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_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 fit_mixture_model(self):
# Initialize empty arrays
self.dataset['em_fits'] = dict(
kappa=np.empty(self.dataset['probe'].size),
mixt_target=np.empty(self.dataset['probe'].size),
mixt_nontarget=np.empty(self.dataset['probe'].size),
mixt_random=np.empty(self.dataset['probe'].size),
resp_target=np.empty(self.dataset['probe'].size),
resp_nontarget=np.empty(self.dataset['probe'].size),
resp_random=np.empty(self.dataset['probe'].size),
train_LL=np.empty(self.dataset['probe'].size),
test_LL=np.empty(self.dataset['probe'].size))
for key in self.dataset['em_fits']:
self.dataset['em_fits'][key].fill(np.nan)
self.dataset['target'] = np.empty(self.dataset['probe'].size)
self.dataset['em_fits_subjects_nitems'] = dict()
for subject in np.unique(self.dataset['subject']):
self.dataset['em_fits_subjects_nitems'][subject] = dict()
self.dataset['em_fits_nitems'] = dict(mean=dict(), std=dict(), values=dict())
# Compute mixture model fits per n_items and per subject
for n_items in np.unique(self.dataset['n_items']):
for subject in np.unique(self.dataset['subject']):
ids_filtered = (self.dataset['subject']==subject).flatten() & (self.dataset['n_items'] == n_items).flatten()
print "Fit mixture model, %d items, subject %d, %d datapoints" % (subject, n_items, np.sum(ids_filtered))
self.dataset['target'][ids_filtered] = self.dataset['item_angle'][ids_filtered, 0]
# params_fit = em_circularmixture.fit(self.dataset['response'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:])
params_fit = em_circularmixture_allitems_uniquekappa.fit(self.dataset['response'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:])
params_fit['mixt_nontargets_sum'] = np.sum(params_fit['mixt_nontargets'])
# print self.dataset['response'][ids_filtered, 0].shape, self.dataset['item_angle'][ids_filtered, 0].shape, self.dataset['item_angle'][ids_filtered, 1:].shape
# cross_valid_outputs = em_circularmixture.cross_validation_kfold(self.dataset['response'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:], K=10, shuffle=True, debug=False)
# params_fit = cross_valid_outputs['best_fit']
# resp = em_circularmixture.compute_responsibilities(self.dataset['response'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:], params_fit)
resp = em_circularmixture_allitems_uniquekappa.compute_responsibilities(self.dataset['response'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 0], self.dataset['item_angle'][ids_filtered, 1:], params_fit)
self.dataset['em_fits']['kappa'][ids_filtered] = params_fit['kappa']
self.dataset['em_fits']['mixt_target'][ids_filtered] = params_fit['mixt_target']
# self.dataset['em_fits']['mixt_nontarget'][ids_filtered] = params_fit['mixt_nontargets']
self.dataset['em_fits']['mixt_nontarget'][ids_filtered] = params_fit['mixt_nontargets_sum']
self.dataset['em_fits']['mixt_random'][ids_filtered] = params_fit['mixt_random']
self.dataset['em_fits']['resp_target'][ids_filtered] = resp['target']
self.dataset['em_fits']['resp_nontarget'][ids_filtered] = np.sum(resp['nontargets'], axis=1)
self.dataset['em_fits']['resp_random'][ids_filtered] = resp['random']
self.dataset['em_fits']['train_LL'][ids_filtered] = params_fit['train_LL']
# self.dataset['em_fits']['test_LL'][ids_filtered] = cross_valid_outputs['best_test_LL']
self.dataset['em_fits_subjects_nitems'][subject][n_items] = params_fit
## Now compute mean/std em_fits per n_items
self.dataset['em_fits_nitems']['mean'][n_items] = dict()
self.dataset['em_fits_nitems']['std'][n_items] = dict()
self.dataset['em_fits_nitems']['values'][n_items] = dict()
# Need to extract the values for a subject/nitems pair, for all keys of em_fits. Annoying dictionary indexing needed
emfits_keys = params_fit.keys()
for key in emfits_keys:
values_allsubjects = [self.dataset['em_fits_subjects_nitems'][subject][n_items][key] for subject in np.unique(self.dataset['subject'])]
self.dataset['em_fits_nitems']['mean'][n_items][key] = np.mean(values_allsubjects)
self.dataset['em_fits_nitems']['std'][n_items][key] = np.std(values_allsubjects)
self.dataset['em_fits_nitems']['values'][n_items][key] = values_allsubjects
## Construct array versions of the em_fits_nitems mixture proportions, for convenience
self.construct_arrays_em_fits()
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()