results_precision_filtered_smoothed, axis=0), np.arange(results_precision_filtered_smoothed.shape[-1]), 'ko', markersize=10) if savefigs: dataio.save_current_figure('results_2dsmoothnorm_{label}_global_{unique_id}.pdf') if plots_singleaxe: plt.figure() plt.plot(ratio_MMlower_space, results_precision_filtered_smoothed/np.max(results_precision_filtered_smoothed, axis=0), linewidth=2) plt.plot(ratio_MMlower_space[np.argmax(results_precision_filtered_smoothed, axis=0)], np.ones(results_precision_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)) if savefigs: dataio.save_current_figure('results_1dsmoothnormsame_{label}_global_{unique_id}.pdf') plt.figure() moved_results_precision_filtered_smoothed = 1.2*np.arange(results_precision_filtered_smoothed.shape[-1]) + results_precision_filtered_smoothed/np.max(results_precision_filtered_smoothed, axis=0) all_lines = [] for i, max_i in enumerate(np.argmax(results_precision_filtered_smoothed, axis=0)): curr_lines = plt.plot(ratio_MMlower_space, moved_results_precision_filtered_smoothed[:, i], linewidth=2) plt.plot(ratio_MMlower_space[max_i], moved_results_precision_filtered_smoothed[max_i, i], 'o', markersize=10, color=curr_lines[0].get_color()) all_lines.extend(curr_lines) plt.plot(np.linspace(0.0, 1.0, 100), np.outer(np.ones(100), 1.2*np.arange(1, results_precision_filtered_smoothed.shape[-1])), 'k:') plt.grid() plt.legend(all_lines, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='best') plt.ylim((0., moved_results_precision_filtered_smoothed.max()*1.05)) plt.yticks([]) plt.xticks(np.linspace(0, 1.0, 5)) if savefigs: dataio.save_current_figure('results_1dsmoothnorm_{label}_global_{unique_id}.pdf') if plots_multipleaxes: # Plot smooth precisions, all T on multiple subplots. # all_lines = [] f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12)) for i, max_ind in enumerate(np.argmax(results_precision_filtered_smoothed, axis=0)): curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered_smoothed[:, i], linewidth=2) # , color=all_lines[i].get_color()) axes[i].plot(ratio_MMlower_space[max_ind], results_precision_filtered_smoothed[max_ind, i], 'o', markersize=10, color=curr_lines[0].get_color()) axes[i].grid() axes[i].set_xticks(np.linspace(0., 1.0, 5)) axes[i].set_xlim((0.0, 1.0)) # axes[i].set_yticks([]) axes[i].set_ylim((np.min(results_precision_filtered_smoothed[:, i]), results_precision_filtered_smoothed[max_ind, i]*1.1)) axes[i].locator_params(axis='y', tight=True, nbins=4) # all_lines.extend(curr_lines) f.subplots_adjust(right=0.75) # plt.figlegend(all_lines, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5)) # f.tight_layout() if savefigs: dataio.save_current_figure('results_subplots_1dsmoothnorm_{label}_global_{unique_id}.pdf') # Plot precisions with standard deviation around f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12)) # all_lines_bis = [] for i, max_ind in enumerate(np.argmax(results_precision_filtered, axis=0)): utils.plot_mean_std_area(ratio_MMlower_space, results_precision_filtered[:, i], results_precision_filtered_std[:, i], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color()) # curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color()) axes[i].grid() axes[i].set_xticks(np.linspace(0., 1.0, 5)) axes[i].set_xlim((0.0, 1.0)) # axes[i].set_yticks([]) axes[i].set_ylim((np.min(results_precision_filtered[:, i]), results_precision_filtered[max_ind, i]*1.1)) axes[i].locator_params(axis='y', tight=True, nbins=4) # all_lines_bis.extend(curr_lines) f.subplots_adjust(right=0.75) # plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5)) if savefigs: dataio.save_current_figure('results_subplots_1dnorm_{label}_global_{unique_id}.pdf') if plots_multipleaxes_emfits: f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12)) all_lines_bis = [] for i, max_ind in enumerate(np.nanargmax(result_emfits_filtered[..., 0], axis=0)): utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 0], 0*result_emfits_filtered[:, i, 0], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color()) # curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color()) axes[i].grid() axes[i].set_xticks(np.linspace(0., 1.0, 5)) axes[i].set_xlim((0.0, 1.0)) # axes[i].set_yticks([]) # axes[i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1)) axes[i].locator_params(axis='y', tight=True, nbins=4) # all_lines_bis.extend(curr_lines) f.subplots_adjust(right=0.75) # plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5)) if savefigs: dataio.save_current_figure('results_subplots_emkappa_{label}_global_{unique_id}.pdf') variables_to_save = [] if savedata: dataio.save_variables_default(locals(), variables_to_save) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='hierarchicalrandomnetwork_characterisation') plt.show() return locals()
def plots_memory_curves(data_pbs, generator_module=None): """ Reload and plot memory curve of a feature code. Can use Marginal Fisher Information and fitted Mixture Model as well """ #### SETUP # savefigs = True savedata = True plot_pcolor_fit_precision_to_fisherinfo = True plot_selected_memory_curves = True plot_best_memory_curves = 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( np.squeeze(data_pbs.dict_arrays["result_all_precisions"]["results"]), axis=-1 ) result_all_precisions_std = utils.nanstd( np.squeeze(data_pbs.dict_arrays["result_all_precisions"]["results"]), axis=-1 ) result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays["result_em_fits"]["results"]), axis=-1) result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays["result_em_fits"]["results"]), axis=-1) result_marginal_inv_fi_mean = utils.nanmean( np.squeeze(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"]), axis=-1 ) result_marginal_inv_fi_std = utils.nanstd( np.squeeze(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"]), axis=-1 ) result_marginal_fi_mean = utils.nanmean( 1.0 / np.squeeze(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"]), axis=-1 ) result_marginal_fi_std = utils.nanstd( 1.0 / np.squeeze(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"]), axis=-1 ) M_space = data_pbs.loaded_data["parameters_uniques"]["M"] sigmax_space = data_pbs.loaded_data["parameters_uniques"]["sigmax"] T_space = data_pbs.loaded_data["datasets_list"][0]["T_space"] 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 data_simult = load_experimental_data.load_data_simult( data_dir=os.path.normpath( os.path.join(os.path.split(load_experimental_data.__file__)[0], "../../experimental_data/") ) ) memory_experimental = data_simult["precision_nitems_theo"] data_bays2009 = load_experimental_data.load_data_bays09( data_dir=os.path.normpath( os.path.join(os.path.split(load_experimental_data.__file__)[0], "../../experimental_data/") ), fit_mixture_model=True, ) bays09_experimental_mixtures_mean = data_bays2009["em_fits_nitems_arrays"]["mean"][1:] # add interpolated points for 3 and 5 items bays3 = (bays09_experimental_mixtures_mean[:, 2] + bays09_experimental_mixtures_mean[:, 1]) / 2.0 bays5 = (bays09_experimental_mixtures_mean[:, -1] + bays09_experimental_mixtures_mean[:, -2]) / 2.0 bays09_experimental_mixtures_mean_compatible = c_[ bays09_experimental_mixtures_mean[:, :2], bays3, bays09_experimental_mixtures_mean[:, 2], bays5 ] # 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) # Force non target em fit mixture to be zero and not nan result_em_fits_mean[..., 0, 2] = 0 result_em_fits_std[..., 0, 2] = 0 # Compute some landscapes of fit! dist_diff_precision_margfi = np.sum( np.abs(result_all_precisions_mean * 2.0 - result_marginal_fi_mean[..., 0]) ** 2.0, axis=-1 ) dist_diff_precision_margfi_1item = ( np.abs(result_all_precisions_mean[..., 0] * 2.0 - result_marginal_fi_mean[..., 0, 0]) ** 2.0 ) dist_diff_emkappa_margfi = np.sum( np.abs(result_em_fits_mean[..., 0] * 2.0 - result_marginal_fi_mean[..., 0]) ** 2.0, axis=-1 ) dist_ratio_emkappa_margfi = np.sum( np.abs((result_em_fits_mean[..., 0] * 2.0) / result_marginal_fi_mean[..., 0] - 1.0) ** 2.0, axis=-1 ) dist_diff_precision_experim = np.sum(np.abs(result_all_precisions_mean - memory_experimental) ** 2.0, axis=-1) dist_diff_precision_experim_1item = np.abs(result_all_precisions_mean[..., 0] - memory_experimental[0]) ** 2.0 dist_diff_emkappa_experim = np.sum(np.abs(result_em_fits_mean[..., 0] - memory_experimental) ** 2.0, axis=-1) dist_diff_emkappa_experim_1item = np.abs(result_em_fits_mean[..., 0, 0] - memory_experimental[0]) ** 2.0 dist_diff_margfi_experim_1item = np.abs(result_marginal_fi_mean[..., 0, 0] - memory_experimental[0]) ** 2.0 dist_diff_emkappa_mixtures_bays09 = np.sum( np.sum((result_em_fits_mean[..., 1:4] - bays09_experimental_mixtures_mean_compatible.T) ** 2.0, axis=-1), axis=-1, ) if plot_pcolor_fit_precision_to_fisherinfo: # Check fit between precision and fisher info utils.pcolor_2d_data( dist_diff_precision_margfi, log_scale=True, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax" ) if savefigs: dataio.save_current_figure("match_precision_margfi_log_pcolor_{label}_{unique_id}.pdf") # utils.pcolor_2d_data(dist_diff_precision_margfi, x=M_space, y=sigmax_space[2:], xlabel='M', ylabel='sigmax') # if savefigs: # dataio.save_current_figure('match_precision_margfi_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data( dist_diff_precision_experim, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", log_scale=True ) utils.pcolor_2d_data( dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", log_scale=True ) utils.pcolor_2d_data( dist_diff_precision_margfi * dist_diff_emkappa_margfi * dist_diff_precision_experim * dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", log_scale=True, ) utils.pcolor_2d_data( dist_diff_precision_margfi_1item, log_scale=True, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax" ) utils.pcolor_2d_data( dist_diff_precision_experim_1item, log_scale=True, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax" ) utils.pcolor_2d_data( dist_diff_emkappa_experim_1item, log_scale=True, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax" ) utils.pcolor_2d_data( dist_diff_margfi_experim_1item, log_scale=True, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax" ) utils.pcolor_2d_data( dist_diff_emkappa_mixtures_bays09, log_scale=False, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax" ) if plot_selected_memory_curves: selected_values = [[100, 0.8], [200, 0.27], [100, 0.1], [200, 0.8], [100, 0.17], [100, 0.08], [50, 0.21]] 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)) ax = utils.plot_mean_std_area( T_space, memory_experimental, np.zeros(T_space.size), linewidth=3, fmt="o-", markersize=8 ) ax = utils.plot_mean_std_area( T_space, result_all_precisions_mean[M_i, sigmax_i], result_all_precisions_std[M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt="o-", markersize=8, ) 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, ) 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, ) # ax.set_title('M %d, sigmax %.2f' % (M_space[M_i], sigmax_space[sigmax_i])) plt.legend(["Experimental data", "Precision of samples", "Marginal Fisher Information", "Fitted kappa"]) ax.set_xlim([0.9, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) if savefigs: dataio.save_current_figure( "memorycurves_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, 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=ax, linewidth=3, fmt="o-", markersize=5, 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=ax, linewidth=3, fmt="o-", markersize=5, 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=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, 5.0]) ax.set_ylim([0.0, 1.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) f.canvas.draw() if savefigs: dataio.save_current_figure( "memorycurves_emfits_paper_M%.2fsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i]) ) if plot_best_memory_curves: # Best mixtures fit best_axis2_i_all = np.argmin(dist_diff_emkappa_mixtures_bays09, axis=1) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): em_plot_paper(best_axis2_i, axis1_i) all_args = data_pbs.loaded_data["args_list"] variables_to_save = [ "result_all_precisions_mean", "result_em_fits_mean", "result_marginal_inv_fi_mean", "result_all_precisions_std", "result_em_fits_std", "result_marginal_inv_fi_std", "result_marginal_fi_mean", "result_marginal_fi_std", "M_space", "sigmax_space", "T_space", "all_args", ] if savedata: dataio.save_variables(variables_to_save, locals()) # Make link to Dropbox dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder="memory_curves") plt.show() return locals()
def plots_3dvolume_hierarchical_M_Mlayerone(data_pbs, generator_module=None): ''' Reload 3D volume runs from PBS and plot them ''' #### SETUP # savefigs = True savedata = False # warning, huge file created... plots_pcolors = False plots_singleaxe = False plots_multipleaxes = True plots_multipleaxes_emfits = True load_fit_mixture_model = True # caching_emfit_filename = None caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_emfit_newshapes.pickle') plt.rcParams['font.size'] = 16 # #### /SETUP dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) print "Order parameters: ", generator_module.dict_parameters_range.keys() results_precision_constant_M_Mlower = np.squeeze(utils.nanmean(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1)) results_precision_constant_M_Mlower_std = np.squeeze(utils.nanstd(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1)) results_responses = np.squeeze(data_pbs.dict_arrays['result_responses']['results']) results_targets = np.squeeze(data_pbs.dict_arrays['result_targets']['results']) results_nontargets = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results']) # results_emfits_M_T = np.squeeze(data_pbs.dict_arrays['results_emfits_M_T']['results']) M_space = data_pbs.loaded_data['parameters_uniques']['M'] M_layer_one_space = data_pbs.loaded_data['parameters_uniques']['M_layer_one'] ratio_MMlower_space = M_space/generator_module.filtering_function_parameters['target_M_total'] filtering_indices = (np.arange(M_space.size), np.arange(-M_layer_one_space.size, 0)[::-1]) T = results_precision_constant_M_Mlower.shape[-1] T_space = np.arange(T) N = results_nontargets.shape[-2] num_repetitions = data_pbs.loaded_data['args_list'][0]['num_repetitions'] # Fix after @3625585, now the shape are: # results_responses, results_targets: M_space.size, M_layer_one_space.size, T, wrong num_repet, N # results_nontargets: M_space.size, M_layer_one_space.size, T, wrong num_repet, N, T-1 if data_pbs.loaded_data['nb_datasets_per_parameters'] > 1: num_repetitions = data_pbs.loaded_data['nb_datasets_per_parameters'] # Filter and reshape results_responses = results_responses[:, :, :, :num_repetitions, :] results_responses.shape = (M_space.size, M_layer_one_space.size, T, num_repetitions*N) results_targets = results_targets[:, :, :, :num_repetitions, :] results_targets.shape = (M_space.size, M_layer_one_space.size, T, num_repetitions*N) results_nontargets = results_nontargets[:, :, :, :num_repetitions, :, :] results_nontargets.shape = (M_space.size, M_layer_one_space.size, T, num_repetitions*N, T-1) print M_space print M_layer_one_space print results_precision_constant_M_Mlower.shape # print results_precision_constant_M_Mlower results_precision_filtered = results_precision_constant_M_Mlower[filtering_indices] del results_precision_constant_M_Mlower results_precision_filtered_std = results_precision_constant_M_Mlower_std[filtering_indices] del results_precision_constant_M_Mlower_std results_responses_filtered = results_responses[filtering_indices] del results_responses results_targets_filtered = results_targets[filtering_indices] del results_targets results_nontargets_filtered = results_nontargets[filtering_indices] del results_nontargets # results_emfits_M_T_filtered = results_emfits_M_T[filtering_indices] # del results_emfits_M_T results_precision_filtered_smoothed = np.apply_along_axis(smooth, 0, results_precision_filtered, *(10, 'bartlett')) if load_fit_mixture_model: # Fit the mixture model on the samples if caching_emfit_filename is not None: if os.path.exists(caching_emfit_filename): # Got file, open it and try to use its contents try: with open(caching_emfit_filename, 'r') as file_in: # Load and assign values cached_data = pickle.load(file_in) result_emfits_filtered = cached_data['result_emfits_filtered'] results_responses_sha1_loaded = cached_data.get('results_responses_sha1', '') # Check that the sha1 is the same, if not recompute! if results_responses_sha1_loaded == hashlib.sha1(results_responses_filtered).hexdigest(): print "Loading from cache file %s" % caching_emfit_filename load_fit_mixture_model = False else: print "Tried loading from cache file %s, but data changed, recomputing..." % caching_emfit_filename load_fit_mixture_model = True except IOError: print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits" load_fit_mixture_model = False if load_fit_mixture_model: result_emfits_filtered = np.nan*np.empty((ratio_MMlower_space.size, T, 5)) # Fit EM model print "fitting EM model" for ratio_MMlower_i, ratio_MMlower in enumerate(ratio_MMlower_space): for T_i in T_space: if np.any(~np.isnan(results_responses_filtered[ratio_MMlower_i, T_i])): print "ratio MM, T:", ratio_MMlower, T_i+1 curr_em_fits = em_circularmixture_allitems_uniquekappa.fit(results_responses_filtered[ratio_MMlower_i, T_i], results_targets_filtered[ratio_MMlower_i, T_i], results_nontargets_filtered[ratio_MMlower_i, T_i, :, :T_i]) curr_em_fits['mixt_nontargets_sum'] = np.sum(curr_em_fits['mixt_nontargets']) result_emfits_filtered[ratio_MMlower_i, T_i] = [curr_em_fits[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL')] # Save everything to a file, for faster later plotting if caching_emfit_filename is not None: try: with open(caching_emfit_filename, 'w') as filecache_out: results_responses_sha1 = hashlib.sha1(results_responses_filtered).hexdigest() data_emfit = dict(result_emfits_filtered=result_emfits_filtered, results_responses_sha1=results_responses_sha1) pickle.dump(data_emfit, filecache_out, protocol=2) print "cache file %s written" % caching_emfit_filename except IOError: print "Error writing out to caching file ", caching_emfit_filename if plots_multipleaxes: # Plot precisions with standard deviation around f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12)) # all_lines_bis = [] for i, max_ind in enumerate(np.argmax(results_precision_filtered, axis=0)): utils.plot_mean_std_area(ratio_MMlower_space, results_precision_filtered[:, i], results_precision_filtered_std[:, i], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color()) # curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color()) axes[i].grid() axes[i].set_xticks(np.linspace(0., 1.0, 5)) axes[i].set_xlim((0.0, 1.0)) # axes[i].set_yticks([]) axes[i].set_ylim((np.min(results_precision_filtered[:, i]), results_precision_filtered[max_ind, i]*1.1)) axes[i].locator_params(axis='y', tight=True, nbins=4) # all_lines_bis.extend(curr_lines) f.subplots_adjust(right=0.75) # plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5)) if savefigs: dataio.save_current_figure('results_subplots_1dnorm_{label}_global_{unique_id}.pdf') if plots_multipleaxes_emfits: f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12)) all_lines_bis = [] for i, max_ind in enumerate(np.nanargmax(result_emfits_filtered[..., 0], axis=0)): # Plot Target mixture utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 1:4], 0*result_emfits_filtered[:, i, 1:4], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color()) # curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color()) axes[i].grid() axes[i].set_xticks(np.linspace(0., 1.0, 5)) axes[i].set_xlim((0.0, 1.0)) # axes[i].set_yticks([]) # axes[i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1)) axes[i].set_ylim((0.0, 1.05)) axes[i].locator_params(axis='y', tight=True, nbins=4) # all_lines_bis.extend(curr_lines) if savefigs: dataio.save_current_figure('results_subplots_emtarget_{label}_global_{unique_id}.pdf') f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12)) for i, max_ind in enumerate(np.nanargmax(result_emfits_filtered[..., 0], axis=0)): # Plot kappa mixture utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 0], 0*result_emfits_filtered[:, i, 0], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color()) # curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color()) axes[i].grid() axes[i].set_xticks(np.linspace(0., 1.0, 5)) axes[i].set_xlim((0.0, 1.0)) # axes[i].set_yticks([]) # axes[i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1)) # axes[i].set_ylim((0.0, 1.0)) axes[i].locator_params(axis='y', tight=True, nbins=4) # all_lines_bis.extend(curr_lines) # f.subplots_adjust(right=0.75) # plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5)) if savefigs: dataio.save_current_figure('results_subplots_emkappa_{label}_global_{unique_id}.pdf') variables_to_save = [] if savedata: dataio.save_variables_default(locals(), variables_to_save) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='hierarchicalrandomnetwork_characterisation') plt.show() return locals()
def plots_specific_stimuli_mixed(data_pbs, generator_module=None): ''' Reload and plot behaviour of mixed population code on specific Stimuli of 3 items. ''' #### SETUP # savefigs = True savedata = True plot_per_min_dist_all = False specific_plots_paper = False specific_plots_emfits = 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(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1) result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results']) result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results']) result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results']) nb_repetitions = result_responses_all.shape[-1] K = result_nontargets_all.shape[-2] N = result_responses_all.shape[-2] enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance'] sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax'] ratio_space = data_pbs.loaded_data['datasets_list'][0]['ratio_space'] print enforce_min_distance_space print sigmax_space print ratio_space print result_all_precisions_mean.shape, result_em_fits_mean.shape print result_responses_all.shape dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) if plot_per_min_dist_all: # Do one plot per min distance. for min_dist_i, min_dist in enumerate(enforce_min_distance_space): # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist) if savefigs: dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Show log precision utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True) if savefigs: dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated model precision utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot estimated Target, nontarget and random mixture components, in multiple subplots _, axes = plt.subplots(1, 3, figsize=(18, 6)) plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5) utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5) if savefigs: dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) # Plot Log-likelihood of Mixture model, sanity check utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False) if savefigs: dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist) if specific_plots_paper: # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.15 target_mindist_medium = 0.36 target_mindist_high = 1.5 sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) ## Do for each distance for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]: # for min_dist_i in xrange(enforce_min_distance_space.size): # Plot precision utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])]) if savefigs: dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa fitted utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa') plt.ylim([-0.1, np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0])]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot kappa-stddev fitted. Easier to visualize utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) plt.ylim([0, 1.1*np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i])]) if savefigs: dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot LLH utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit') # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) if savefigs: dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Plot mixture parameters, std utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T) plt.ylim([0.0, 1.1]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) # Mixture parameters, SEM utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions)) plt.ylim([0.0, 1.1]) # plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i]) # plt.legend("Target", "Non-target", "Random") if savefigs: dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) if specific_plots_emfits: # We need to choose 3 levels of min_distances target_sigmax = 0.25 target_mindist_low = 0.15 target_mindist_medium = 0.36 target_mindist_high = 1.5 sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax)) min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low)) min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium)) min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high)) min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]) # kappa (K+1), mixt_target, mixt_nontargets (K), mixt_random, LL, bic result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan ## Do for each distance for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space): # Fit the mixture model for ratio_i, ratio in enumerate(ratio_space): print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i] em_fit = em_circularmixture_allitems.fit( result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(), result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K))) result_emfitallitems[min_dist_plotting_i, ratio_i] = em_fit['kappa'].tolist() + [em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')] # Plot now _, ax = plt.subplots() ax.plot(ratio_space, result_emfitallitems[min_dist_plotting_i, :, 3:7]) if savefigs: dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i]) all_args = data_pbs.loaded_data['args_list'] variables_to_save = ['nb_repetitions'] if savedata: dataio.save_variables_default(locals(), variables_to_save) dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli') plt.show() return locals()
def plots_memory_curves(data_pbs, generator_module=None): ''' Reload and plot memory curve of a feature code. Can use Marginal Fisher Information and fitted Mixture Model as well ''' #### SETUP # savefigs = True savedata = True plot_pcolor_fit_precision_to_fisherinfo = True plot_selected_memory_curves = 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(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1) result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1) result_marginal_inv_fi_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) result_marginal_inv_fi_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) result_marginal_fi_mean = utils.nanmean(1./np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) result_marginal_fi_std = utils.nanstd(1./np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1) M_space = data_pbs.loaded_data['parameters_uniques']['M'] sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax'] T_space = data_pbs.loaded_data['datasets_list'][0]['T_space'] 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 data_simult = load_experimental_data.load_data_simult(data_dir=os.path.normpath(os.path.join(os.path.split(load_experimental_data.__file__)[0], '../../experimental_data/'))) memory_experimental = data_simult['precision_nitems_theo'] # 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_diff_precision_margfi_1item = np.abs(result_all_precisions_mean[..., 0]*2. - result_marginal_fi_mean[..., 0, 0])**2. dist_diff_all_precision_margfi = np.abs(result_all_precisions_mean*2. - result_marginal_fi_mean[..., 0])**2. 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 - memory_experimental)**2., axis=-1) dist_diff_emkappa_experim = np.sum(np.abs(result_em_fits_mean[..., 0] - memory_experimental)**2., axis=-1) if plot_pcolor_fit_precision_to_fisherinfo: # Check fit between precision and fisher info utils.pcolor_2d_data(dist_diff_precision_margfi, log_scale=True, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax') if savefigs: dataio.save_current_figure('match_precision_margfi_log_pcolor_{label}_{unique_id}.pdf') # utils.pcolor_2d_data(dist_diff_precision_margfi, x=M_space, y=sigmax_space[2:], xlabel='M', ylabel='sigmax') # if savefigs: # dataio.save_current_figure('match_precision_margfi_pcolor_{label}_{unique_id}.pdf') utils.pcolor_2d_data(dist_ratio_precision_margfi[4:], x=M_space[4:], y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True) utils.pcolor_2d_data(dist_diff_emkappa_margfi, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True) utils.pcolor_2d_data(dist_ratio_emkappa_margfi[4:], x=M_space[4:], y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True) utils.pcolor_2d_data(dist_diff_precision_experim, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True) utils.pcolor_2d_data(dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True) utils.pcolor_2d_data(dist_diff_precision_margfi*dist_diff_emkappa_margfi*dist_diff_precision_experim*dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True) utils.pcolor_2d_data(dist_diff_precision_margfi_1item, log_scale=True, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax') if plot_selected_memory_curves: selected_values = [[20, 0.19], [40, 0.15], [100, 0.44], [140, 0.15], [40, 0.17], [60, 0.50]] 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)) ax = utils.plot_mean_std_area(T_space, memory_experimental, np.zeros(T_space.size), linewidth=3, fmt='o-', markersize=8) ax = utils.plot_mean_std_area(T_space, result_all_precisions_mean[M_i, sigmax_i], result_all_precisions_std[M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8) 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) 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='Kappa/Inverse variance', linewidth=3, fmt='o-', markersize=8) ax.set_title('M %d, sigmax %.2f' % (M_space[M_i], sigmax_space[sigmax_i])) plt.legend(['Experimental data', 'Precision of samples', 'Marginal Fisher Information', 'Fitted kappa']) ax.set_xlim([0.9, 5.1]) ax.set_xticks(range(1, 6)) ax.set_xticklabels(range(1, 6)) if savefigs: dataio.save_current_figure('memorycurves_M%dsigmax%.2f_{label}_{unique_id}.pdf' % (M_space[M_i], sigmax_space[sigmax_i])) all_args = data_pbs.loaded_data['args_list'] variables_to_save = ['result_all_precisions_mean', 'result_em_fits_mean', 'result_marginal_inv_fi_mean', 'result_all_precisions_std', 'result_em_fits_std', 'result_marginal_inv_fi_std', 'result_marginal_fi_mean', 'result_marginal_fi_std', 'M_space', 'sigmax_space', 'T_space', 'all_args'] if savedata: dataio.save_variables(variables_to_save, locals()) plt.show() return locals()
def plots_logposterior_mixed_autoset(data_pbs, generator_module=None): ''' Reload 2D volume runs from PBS and plot them ''' #### SETUP # savefigs = True plot_per_ratio = False plot_2d_pcolor = False do_relaunch_bestparams_pbs = True colormap = None # or 'cubehelix' plt.rcParams['font.size'] = 16 # #### /SETUP print "Order parameters: ", generator_module.dict_parameters_range.keys() result_log_posterior_mean = np.squeeze(data_pbs.dict_arrays['result_log_posterior_mean']['results']) result_log_posterior_std = np.squeeze(data_pbs.dict_arrays['result_log_posterior_std']['results']) ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj'] sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax'] exp_dataset = data_pbs.loaded_data['args_list'][0]['experiment_id'] print ratio_space print sigmax_space print result_log_posterior_mean.shape, result_log_posterior_std.shape dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename']) if plot_per_ratio: # Plot the evolution of loglike as a function of sigmax, with std shown for ratio_conj_i, ratio_conj in enumerate(ratio_space): ax = utils.plot_mean_std_area(sigmax_space, result_log_posterior_mean[ratio_conj_i], result_log_posterior_std[ratio_conj_i]) ax.get_figure().canvas.draw() if savefigs: dataio.save_current_figure('results_fitexp_%s_loglike_ratioconj%.2f_{label}_global_{unique_id}.pdf' % (exp_dataset, ratio_conj)) if plot_2d_pcolor: # Plot the mean loglikelihood as a 2d surface utils.pcolor_2d_data(result_log_posterior_mean, x=ratio_space, y=sigmax_space, xlabel="Ratio conj", ylabel="Sigma x", title="Loglikelihood of experimental data, \n3 items dualrecall, rcscale automatically set", ticks_interpolate=5, cmap=colormap) # plt.tight_layout() if savefigs: dataio.save_current_figure('results_fitexp_%s_loglike_2d_ratiosigmax_{label}_global_{unique_id}.pdf' % exp_dataset) if do_relaunch_bestparams_pbs: # Will check the best fitting parameters, and relaunch simulations for them, in order to get new cool plots. # We can actually use the original dictionary for submission informations, and just change the launcher to one produces the appropriate results generator_parameters_dict = generator_module.__dict__ generator_parameters_dict['pbs_submission_infos']['other_options'].update(dict( action_to_do='launcher_do_memory_curve_marginal_fi', subaction='collect_responses', inference_method='sample', N=300, T=6, num_samples=500, selection_method='last', num_repetitions=3, burn_samples=500, stimuli_generation='random', stimuli_generation_recall='random', session_id='fitting_experiments_relaunchs', result_computation='filenameoutput')) generator_parameters_dict['pbs_submission_infos']['other_options']['label'] = 'fitting_experiment_rerun_nitems{T}M{M}_090414'.format(T = generator_parameters_dict['n_items_to_fit'], M = generator_parameters_dict['M']) generator_parameters_dict['sleeping_period'] = dict(min=5, max=10) submit_pbs = submitpbs.SubmitPBS(pbs_submission_infos=generator_parameters_dict['pbs_submission_infos'], debug=True) # Now run a series of Jobs to obtain the required data # the "Result" of them are the filename of their output. Should then save those in a dictionary. # For later processing, if the exact same parameters are used, then everything will be reloaded automatically, MAAAGIC. # Extract the parameters to try best_params_to_try = [] best_axis2_i_all = np.argmax(result_log_posterior_mean, axis=1) for axis1_i, best_axis2_i in enumerate(best_axis2_i_all): parameter_dict = dict(ratio_conj=ratio_space[axis1_i], sigmax=sigmax_space[best_axis2_i]) best_params_to_try.append(parameter_dict) # Submit them, waiting on them in the process. Should obtain a list of filenames back utils.chdir_safe(generator_parameters_dict['pbs_submission_infos']['simul_out_dir']) result_minibatch_filenames = submit_pbs.submit_minibatch_jobswrapper(best_params_to_try, generator_parameters_dict) result_reloaded_datasets = [] for filename_i, result_filename in enumerate(result_minibatch_filenames): curr_reloaded_dataset = utils.load_npy(result_filename + '.npy') result_reloaded_datasets.append(curr_reloaded_dataset) utils.chdir_safe('../') all_args = data_pbs.loaded_data['args_list'] variables_to_save = ['exp_dataset'] if savefigs: dataio.save_variables_default(locals(), variables_to_save) plt.show() return locals()