def setup_experimental_stimuli_T(self, T):
'''
Setup everything needed (Sampler, etc) and then force a human experimental dataset.
If already setup correctly, do nothing.
'''
assert T in self.T_space, "T=%d not possible. %s" % (T, self.T_space)
if self.enforced_T != T:
self.enforced_T = T
if T not in self.all_samplers:
print "\n>>> Setting up {} nitems, {} datapoints".format(T, self.num_datapoints)
# Update parameters
self.parameters['T'] = T
self.parameters['N'] = self.num_datapoints
self.parameters['fixed_cued_feature_time'] = self.experiment_data_to_fit[T]['probe'][0] # should be scalar
self.parameters['stimuli_to_use'] = self.experiment_data_to_fit[T]['item_features'][self.filter_datapoints_mask]
# Instantiate everything
(_, _, _, newSampler) = launchers.init_everything(self.parameters)
# Fix responses to the human ones
newSampler.set_theta(self.experiment_data_to_fit[T]['response'][self.filter_datapoints_mask])
# Store it
self.all_samplers[self.enforced_T] = newSampler
self.sampler = self.all_samplers[self.enforced_T]
def test_loglike_fit():
"""
Check if the LL computation is correct
Use specific data, generated from a given model. This model should then have max LL.
"""
# Get a specific model, with given ratio and sigmax
experiment_parameters = dict(
action_to_do="launcher_do_simple_run",
inference_method="sample",
T=2,
M=200,
N=400,
num_samples=500,
selection_method="last",
sigmax=0.15,
sigmay=0.0001,
code_type="mixed",
ratio_conj=0.6,
output_directory=".",
stimuli_generation_recall="random",
autoset_parameters=None,
)
experiment_launcher = experimentlauncher.ExperimentLauncher(run=True, arguments_dict=experiment_parameters)
experiment_parameters_full = experiment_launcher.args_dict
sampler = experiment_launcher.all_vars["sampler"]
# Keep its dataset and responses
stimuli_correct_to_force = sampler.data_gen.stimuli_correct.copy()
response_to_force = sampler.theta[:, 0].copy()
LL_target = sampler.compute_loglikelihood()
experiment_parameters_full["stimuli_to_use"] = stimuli_correct_to_force
ratio_space = np.linspace(0.0, 1.0, 31.0)
LL_all_new = np.zeros(ratio_space.shape)
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
experiment_parameters_full["ratio_conj"] = ratio_conj
_, _, _, sampler = launchers.init_everything(experiment_parameters_full)
# Set responses
sampler.set_theta(response_to_force)
# Compute LL
# LL_all_new[ratio_conj_i] = sampler.compute_loglikelihood()
LL_all_new[ratio_conj_i] = sampler.compute_loglikelihood_top90percent()
# Print result
print LL_all_new[ratio_conj_i]
print LL_target
print ratio_space, LL_all_new
print ratio_space[np.argmax(LL_all_new)]
return locals()
def setup_experimental_stimuli(self, T, trecall):
'''
Setup everything needed (Sampler, etc) and then force a human experimental dataset.
If already setup correctly, do nothing.
'''
assert T in self.T_space, "T=%d not possible. %s" % (T, self.T_space)
if self.enforced_T != T or self.enforced_trecall != trecall:
self.enforced_T = T
self.enforced_trecall = trecall
if (T, trecall) not in self.all_samplers:
print "\n>>> Setting up {} nitems, {} trecall, {} datapoints".format(T, trecall, self.num_datapoints)
# Update parameters
self.parameters['T'] = T
self.parameters['N'] = self.num_datapoints
self.parameters['fixed_cued_feature_time'] = T - trecall
self.parameters['stimuli_to_use'] = (
self.experiment_data_to_fit[T][trecall]['item_features'][
self.filter_datapoints_mask])
# Instantiate everything
(_, _, _, self.sampler) = launchers.init_everything(self.parameters)
# Fix responses to the human ones
self.sampler.set_theta(
self.experiment_data_to_fit[T][trecall]['responses'][
self.filter_datapoints_mask])
self.store_responses('human')
# Store it
self.all_samplers[(T, trecall)] = self.sampler
self.sampler = self.all_samplers[
(self.enforced_T, self.enforced_trecall)]
def launcher_do_mixed_varyratio_precision_pbs(args):
"""
Compare the evolution of the precision curve as the number of neurons in a mixed network changes.
"""
print "Doing a piece of work for launcher_do_mixed_varyratio_precision_pbs"
save_all_output = False
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
code_type = "mixed"
dataio = DataIO(
output_folder=all_parameters["output_directory"], label=all_parameters["label"].format(**all_parameters)
)
save_every = 5
run_counter = 0
num_repetitions = all_parameters["num_repetitions"]
ratio_space = np.array([all_parameters["ratio_conj"]])
T_space = np.arange(1, all_parameters["T"] + 1)
results_precision_ratio_T = np.nan * np.empty((ratio_space.size, T_space.size, num_repetitions), dtype=float)
# if save_all_output:
# results_all_responses = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, num_repetitions, all_parameters['N']))
# results_all_targets = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, num_repetitions, all_parameters['N']))
# results_all_nontargets = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, num_repetitions, all_parameters['N'], all_parameters['T']-1))
# Show the progress
search_progress = progress.Progress(T_space.size * ratio_space.size * num_repetitions)
print T_space
print ratio_space
for repet_i in xrange(num_repetitions):
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
for t_i, t in enumerate(T_space):
# Will estimate the precision
print "Precision as function of N, hierarchical network, T: %d/%d, ratio_conj %.2f, (%d/%d). %.2f%%, %s left - %s" % (
t,
T_space[-1],
ratio_conj,
repet_i + 1,
num_repetitions,
search_progress.percentage(),
search_progress.time_remaining_str(),
search_progress.eta_str(),
)
# Current parameter values
all_parameters["T"] = t
all_parameters["code_type"] = code_type
all_parameters["ratio_conj"] = ratio_conj
### WORK UNIT
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
if all_parameters["inference_method"] == "sample":
# Sample thetas
sampler.sample_theta(
num_samples=all_parameters["num_samples"],
burn_samples=100,
selection_method=all_parameters["selection_method"],
selection_num_samples=all_parameters["selection_num_samples"],
integrate_tc_out=False,
debug=False,
)
elif all_parameters["inference_method"] == "max_lik":
# Just use the ML value for the theta
sampler.set_theta_max_likelihood(num_points=100, post_optimise=True)
results_precision_ratio_T[ratio_conj_i, t_i, repet_i] = sampler.get_precision()
print results_precision_ratio_T[ratio_conj_i, t_i, repet_i]
# if save_all_output:
# (results_all_responses[ratio_conj_i, t_i, repet_i], results_all_targets[ratio_conj_i, t_i, repet_i], results_all_nontargets[ratio_conj_i, t_i, repet_i, :, :t_i]) = sampler.collect_responses()
### DONE WORK UNIT
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
print "All finished"
return locals()
def plots_ratioMscaling(data_pbs, generator_module=None):
'''
Reload and plot precision/fits of a Mixed code.
'''
#### SETUP
#
savefigs = True
savedata = True
plots_pcolor_all = False
plots_effect_M_target_precision = False
plots_multiple_precisions = False
plots_effect_M_target_kappa = False
plots_subpopulations_effects = False
plots_subpopulations_effects_kappa_fi = True
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()
result_all_precisions_mean = (utils.nanmean(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_all_precisions_std = (utils.nanstd(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_em_fits_mean = (utils.nanmean(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
result_em_fits_std = (utils.nanstd(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
all_args = data_pbs.loaded_data['args_list']
result_em_fits_kappa = result_em_fits_mean[..., 0]
M_space = data_pbs.loaded_data['parameters_uniques']['M'].astype(int)
ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']
num_repetitions = generator_module.num_repetitions
print M_space
print ratio_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape
dataio = DataIO.DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
target_precision = 100.
dist_to_target_precision = (result_all_precisions_mean - target_precision)**2.
best_dist_to_target_precision = np.argmin(dist_to_target_precision, axis=1)
MAX_DISTANCE = 100.
ratio_target_precision_given_M = np.ma.masked_where(dist_to_target_precision[np.arange(dist_to_target_precision.shape[0]), best_dist_to_target_precision] > MAX_DISTANCE, ratio_space[best_dist_to_target_precision])
if plots_pcolor_all:
# Check evolution of precision given M and ratio
utils.pcolor_2d_data(result_all_precisions_mean, log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='precision wrt M / ratio')
if savefigs:
dataio.save_current_figure('precision_log_pcolor_{label}_{unique_id}.pdf')
# See distance to target precision evolution
utils.pcolor_2d_data(dist_to_target_precision, log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='Dist to target precision %d' % target_precision)
if savefigs:
dataio.save_current_figure('dist_targetprecision_log_pcolor_{label}_{unique_id}.pdf')
# Show kappa
utils.pcolor_2d_data(result_em_fits_kappa, log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='kappa wrt M / ratio')
if savefigs:
dataio.save_current_figure('kappa_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data((result_em_fits_kappa - 200)**2., log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='dist to kappa')
if savefigs:
dataio.save_current_figure('dist_kappa_log_pcolor_{label}_{unique_id}.pdf')
if plots_effect_M_target_precision:
def plot_ratio_target_precision(ratio_target_precision_given_M, target_precision):
f, ax = plt.subplots()
ax.plot(M_space, ratio_target_precision_given_M)
ax.set_xlabel('M')
ax.set_ylabel('Optimal ratio')
ax.set_title('Optimal Ratio for precison %d' % target_precision)
if savefigs:
dataio.save_current_figure('effect_ratio_M_targetprecision%d_{label}_{unique_id}.pdf' % target_precision)
plot_ratio_target_precision(ratio_target_precision_given_M, target_precision)
if plots_multiple_precisions:
target_precisions = np.array([100, 200, 300, 500, 1000])
for target_precision in target_precisions:
dist_to_target_precision = (result_all_precisions_mean - target_precision)**2.
best_dist_to_target_precision = np.argmin(dist_to_target_precision, axis=1)
ratio_target_precision_given_M = np.ma.masked_where(dist_to_target_precision[np.arange(dist_to_target_precision.shape[0]), best_dist_to_target_precision] > MAX_DISTANCE, ratio_space[best_dist_to_target_precision])
# replot
plot_ratio_target_precision(ratio_target_precision_given_M, target_precision)
if plots_effect_M_target_kappa:
def plot_ratio_target_kappa(ratio_target_kappa_given_M, target_kappa):
f, ax = plt.subplots()
ax.plot(M_space, ratio_target_kappa_given_M)
ax.set_xlabel('M')
ax.set_ylabel('Optimal ratio')
ax.set_title('Optimal Ratio for precison %d' % target_kappa)
if savefigs:
dataio.save_current_figure('effect_ratio_M_targetkappa%d_{label}_{unique_id}.pdf' % target_kappa)
target_kappa = np.array([100, 200, 300, 500, 1000, 3000])
for target_kappa in target_kappa:
dist_to_target_kappa = (result_em_fits_kappa - target_kappa)**2.
best_dist_to_target_kappa = np.argmin(dist_to_target_kappa, axis=1)
ratio_target_kappa_given_M = np.ma.masked_where(dist_to_target_kappa[np.arange(dist_to_target_kappa.shape[0]), best_dist_to_target_kappa] > MAX_DISTANCE, ratio_space[best_dist_to_target_kappa])
# replot
plot_ratio_target_kappa(ratio_target_kappa_given_M, target_kappa)
if plots_subpopulations_effects:
# result_all_precisions_mean
for M_tot_selected_i, M_tot_selected in enumerate(M_space[::2]):
M_conj_space = ((1.-ratio_space)*M_tot_selected).astype(int)
M_feat_space = M_tot_selected - M_conj_space
f, axes = plt.subplots(2, 2)
axes[0, 0].plot(ratio_space, result_all_precisions_mean[2*M_tot_selected_i])
axes[0, 0].set_xlabel('ratio')
axes[0, 0].set_title('Measured precision')
axes[1, 0].plot(ratio_space, M_conj_space**2*M_feat_space)
axes[1, 0].set_xlabel('M_feat_size')
axes[1, 0].set_title('M_c**2*M_f')
axes[0, 1].plot(ratio_space, M_conj_space**2.)
axes[0, 1].set_xlabel('M')
axes[0, 1].set_title('M_c**2')
axes[1, 1].plot(ratio_space, M_feat_space)
axes[1, 1].set_xlabel('M')
axes[1, 1].set_title('M_f')
f.suptitle('M_tot %d' % M_tot_selected, fontsize=15)
f.set_tight_layout(True)
if savefigs:
dataio.save_current_figure('scaling_precision_subpop_Mtot%d_{label}_{unique_id}.pdf' % M_tot_selected)
plt.close(f)
if plots_subpopulations_effects_kappa_fi:
# 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_Mratio = cached_data['result_fisherinfo_Mratio']
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_Mratio = np.empty((M_space.size, ratio_space.size))
# Invert the all_args_i -> M, ratio_conj direction
parameters_indirections = data_pbs.loaded_data['parameters_dataset_index']
for M_i, M in enumerate(M_space):
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)
try:
arg_index = parameters_indirections[(M, 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)
(_, _, _, sampler) = launchers.init_everything(curr_args)
# Theo Fisher info
result_fisherinfo_Mratio[M_i, ratio_conj_i] = sampler.estimate_fisher_info_theocov()
# del curr_args['stimuli_generation']
except KeyError:
result_fisherinfo_Mratio[M_i, ratio_conj_i] = np.nan
# 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_Mratio=result_fisherinfo_Mratio)
pickle.dump(data_cache, filecache_out, protocol=2)
except IOError:
print "Error writing out to caching file ", caching_fisherinfo_filename
# result_em_fits_kappa
if False:
for M_tot_selected_i, M_tot_selected in enumerate(M_space[::2]):
M_conj_space = ((1.-ratio_space)*M_tot_selected).astype(int)
M_feat_space = M_tot_selected - M_conj_space
f, axes = plt.subplots(2, 2)
axes[0, 0].plot(ratio_space, result_em_fits_kappa[2*M_tot_selected_i])
axes[0, 0].set_xlabel('ratio')
axes[0, 0].set_title('Fitted kappa')
axes[1, 0].plot(ratio_space, utils.stddev_to_kappa(1./result_fisherinfo_Mratio[2*M_tot_selected_i]**0.5))
axes[1, 0].set_xlabel('M_feat_size')
axes[1, 0].set_title('kappa_FI_mixed')
f.suptitle('M_tot %d' % M_tot_selected, fontsize=15)
f.set_tight_layout(True)
if savefigs:
dataio.save_current_figure('scaling_kappa_subpop_Mtot%d_{label}_{unique_id}.pdf' % M_tot_selected)
plt.close(f)
utils.pcolor_2d_data((result_fisherinfo_Mratio- 2000)**2., log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='Fisher info')
if savefigs:
dataio.save_current_figure('dist2000_fi_log_pcolor_{label}_{unique_id}.pdf')
all_args = data_pbs.loaded_data['args_list']
variables_to_save = []
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='ratio_scaling_M')
plt.show()
return locals()
def launcher_do_error_distributions_allT(args):
'''
Compute histograms of errors distributions. Also get histogram of bias to nontargets.
Do it for t=1...T items.
Looks like the Bays 2009, used in paper.
'''
print "Doing a piece of work for launcher_do_error_distributions_allT"
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
print all_parameters
# Create DataIO
# (complete label with current variable state)
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
bins = 51
# Parameters to vary
T_all = all_parameters['T']
T_space = np.arange(1, T_all+1)
# Result arrays
result_responses = np.nan*np.ones((T_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_target = np.nan*np.ones((T_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_nontargets = np.nan*np.ones((T_space.size, all_parameters['N'], T_all-1, all_parameters['num_repetitions']))
result_em_fits = np.nan*np.ones((T_space.size, 5, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll
search_progress = progress.Progress(T_space.size*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for T_i, T in enumerate(T_space):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for T=%d, %d/%d" % (T, repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['T'] = T
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Collect and store responses
(responses, target, nontarget) = sampler.collect_responses()
result_responses[T_i, :, repet_i] = responses
result_target[T_i, :, repet_i] = target
result_nontargets[T_i, :, :T_i, repet_i] = nontarget[:, :T_i]
# Fit mixture model
curr_params_fit = em_circularmixture.fit(*sampler.collect_responses())
result_em_fits[T_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random', 'train_LL')]
# Do plots
sampler.plot_histogram_errors(bins=bins)
dataio.save_current_figure('papertheo_histogram_errorsM%dsigmax%.2fT%d_{label}_{unique_id}.pdf' % tuple([all_parameters[key] for key in ('M', 'sigmax', 'T')]))
if T > 1:
sampler.plot_histogram_bias_nontarget(dataio=dataio)
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_average_posterior(args):
'''
Compute average posterior for a fixed set of stimuli.
Show graphically how it looks like.
'''
# TODO Could analyse it theoretically at some point, e.g. probability of answering nontarget?
print "Doing a piece of work for launcher_do_average_posterior"
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
# Create DataIO
# (complete label with current variable state)
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
# Fix some parameters
all_parameters['stimuli_generation'] = lambda T: np.linspace(-np.pi*0.6, np.pi*0.6, T)
all_parameters['stimuli_generation_recall'] = 'random'
all_parameters['enforce_first_stimulus'] = False
num_points = 500
if 'do_precision' in all_parameters:
do_precision = all_parameters['do_precision']
else:
do_precision = True
result_all_log_posterior = np.nan*np.ones((all_parameters['N'], num_points))
result_all_thetas = np.nan*np.empty(all_parameters['N'])
search_progress = progress.Progress(all_parameters['N'])
save_every = 10
print_every = 10
run_counter = 0
ax_handle = None
plt.ion()
# all_parameters['rc_scale'] = rc_scale
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
### WORK WORK WORK work? ###
all_angles = np.linspace(-np.pi, np.pi, num_points)
if do_precision:
print 'Precision...'
sampler.sample_theta(num_samples=all_parameters['num_samples'], burn_samples=all_parameters['burn_samples'], selection_method=all_parameters['selection_method'], selection_num_samples=all_parameters['selection_num_samples'], integrate_tc_out=False, debug=True)
result_all_thetas, targets, nontargets = sampler.collect_responses()
print "Average posterior..."
for n in xrange(all_parameters['N']):
if run_counter % print_every == 0:
print "%.2f%% %s/%s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
result_all_log_posterior[n] = sampler.compute_likelihood_fullspace(n=n, all_angles=all_angles, num_points=num_points, should_exponentiate=False, remove_mean=True)[:, -1].T
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
# Plots
# plt.figure(1)
# plt.plot(result_all_log_posterior.T, hold=False)
# ax_handle = plot_mean_std_area(all_angles, nanmean(result_all_log_posterior[ axis=0), nanstd(result_all_log_posterior[ axis=0), ax_handle=ax_handle)
# ax_handle.hold(False)
# dataio.save_current_figure('FI_compare_theo_finite-precisionvstheo-{label}_{unique_id}.pdf')
run_counter += 1
#### Plots ###
plot_mean_std_area(all_angles, nanmean(result_all_log_posterior, axis=0), nanstd(result_all_log_posterior, axis=0), ax_handle=ax_handle)
dataio.save_current_figure('avg_posterior-posterior-{label}_{unique_id}.pdf')
if do_precision:
sampler.plot_histogram_errors(bins=50, nice_xticks=True)
dataio.save_current_figure('avg_posterior-hist_errors-{label}_{unique_id}.pdf')
sampler.plot_histogram_responses(bins=50, show_angles=True, nice_xticks=True)
dataio.save_current_figure('avg_posterior-hist_responses-{label}_{unique_id}.pdf')
print "All finished"
plt.show()
return locals()
def launcher_do_fit_mixturemodels_sequential_alltrecall(args):
'''
Run the model for 1..T items sequentially, for all possible trecall/T.
Compute:
- Precision of samples
- EM mixture model fits. Both independent and collapsed model.
- Theoretical Fisher Information
- EM Mixture model distances to set of currently working datasets.
'''
print "Doing a piece of work for launcher_do_fit_mixturemodels_sequential_alltrecall"
all_parameters = utils.argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
# Create DataIO
# (complete label with current variable state)
dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Load dataset to compare against
data_gorgo11_sequ = load_experimental_data.load_data_gorgo11_sequential(data_dir=all_parameters['experiment_data_dir'], fit_mixture_model=True)
gorgo11_sequ_T_space = np.unique(data_gorgo11_sequ['n_items'])
# Parameters to vary
T_max = all_parameters['T']
T_space = np.arange(1, T_max+1)
repetitions_axis = -1
# Result arrays
result_all_precisions = np.nan*np.empty((T_space.size, T_space.size, all_parameters['num_repetitions']))
result_fi_theo = np.nan*np.empty((T_space.size, T_space.size, all_parameters['num_repetitions']))
result_fi_theocov = np.nan*np.empty((T_space.size, T_space.size, all_parameters['num_repetitions']))
result_em_fits = np.nan*np.empty((T_space.size, T_space.size, 6, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll, bic
result_em_fits_collapsed_tr = np.nan*np.empty((T_space.size, T_space.size, 4, all_parameters['num_repetitions'])) # kappa_tr, mixt_target_tr, mixt_nontarget_tr, mixt_random_tr
result_em_fits_collapsed_summary = np.nan*np.empty((5, all_parameters['num_repetitions'])) # bic, ll, kappa_theta
result_dist_gorgo11_sequ = np.nan*np.empty((T_space.size, T_space.size, 4, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random
result_dist_gorgo11_sequ_emmixt_KL = np.nan*np.empty((T_space.size, T_space.size, all_parameters['num_repetitions']))
result_dist_gorgo11_sequ_collapsed = np.nan*np.empty((T_space.size, T_space.size, 4, all_parameters['num_repetitions']))
result_dist_gorgo11_sequ_collapsed_emmixt_KL = np.nan*np.empty((T_space.size, T_space.size, all_parameters['num_repetitions']))
gorgo11_sequ_collapsed_mixtmod_mean = data_gorgo11_sequ['collapsed_em_fits_doublepowerlaw_array']
# If desired, will automatically save all Model responses.
if all_parameters['collect_responses']:
print "--- Collecting all responses..."
result_responses = np.nan*np.empty((T_space.size, T_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_target = np.nan*np.empty((T_space.size, T_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_nontargets = np.nan*np.empty((T_space.size, T_space.size, all_parameters['N'], T_max-1, all_parameters['num_repetitions']))
search_progress = progress.Progress(T_space.size*(T_space.size + 1)/2.*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for T_i, T in enumerate(T_space):
for trecall_i, trecall in enumerate(np.arange(T, 0, -1)):
# Inverting indexing of trecall, to be consistent. trecall_i 0 == last item.
# But trecall still means the actual time of recall!
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for T=%d, tr=%d, %d/%d" % (T, trecall, repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['T'] = T
all_parameters['fixed_cued_feature_time'] = trecall - 1
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[T_i, trecall_i, repet_i] = sampler.get_precision()
# Fit mixture model, independent
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)
result_em_fits[T_i, trecall_i, :, repet_i] = [curr_params_fit[key] for key in ['kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL', 'bic']]
# Compute fisher info
print "compute fisher info"
result_fi_theo[T_i, trecall_i, repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=False)
result_fi_theocov[T_i, trecall_i, repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=True)
# Compute distances to datasets (this is for the non-collapsed stuff, not the best)
if T in gorgo11_sequ_T_space:
gorgo11_sequ_mixtures_mean = data_gorgo11_sequ['em_fits_nitems_trecall_arrays'][gorgo11_sequ_T_space==T, trecall_i, :4].flatten()
result_dist_gorgo11_sequ[T_i, trecall_i, :, repet_i] = (gorgo11_sequ_mixtures_mean - result_em_fits[T_i, trecall_i, :4, repet_i])**2.
result_dist_gorgo11_sequ_emmixt_KL[T_i, trecall_i, repet_i] = utils.KL_div(result_em_fits[T_i, trecall_i, 1:4, repet_i], gorgo11_sequ_mixtures_mean[1:])
# If needed, store responses
if all_parameters['collect_responses']:
print "collect responses"
(responses, target, nontarget) = sampler.collect_responses()
result_responses[T_i, trecall_i, :, repet_i] = responses
result_target[T_i, trecall_i, :, repet_i] = target
result_nontargets[T_i, trecall_i, :, :T_i, repet_i] = nontarget
print "CURRENT RESULTS:\n", result_all_precisions[T_i, trecall_i, repet_i], curr_params_fit, result_fi_theo[T_i, trecall_i, repet_i], result_fi_theocov[T_i, trecall_i, repet_i], np.sum(result_dist_gorgo11_sequ[T_i, trecall_i, :, repet_i]), np.sum(result_dist_gorgo11_sequ_emmixt_KL[T_i, trecall_i, repet_i]), "\n"
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Fit Collapsed mixture model
# TODO check dimensionality...
print 'Fitting Collapsed double powerlaw mixture model...'
params_fit = em_circularmixture_parametrickappa_doublepowerlaw.fit(T_space, result_responses[..., repet_i], result_target[..., repet_i], result_nontargets[..., repet_i], debug=False)
# First store the parameters that depend on T/trecall
for i, key in enumerate(['kappa', 'mixt_target_tr', 'mixt_nontargets_tr', 'mixt_random_tr']):
result_em_fits_collapsed_tr[..., i, repet_i] = params_fit[key]
# Then the ones that do not, only one per full collapsed fit.
result_em_fits_collapsed_summary[0, repet_i] = params_fit['bic']
# result_em_fits_collapsed_summary[1, repet_i] = params_fit['train_LL']
result_em_fits_collapsed_summary[2:, repet_i] = params_fit['kappa_theta']
# Compute distances to dataset for collapsed model
result_dist_gorgo11_sequ_collapsed[..., repet_i] = (gorgo11_sequ_collapsed_mixtmod_mean - result_em_fits_collapsed_tr[..., repet_i])**2.
result_dist_gorgo11_sequ_collapsed_emmixt_KL[..., repet_i] = utils.KL_div(result_em_fits_collapsed_tr[..., 1:4, repet_i], gorgo11_sequ_collapsed_mixtmod_mean[..., 1:], axis=-1)
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_fit_mixturemodel_dualrecall(args):
'''
Run the model for T items, trying to fit
the DualRecall dataset, which has two conditions.
Get:
- Precision
- EM mixture model fits
- Theoretical Fisher Information
- EM Mixture model distances
'''
print "Doing a piece of work for launcher_do_fit_mixturemodel_dualrecall"
all_parameters = utils.argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
# Create DataIO
# (complete label with current variable state)
dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Load datasets to compare against
data_dualrecall = load_experimental_data.load_data_dualrecall(data_dir=all_parameters['experiment_data_dir'], fit_mixture_model=True)
dualrecall_T_space = data_dualrecall['data_to_fit']['n_items']
dualrecall_experimental_angle_emfits_mean = data_dualrecall['em_fits_angle_nitems_arrays']['mean']
dualrecall_experimental_colour_emfits_mean = data_dualrecall['em_fits_colour_nitems_arrays']['mean']
# Parameters to vary
repetitions_axis = -1
# Result arrays
result_all_precisions = np.nan*np.empty((all_parameters['num_repetitions']))
result_fi_theo = np.nan*np.empty((all_parameters['num_repetitions']))
result_fi_theocov = np.nan*np.empty((all_parameters['num_repetitions']))
result_em_fits = np.nan*np.empty((6, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll, bic
result_dist_dualrecall_angle = np.nan*np.empty((4, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random
result_dist_dualrecall_angle_emmixt_KL = np.nan*np.empty((all_parameters['num_repetitions']))
result_dist_dualrecall_colour = np.nan*np.empty((4, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random
result_dist_dualrecall_colour_emmixt_KL = np.nan*np.empty((all_parameters['num_repetitions']))
# If desired, will automatically save all Model responses.
if all_parameters['collect_responses']:
print "--- Collecting all responses..."
result_responses = np.nan*np.ones((all_parameters['N'], all_parameters['num_repetitions']))
result_target = np.nan*np.ones((all_parameters['N'], all_parameters['num_repetitions']))
result_nontargets = np.nan*np.ones((all_parameters['N'], all_parameters['T'] - 1, all_parameters['num_repetitions']))
search_progress = progress.Progress(all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for T=%d, %d/%d" % (all_parameters['T'], repet_i+1, all_parameters['num_repetitions'])
## Update parameter
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[repet_i] = sampler.get_precision()
# Fit mixture model
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)
# curr_params_fit['mixt_nontargets_sum'] = np.sum(curr_params_fit['mixt_nontargets'])
result_em_fits[:, repet_i] = [curr_params_fit[key] for key in ['kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL', 'bic']]
# Compute fisher info
print "compute fisher info"
result_fi_theo[repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=False)
result_fi_theocov[repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=True)
# Compute distances to datasets
if all_parameters['T'] in dualrecall_T_space:
# Angle trials
result_dist_dualrecall_angle[:, repet_i] = (dualrecall_experimental_angle_emfits_mean[:, dualrecall_T_space == all_parameters['T']].flatten() - result_em_fits[:4, repet_i])**2.
result_dist_dualrecall_angle_emmixt_KL[repet_i] = utils.KL_div(result_em_fits[1:4, repet_i], dualrecall_experimental_angle_emfits_mean[1:, dualrecall_T_space==all_parameters['T']].flatten())
# Colour trials
result_dist_dualrecall_colour[:, repet_i] = (dualrecall_experimental_colour_emfits_mean[:, dualrecall_T_space == all_parameters['T']].flatten() - result_em_fits[:4, repet_i])**2.
result_dist_dualrecall_colour_emmixt_KL[repet_i] = utils.KL_div(result_em_fits[1:4, repet_i], dualrecall_experimental_colour_emfits_mean[1:, dualrecall_T_space==all_parameters['T']].flatten())
# If needed, store responses
if all_parameters['collect_responses']:
(responses, target, nontarget) = sampler.collect_responses()
result_responses[:, repet_i] = responses
result_target[:, repet_i] = target
result_nontargets[..., repet_i] = nontarget
print "collected responses"
print "CURRENT RESULTS:\n", result_all_precisions[repet_i], curr_params_fit, result_fi_theo[repet_i], result_fi_theocov[repet_i], np.sum(result_dist_dualrecall_angle[:, repet_i]), np.sum(result_dist_dualrecall_colour[:, repet_i]), "\n"
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_noise_output_effect_allT(args):
'''
Run the model for 1..T items, varying sigma_output
'''
print "Doing a piece of work for launcher_do_noise_output_effect_allT"
all_parameters = utils.argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
if 'plots_during_simulation_callback' in all_parameters:
plots_during_simulation_callback = all_parameters['plots_during_simulation_callback']
del all_parameters['plots_during_simulation_callback']
else:
plots_during_simulation_callback = None
# Create DataIO
# (complete label with current variable state)
dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Parameters to vary
T_max = all_parameters['T']
T_space = np.arange(1, T_max+1)
repetitions_axis = -1
# Parameters to vary
precision_sigmaoutput = 20
sigmaoutput_space = np.linspace(0.0, 0.5, precision_sigmaoutput)
# Result arrays
result_all_precisions = np.nan*np.ones((sigmaoutput_space.size, T_max, all_parameters['num_repetitions']))
result_em_fits = np.nan*np.ones((sigmaoutput_space.size, T_max, 6, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll, bic
search_progress = progress.Progress(sigmaoutput_space.size*T_max*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for sigmaoutput_i, sigma_output in enumerate(sigmaoutput_space):
for T_i, T in enumerate(T_space):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for sigma_output=%.3f, T %d, %d/%d" % (sigma_output, T, repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['sigma_output'] = sigma_output
all_parameters['T'] = T
### WORK WORK WORK work? ###
# Fix some parameters
# all_parameters['stimuli_generation'] = 'separated'
# all_parameters['slice_width'] = np.pi/64.
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[sigmaoutput_i, T_i, repet_i] = sampler.get_precision()
# Fit mixture model
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)
result_em_fits[sigmaoutput_i, T_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL', 'bic')]
print result_all_precisions[sigmaoutput_i, T_i, repet_i], curr_params_fit
## Run callback function if exists
if plots_during_simulation_callback:
print "Doing plots..."
try:
# Best super safe, if this fails then the simulation must continue!
plots_during_simulation_callback['function'](locals(), plots_during_simulation_callback['parameters'])
print "plots done."
except:
print "error during plotting callback function", plots_during_simulation_callback['function'], plots_during_simulation_callback['parameters']
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_fitexperimentsinglet(args):
'''
Perform a simple estimation of the loglikelihood of the data, under a model with provided parameters
If inference_method is not none, also fits a EM mixture model, get the precision and the fisher information
'''
print "Doing a piece of work for launcher_do_fitexperimentsinglet"
all_parameters = argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
# Force some parameters
all_parameters.setdefault('experiment_ids', ['gorgo11', 'bays09', 'dualrecall'])
if 'fitexperiment_parameters' not in all_parameters:
fitexperiment_parameters = dict(experiment_ids=all_parameters['experiment_ids'], fit_mixture_model=True)
print "\n T={:d}, experiment_ids {}\n".format(all_parameters['T'], all_parameters['experiment_ids'])
# Create DataIO
# (complete label with current variable state)
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Result arrays
result_fitexperiments = np.nan*np.empty((3, all_parameters['num_repetitions'])) # BIC total, LL, LL90
result_fitexperiments_all = np.nan*np.empty((3, len(all_parameters['experiment_ids']), all_parameters['num_repetitions'])) # BIC, LL, LL90; per experiments,
if all_parameters['inference_method'] != 'none':
result_all_precisions = np.nan*np.empty((all_parameters['num_repetitions']))
result_em_fits = np.nan*np.empty((6, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll, bic
result_fi_theo = np.nan*np.empty((all_parameters['num_repetitions']))
result_fi_theocov = np.nan*np.empty((all_parameters['num_repetitions']))
if all_parameters['sigma_output'] > 0.0:
# We asked for the additional noise convolved, need to take it into account.
result_fitexperiments_noiseconv = np.nan*np.empty((3, all_parameters['num_repetitions'])) # bic (K+1), LL conv, LL90 conv
result_fitexperiments_noiseconv_all = np.nan*np.empty((3, len(all_parameters['experiment_ids']), all_parameters['num_repetitions'])) # bic, LL conv, LL90 conv
search_progress = progress.Progress(all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
print "%d/%d | %.2f%%, %s left - %s" % (repet_i+1, all_parameters['num_repetitions'], search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
### Do the actual FitExperiment computations
fit_exp = FitExperimentSingleT(sampler, fitexperiment_parameters)
## Compute and store the BIC and LL
if all_parameters['code_type'] == 'mixed':
K_nb_params = 3
else:
K_nb_params = 2
bic_loglik_dict = fit_exp.compute_bic_loglik_all_datasets(K=K_nb_params)
for exper_i, exper in enumerate(all_parameters['experiment_ids']):
try:
result_fitexperiments_all[0, exper_i, repet_i] = bic_loglik_dict[exper]['bic']
result_fitexperiments_all[1, exper_i, repet_i] = bic_loglik_dict[exper]['LL']
result_fitexperiments_all[2, exper_i, repet_i] = bic_loglik_dict[exper]['LL90']
except TypeError:
pass
result_fitexperiments[:, repet_i] = np.nansum(result_fitexperiments_all[..., repet_i], axis=1)
if all_parameters['sigma_output'] > 0.0:
# Compute the loglikelihoods with the convolved posterior. Slowish.
## Compute and store the BIC and LL
bic_loglik_noise_convolved_dict = fit_exp.compute_bic_loglik_noise_convolved_all_datasets(precision=150)
for exper_i, exper in enumerate(all_parameters['experiment_ids']):
try:
result_fitexperiments_noiseconv_all[0, exper_i, repet_i] = bic_loglik_noise_convolved_dict[exper]['bic']
result_fitexperiments_noiseconv_all[1, exper_i, repet_i] = bic_loglik_noise_convolved_dict[exper]['LL']
result_fitexperiments_noiseconv_all[2, exper_i, repet_i] = bic_loglik_noise_convolved_dict[exper]['LL90']
except TypeError:
pass
result_fitexperiments_noiseconv[:, repet_i] = np.nansum(result_fitexperiments_noiseconv_all[:, :, repet_i], axis=1)
# If sampling_method is not none, try to get em_fits and others. EXTRA SLOW.
if not all_parameters['inference_method'] == 'none':
parameters = dict([[key, eval(key)] for key in ['all_parameters', 'repet_i', 'result_all_precisions', 'result_em_fits', 'result_fi_theo', 'result_fi_theocov']])
def additional_computations(sampler, parameters):
for key, val in parameters.iteritems():
locals()[key] = val
# Sample
print "sampling..."
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[repet_i] = sampler.get_precision()
# Fit mixture model
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=True)
result_em_fits[:, repet_i] = [curr_params_fit[key] for key in ['kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL', 'bic']]
# Compute fisher info
print "compute fisher info"
result_fi_theo[repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=False)
result_fi_theocov[repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=True)
# Apply that on each dataset!
fct_infos = dict(fct=additional_computations, parameters=parameters)
fit_exp.apply_fct_all_datasets(fct_infos)
print "CURRENT RESULTS:"
if all_parameters['inference_method'] != 'none':
print result_all_precisions[repet_i], result_em_fits[:, repet_i], result_fi_theo[repet_i], result_fi_theocov[repet_i]
print "Fits LL no noise:", bic_loglik_dict
if all_parameters['sigma_output'] > 0.0:
print "Fits LL output noise %.2f: %s" % (all_parameters['sigma_output'], bic_loglik_noise_convolved_dict)
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
### /Work ###
additional_variables = ['fitexperiment_parameters']
dataio.save_variables_default(locals(), additional_variables)
#### Plots ###
print "All finished"
return locals()
def launcher_do_check_scaling_ratio_with_M(args):
'''
Reviewer 3 asked to see if the proportion of conjunctive units varies with M when a given precision is to be achieved.
Check it.
'''
print "Doing a piece of work for launcher_do_check_scaling_ratio_with_M"
all_parameters = utils.argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
if 'plots_during_simulation_callback' in all_parameters:
plots_during_simulation_callback = all_parameters['plots_during_simulation_callback']
del all_parameters['plots_during_simulation_callback']
else:
plots_during_simulation_callback = None
# Create DataIO
# (complete label with current variable state)
dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Fix some parameters
all_parameters['autoset_parameters'] = True
# Parameters to vary
nb_M_space = 10
M_max = 800
M_min = 20
M_space = np.arange(M_min, M_max, np.ceil((M_max - M_min)/float(nb_M_space)), dtype=int)
nb_ratio_space = 10
ratio_space = np.linspace(0.0001, 1.0, nb_ratio_space)
# Result arrays
result_all_precisions = np.nan*np.ones((M_space.size, ratio_space.size, all_parameters['num_repetitions']))
result_em_fits = np.nan*np.ones((M_space.size, ratio_space.size, 5, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll
search_progress = progress.Progress(M_space.size*ratio_space.size*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for M_i, M in enumerate(M_space):
for ratio_i, ratio in enumerate(ratio_space):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for M=%d, ratio=%.3f %d/%d" % (M, ratio, repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['M'] = M
all_parameters['ratio_conj'] = ratio
### WORK WORK WORK work? ###
try:
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[M_i, ratio_i, repet_i] = sampler.get_precision()
# Fit mixture model
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)
result_em_fits[M_i, ratio_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL')]
except Exception:
# oh well...
print "something failed here, so sad"
print result_all_precisions[M_i, ratio_i, repet_i], curr_params_fit
## Run callback function if exists
if plots_during_simulation_callback:
print "Doing plots..."
try:
# Best super safe, if this fails then the simulation must continue!
plots_during_simulation_callback['function'](locals(), plots_during_simulation_callback['parameters'])
print "plots done."
except Exception as e:
print "error during plotting callback function", plots_during_simulation_callback['function'], plots_during_simulation_callback['parameters']
print e
traceback.print_exc()
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
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 launcher_do_receptivesize_effect(args):
'''
Run the model for 1 item, varying the receptive size scale.
Compute:
- Precision of samples
- EM mixture model fits
- Marginal Inverse Fisher Information
'''
print "Doing a piece of work for launcher_do_receptivesize_effect"
all_parameters = utils.argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
if 'plots_during_simulation_callback' in all_parameters:
plots_during_simulation_callback = all_parameters['plots_during_simulation_callback']
del all_parameters['plots_during_simulation_callback']
else:
plots_during_simulation_callback = None
# Create DataIO
# (complete label with current variable state)
dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Fix some parameters
all_parameters['autoset_parameters'] = False
all_parameters['feat_ratio'] = -1. # hack to automatically set the ratio
# Parameters to vary
rcscale_space = np.linspace(0.0001, 40., 30)
# Result arrays
result_all_precisions = np.nan*np.ones((rcscale_space.size, all_parameters['num_repetitions']))
result_marginal_inv_fi = np.nan*np.ones((rcscale_space.size, 4, all_parameters['num_repetitions'])) # inv_FI, inv_FI_std, FI, FI_std
result_em_fits = np.nan*np.ones((rcscale_space.size, 5, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll
search_progress = progress.Progress(rcscale_space.size*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for rc_scale_i, rc_scale in enumerate(rcscale_space):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for rc_scale=%.2f, %d/%d" % (rc_scale, repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['rc_scale'] = rc_scale
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[rc_scale_i, repet_i] = sampler.get_precision()
# Fit mixture model
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)
result_em_fits[rc_scale_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL')]
# Compute marginal inverse fisher info
print "compute marginal inverse fisher info"
marginal_fi_dict = sampler.estimate_marginal_inverse_fisher_info_montecarlo()
result_marginal_inv_fi[rc_scale_i, :, repet_i] = [marginal_fi_dict[key] for key in ('inv_FI', 'inv_FI_std', 'FI', 'FI_std')]
print result_all_precisions[rc_scale_i, repet_i], curr_params_fit, marginal_fi_dict
## Run callback function if exists
if plots_during_simulation_callback:
print "Doing plots..."
try:
# Best super safe, if this fails then the simulation must continue!
plots_during_simulation_callback['function'](locals(), plots_during_simulation_callback['parameters'])
print "plots done."
except Exception as e:
print "error during plotting callback function", plots_during_simulation_callback['function'], plots_during_simulation_callback['parameters']
print e
traceback.print_exc()
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
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 launcher_do_memorycurve_theoretical_pbs_theoonly(args):
'''
Compute Fisher info for T objects.
Get the theoretical FI and the posterior variance estimate as well
'''
all_parameters = vars(args)
dataio = DataIO(output_folder=args.output_directory, label=args.label)
variables_to_save = ['rcscale_space', 'sigma_space', 'M_space', 'T_space', 'FI_rc_theo_mult', 'repet_i', 'num_repetitions', 'use_theoretical_cov']
save_every = 5
run_counter = 0
use_theoretical_cov = True
print "Use theo cov: %d" % use_theoretical_cov
num_repetitions = all_parameters['num_repetitions']
check_theoretical_cov = False
do_curvature = False
do_precision = False
do_var = False
rcscale_space = np.linspace(all_parameters['rc_scale'], all_parameters['rc_scale'], 1.)
sigma_space = np.linspace(all_parameters['sigmax'], all_parameters['sigmax'], 1.)
T_space = np.arange(1, all_parameters['T']+1)
# M_space = np.array([all_parameters['M']])
# M_space = np.arange(5, 30, 3, dtype=int)**2. # Ease the load on PBS...
# M_space = np.floor(np.linspace(25, 900, 49)).astype(int)
M_space = np.arange(5, 22, dtype=int)**2.
FI_rc_theo_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, M_space.size, T_space.size, num_repetitions), dtype=float)
if do_curvature:
variables_to_save.append('FI_rc_curv_mult')
FI_rc_curv_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, M_space.size, T_space.size, 2, num_repetitions), dtype=float)
if do_precision:
variables_to_save.append('FI_rc_precision_mult')
FI_rc_precision_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, M_space.size, T_space.size, num_repetitions), dtype=float)
if do_var:
variables_to_save.append('FI_rc_var_mult')
FI_rc_var_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, M_space.size, T_space.size, 2, num_repetitions), dtype=float)
# Show the progress in a nice way
search_progress = progress.Progress(rcscale_space.size*sigma_space.size*T_space.size*M_space.size*num_repetitions)
print rcscale_space
print sigma_space
print M_space
print T_space
for repet_i in xrange(num_repetitions):
for j, sigma in enumerate(sigma_space):
for i, rc_scale in enumerate(rcscale_space):
for m_i, M in enumerate(M_space):
for t_i, t in enumerate(T_space):
### Estimate the Fisher Information
print "FI T effect, T: %d/%d, rcscale %.3f, sigma %.3f, M %d, (%d/%d). %.2f%%, %s left - %s" % (t, T_space[-1], rc_scale, sigma, M, repet_i+1, num_repetitions, search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
# Current parameter values
all_parameters['rc_scale'] = rc_scale
all_parameters['sigmax'] = sigma
all_parameters['M'] = M
all_parameters['T'] = t
### WORK UNIT
# Fisher info
###
if use_theoretical_cov:
if check_theoretical_cov:
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
computed_cov = random_network.compute_covariance_KL(sigma_2=(all_parameters['sigmax']**2. + all_parameters['sigmay']**2.), T=t, beta=1.0, precision=50)
cov_div = np.mean((sampler.noise_covariance-computed_cov)**2.)
if cov_div > 0.00001:
print cov_div
print all_parameters
pcolor_2d_data(computed_cov)
pcolor_2d_data(sampler.noise_covariance)
plt.show()
raise ValueError('Big divergence between measured and theoretical divergence!')
else:
random_network = init_random_network(all_parameters)
computed_cov = random_network.compute_covariance_KL(sigma_2=(all_parameters['sigmax']**2. + all_parameters['sigmay']**2.), T=t, beta=1.0, precision=50)
# Estimate the fisher information here only
print "theoretical FI"
FI_rc_theo_mult[i, j, m_i, t_i, repet_i] = random_network.compute_fisher_information(stimulus_input=(0.0, 0.0), cov_stim=computed_cov)
print FI_rc_theo_mult[i, j, m_i, t_i, repet_i]
else:
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
computed_cov = sampler.noise_covariance
# computed_cov = stat_meas.model_parameters['covariances'][-1, 0]
# Fisher info
print "theoretical FI"
FI_rc_theo_mult[i, j, m_i, t_i, repet_i] = random_network.compute_fisher_information(stimulus_input=(0.0, 0.0), cov_stim=computed_cov)
print FI_rc_theo_mult[i, j, m_i, t_i, repet_i]
# Estimate the rest, possible here.
if do_curvature:
print "from curvature..."
fi_curv_dict = sampler.estimate_fisher_info_from_posterior_avg_randomsubset(subset_size=20, num_points=1000, full_stats=True)
(FI_rc_curv_mult[i, j, m_i, t_i, 0, repet_i], FI_rc_curv_mult[i, j, m_i, t_i, 1, repet_i]) = (fi_curv_dict['mean'], fi_curv_dict['std'])
print FI_rc_curv_mult[i, j, m_i, t_i, :, repet_i]
if do_var:
print "from variance of posterior..."
fi_var_dict = sampler.estimate_precision_from_posterior_avg_randomsubset(subset_size=20, num_points=1000, full_stats=True)
FI_rc_var_mult[i, j, m_i, t_i, 0, repet_i], FI_rc_var_mult[i, j, m_i, t_i, 1, repet_i] = (fi_var_dict['mean'], fi_var_dict['std'])
print FI_rc_var_mult[i, j, m_i, t_i, :, repet_i]
if do_precision:
print "from precision of recall..."
if all_parameters['inference_method'] == 'sample':
# Sample thetas
sampler.sample_theta(num_samples=all_parameters['num_samples'], burn_samples=100, selection_method=all_parameters['selection_method'], selection_num_samples=all_parameters['selection_num_samples'], integrate_tc_out=False, debug=False)
elif all_parameters['inference_method'] == 'max_lik':
# Just use the ML value for the theta
sampler.set_theta_max_likelihood(num_points=200, post_optimise=True)
FI_rc_precision_mult[i, j, m_i, t_i, repet_i] = sampler.get_precision()
print FI_rc_precision_mult[i, j, m_i, t_i, repet_i]
### DONE WORK UNIT
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables(variables_to_save, locals())
run_counter += 1
return locals()
def launcher_do_memory_curve_simult(args):
'''
Get the memory curves, for 1...T objects, simultaneous presentation
(will force alpha=1, and only recall one object for each T, more independent)
'''
# Should collect all responses?
collect_responses = False
# Should compute Fisher info?
fisher_info = True
# Build the random network
alpha = 1.
time_weights_parameters = dict(weighting_alpha=alpha, weighting_beta=1.0, specific_weighting=0.1, weight_prior='uniform')
# Initialise the output file
dataio = DataIO(output_folder=args.output_directory, label=args.label)
output_string = dataio.filename
all_parameters = vars(args)
# List of variables to save
if collect_responses:
variables_to_output = ['all_precisions', 'args', 'num_repetitions', 'output_string', 'power_law_params', 'repet_i', 'all_responses', 'all_targets', 'all_nontargets', 'results_fi', 'results_fi_largen']
else:
variables_to_output = ['all_precisions', 'args', 'num_repetitions', 'output_string', 'power_law_params', 'repet_i', 'results_fi', 'results_fi_largen']
print "Doing do_multiple_memory_curve"
print "max_T: %s" % args.T
print "File: %s" % output_string
all_precisions = np.nan*np.empty((args.T, args.num_repetitions))
results_fi = np.nan*np.empty((args.T, args.num_repetitions))
results_fi_largen = np.nan*np.empty((args.T, args.num_repetitions))
power_law_params = np.nan*np.empty(2)
if collect_responses:
all_responses = np.nan*np.empty((args.T, args.num_repetitions, args.N))
all_targets = np.nan*np.empty((args.T, args.num_repetitions, args.N))
all_nontargets = np.nan*np.empty((args.T, args.num_repetitions, args.N, args.T-1))
# Construct different datasets, with t objects
for repet_i in xrange(args.num_repetitions):
for t in xrange(args.T):
#### Get multiple examples of precisions, for different number of neurons. #####
all_parameters['T'] = t+1
# Init everything
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
print " doing T=%d %d/%d" % (t+1, repet_i+1, args.num_repetitions)
if args.inference_method == 'sample':
# Sample thetas
sampler.sample_theta(num_samples=args.num_samples, burn_samples=100, selection_method=args.selection_method, selection_num_samples=args.selection_num_samples, integrate_tc_out=False, debug=False)
elif args.inference_method == 'max_lik':
# Just use the ML value for the theta
sampler.set_theta_max_likelihood(num_points=200, post_optimise=True)
else:
raise ValueError('Wrong value for inference_method')
# Save the precision
all_precisions[t, repet_i] = sampler.get_precision(remove_chance_level=False, correction_theo_fit=1.0)
# all_precisions[t, repet_i] = 1./sampler.compute_angle_error()['std']
print "-> %.5f" % all_precisions[t, repet_i]
# Collect responses if needed
if collect_responses:
(all_responses[t, repet_i], all_targets[t, repet_i], all_nontargets[t, repet_i, :, :t]) = sampler.collect_responses()
# Compute Fisher information as well
if fisher_info:
results_fi[t, repet_i] = random_network.compute_fisher_information(cov_stim=sampler.noise_covariance, kappa_different=True)
results_fi_largen[t, repet_i] = np.mean(random_network.compute_fisher_information_theoretical(sigma=all_parameters['sigmax'] + all_parameters['sigmay'], kappa1=random_network.neurons_sigma[:, 0], kappa2=random_network.neurons_sigma[:, 1]))
# Save to disk, unique filename
dataio.save_variables(variables_to_output, locals())
if args.T > 1:
xx = np.tile(np.arange(1, args.T+1, dtype='float'), (repet_i+1, 1)).T
power_law_params = fit_powerlaw(xx, all_precisions[:, :(repet_i+1)], should_plot=True)
print '====> Power law fits: exponent: %.4f, bias: %.4f' % (power_law_params[0], power_law_params[1])
# Save to disk, unique filename
dataio.save_variables(variables_to_output, locals())
print all_precisions
# Save to disk, unique filename
dataio.save_variables(variables_to_output, locals())
f = plt.figure()
ax = f.add_subplot(111)
ax = plot_mean_std_area(np.arange(1, args.T+1), np.mean(all_precisions, 1), np.std(all_precisions, 1), linewidth=2, ax_handle=ax, fmt='o-', markersize=10)
ax = plot_mean_std_area(np.arange(1, args.T+1), np.mean(results_fi, 1), np.std(results_fi, 1), linewidth=2, ax_handle=ax, fmt='o-', markersize=10)
# ax = plot_mean_std_area(np.arange(args.T), np.mean(results_fi_largen, 1), np.std(results_fi_largen, 1), ax_handle=ax)
ax.set_xlabel('Number of objects')
ax.set_ylabel('Precision [rad]')
plt.legend(['Precision of samples', 'Fisher Information'])
plt.xticks([1, 2, 3, 4, 5])
plt.xlim((0.9, 5.1))
dataio.save_current_figure('memory_curve_precision_fisherinfo-{label}-{unique_id}.pdf')
print "Done: %s" % output_string
return locals()
def launcher_do_memorycurve_theoretical(args):
'''
Compute the FI for different number of items (T)
Should have a 1/x dependence on T, power law of exponent -1.
'''
all_parameters = vars(args)
data_to_plot = {}
dataio = DataIO(output_folder=args.output_directory, label=args.label)
variables_to_save = ['rcscale_space', 'sigma_space', 'T_space', 'FI_rc_curv_mult', 'FI_rc_var_mult', 'FI_rc_precision_mult', 'FI_rc_theo_mult', 'repet_i', 'num_repetitions']
save_every = 5
run_counter = 0
use_theoretical_cov = all_parameters['use_theoretical_cov']
print "Use theo cov: %d" % use_theoretical_cov
num_repetitions = all_parameters['num_repetitions']
check_theoretical_cov = False
do_curvature = False
do_precision = True
do_var = True
# rcscale_space = np.linspace(0.5, 15.0, 21.)
# rcscale_space = np.linspace(0.01, 15., 21.)
# rcscale_space = np.linspace(4.0, 4.0, 1.)
rcscale_space = np.linspace(all_parameters['rc_scale'], all_parameters['rc_scale'], 1.)
# sigma_space = np.linspace(0.15, 0.3, 10.)
# sigma_space = np.linspace(0.1, 0.1, 1.0)
sigma_space = np.linspace(all_parameters['sigmax'], all_parameters['sigmax'], 1.)
T_space = np.arange(1, all_parameters['T']+1)
FI_rc_curv_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, T_space.size, 2, num_repetitions), dtype=float)
FI_rc_var_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, T_space.size, 2, num_repetitions), dtype=float)
FI_rc_precision_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, T_space.size, num_repetitions), dtype=float)
FI_rc_theo_mult = np.nan*np.empty((rcscale_space.size, sigma_space.size, T_space.size, num_repetitions), dtype=float)
# Show the progress in a nice way
search_progress = progress.Progress(rcscale_space.size*sigma_space.size*T_space.size*num_repetitions)
print rcscale_space
print sigma_space
for repet_i in xrange(num_repetitions):
for j, sigma in enumerate(sigma_space):
for i, rc_scale in enumerate(rcscale_space):
for t_i, t in enumerate(T_space):
### Estimate the Fisher Information
print "FI T effect, T: %d/%d, rcscale %.3f, sigma %.3f (%d/%d). %.2f%%, %s left - %s" % (t, T_space[-1], rc_scale, sigma, repet_i+1, num_repetitions, search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
# Current parameter values
all_parameters['rc_scale'] = rc_scale
all_parameters['sigmax'] = sigma
all_parameters['T'] = t
### WORK UNIT
# Fisher info
###
if use_theoretical_cov:
if check_theoretical_cov:
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
computed_cov = random_network.compute_covariance_KL(sigma_2=(all_parameters['sigmax']**2. + all_parameters['sigmay']**2.), T=t, beta=1.0, precision=50)
cov_div = np.mean((sampler.noise_covariance-computed_cov)**2.)
if cov_div > 0.00001:
print cov_div
print all_parameters
pcolor_2d_data(computed_cov)
pcolor_2d_data(sampler.noise_covariance)
plt.show()
raise ValueError('Big divergence between measured and theoretical divergence!')
else:
random_network = launchers.init_random_network(all_parameters)
computed_cov = random_network.compute_covariance_KL(sigma_2=(all_parameters['sigmax']**2. + all_parameters['sigmay']**2.), T=t, beta=1.0, precision=50)
# Estimate the fisher information here only
print "theoretical FI"
FI_rc_theo_mult[i, j, t_i, repet_i] = random_network.compute_fisher_information(stimulus_input=(0.0, 0.0), cov_stim=computed_cov, kappa_different=True)
print FI_rc_theo_mult[i, j, t_i, repet_i]
else:
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
computed_cov = sampler.noise_covariance
# computed_cov = stat_meas.model_parameters['covariances'][-1, 0]
# Fisher info
print "theoretical FI"
FI_rc_theo_mult[i, j, t_i, repet_i] = random_network.compute_fisher_information(stimulus_input=(0.0, 0.0), cov_stim=computed_cov, kappa_different=True)
# print FI_rc_theo_mult[i, j, t_i, repet_i]
print FI_rc_theo_mult
# Estimate the rest, possible here.
if do_curvature:
print "from curvature..."
fi_curv_dict = sampler.estimate_fisher_info_from_posterior_avg_randomsubset(subset_size=20, num_points=1000, full_stats=True)
(FI_rc_curv_mult[i, j, t_i, 0, repet_i], FI_rc_curv_mult[i, j, t_i, 1, repet_i]) = (fi_curv_dict['mean'], fi_curv_dict['std'])
print FI_rc_curv_mult[i, j, t_i, :, repet_i]
if do_var:
print "from variance of posterior..."
fi_var_dict = sampler.estimate_precision_from_posterior_avg_randomsubset(subset_size=20, num_points=1000, full_stats=True)
FI_rc_var_mult[i, j, t_i, 0, repet_i], FI_rc_var_mult[i, j, t_i, 1, repet_i] = (fi_var_dict['mean'], fi_var_dict['std'])
# print FI_rc_var_mult[i, j, t_i, :, repet_i]
print FI_rc_var_mult[:, :, :, 0, :]
if do_precision:
print "from precision of recall..."
if all_parameters['inference_method'] == 'sample':
# Sample thetas
sampler.sample_theta(num_samples=all_parameters['num_samples'], burn_samples=100, selection_method=all_parameters['selection_method'], selection_num_samples=all_parameters['selection_num_samples'], integrate_tc_out=False, debug=False)
elif all_parameters['inference_method'] == 'max_lik':
# Just use the ML value for the theta
sampler.set_theta_max_likelihood(num_points=200, post_optimise=True)
FI_rc_precision_mult[i, j, t_i, repet_i] = sampler.get_precision()
# print FI_rc_precision_mult[i, j, t_i, repet_i]
print FI_rc_precision_mult
### DONE WORK UNIT
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables(variables_to_save, locals())
# plots
for curr_data in variables_to_save:
data_to_plot[curr_data] = locals()[curr_data]
# plots_fisher_info_param_search(data_to_plot, dataio)
run_counter += 1
return locals()
def launcher_check_fisher_fit_1obj_2016(args):
print "Doing a piece of work for launcher_check_fisher_fit_1obj_2016"
all_parameters = utils.argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
if 'plots_during_simulation_callback' in all_parameters:
plots_during_simulation_callback = all_parameters[
'plots_during_simulation_callback']
del all_parameters['plots_during_simulation_callback']
else:
plots_during_simulation_callback = None
# Create DataIO
# (complete label with current variable state)
dataio = DataIO.DataIO(
output_folder=all_parameters['output_directory'],
label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Result arrays
result_all_precisions = np.nan * np.empty(
(all_parameters['num_repetitions']), dtype=float)
result_FI_rc_curv = np.nan * np.empty(
(all_parameters['N'], all_parameters['num_repetitions']), dtype=float)
result_FI_rc_theo = np.nan * np.empty(
(all_parameters['N'], all_parameters['num_repetitions']), dtype=float)
result_FI_rc_theocov = np.nan * np.empty(
(all_parameters['N'], all_parameters['num_repetitions']), dtype=float)
result_FI_rc_theo_circulant = np.nan * np.empty(
(all_parameters['N'], all_parameters['num_repetitions']), dtype=float)
result_FI_rc_theo_largeN = np.nan * np.empty(
(all_parameters['num_repetitions']), dtype=float)
result_marginal_inv_FI = np.nan * np.ones(
(2, all_parameters['num_repetitions']))
result_marginal_FI = np.nan * np.ones((2, all_parameters['num_repetitions']))
result_em_fits = np.nan * np.empty((6, all_parameters['num_repetitions']))
search_progress = progress.Progress(all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
print "%.2f%%, %s left - %s" % (search_progress.percentage(),
search_progress.time_remaining_str(),
search_progress.eta_str())
print "Fisher Info check, rep %d/%d" % (repet_i + 1,
all_parameters['num_repetitions'])
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[repet_i] = sampler.get_precision()
# Theoretical Fisher info
if all_parameters['code_type'] != 'hierarchical':
print "theoretical FI"
result_FI_rc_theo[:, repet_i] = (
sampler.estimate_fisher_info_theocov(use_theoretical_cov=False))
result_FI_rc_theocov[:, repet_i] = (
sampler.estimate_fisher_info_theocov(use_theoretical_cov=True))
result_FI_rc_theo_largeN[repet_i] = (
sampler.estimate_fisher_info_theocov_largen(use_theoretical_cov=True)
)
result_FI_rc_theo_circulant[:, repet_i] = (
sampler.estimate_fisher_info_circulant())
# Fisher Info from curvature
print "Compute fisher from curvature"
fi_curv_dict = sampler.estimate_fisher_info_from_posterior_avg(
num_points=500, full_stats=True)
result_FI_rc_curv[:, repet_i] = fi_curv_dict['all']
# Fit mixture model
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)
curr_params_fit['mixt_nontargets_sum'] = np.sum(
curr_params_fit['mixt_nontargets'])
result_em_fits[..., repet_i] = [
curr_params_fit[key]
for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum',
'mixt_random', 'train_LL', 'bic')
]
# Compute marginal inverse fisher info
print "compute marginal inverse fisher info"
marginal_fi_dict = sampler.estimate_marginal_inverse_fisher_info_montecarlo(
)
result_marginal_inv_FI[:, repet_i] = [
marginal_fi_dict[key] for key in ('inv_FI', 'inv_FI_std')
]
result_marginal_FI[:, repet_i] = [
marginal_fi_dict[key] for key in ('FI', 'FI_std')
]
## Run callback function if exists
if plots_during_simulation_callback:
print "Doing plots..."
try:
# Best super safe, if this fails then the simulation must continue!
plots_during_simulation_callback['function'](
locals(), plots_during_simulation_callback['parameters'])
print "plots done."
except Exception:
print "error during plotting callback function", plots_during_simulation_callback[
'function'], plots_during_simulation_callback['parameters']
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_hierarchical_precision_M_Mlower_pbs(args):
'''
Compare the evolution of the precision curve as the number of neurons in a hierarchical network increases.
'''
print "Doing a piece of work for launcher_do_hierarchical_precision_M_Mlower_pbs"
save_all_output = True
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
code_type = 'hierarchical'
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'])
variables_to_save = ['repet_i', 'num_repetitions']
save_every = 5
run_counter = 0
num_repetitions = all_parameters['num_repetitions']
M_space = np.array([all_parameters['M']])
M_lower_space = np.array([all_parameters['M_layer_one']])
T_space = np.arange(1, all_parameters['T']+1)
results_precision_M_T = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, num_repetitions), dtype=float)
results_emfits_M_T = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, 5, num_repetitions), dtype=float)
if save_all_output:
result_responses = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, all_parameters['N'], num_repetitions))
result_targets = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, all_parameters['N'], num_repetitions))
result_nontargets = np.nan*np.empty((M_space.size, M_lower_space.size, T_space.size, all_parameters['N'], all_parameters['T']-1, num_repetitions))
# Show the progress
search_progress = progress.Progress(T_space.size*M_space.size*M_lower_space.size*num_repetitions)
print M_space
print M_lower_space
print T_space
for repet_i in xrange(num_repetitions):
for m_i, M in enumerate(M_space):
for m_l_i, M_layer_one in enumerate(M_lower_space):
for t_i, t in enumerate(T_space):
# Will estimate the precision
print "Precision as function of N, hierarchical network, T: %d/%d, M %d, M_layer_one %d, (%d/%d). %.2f%%, %s left - %s" % (t, T_space[-1], M, M_layer_one, repet_i+1, num_repetitions, search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
# Current parameter values
all_parameters['M'] = M
all_parameters['T'] = t
all_parameters['code_type'] = code_type
all_parameters['M_layer_one'] = M_layer_one
### WORK UNIT
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
# Sample / max like
sampler.run_inference(all_parameters)
print 'get precision...'
results_precision_M_T[m_i, m_l_i, t_i, repet_i] = sampler.get_precision()
print results_precision_M_T[m_i, m_l_i, t_i, repet_i]
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=True)
curr_params_fit['mixt_nontargets_sum'] = np.sum(curr_params_fit['mixt_nontargets'])
results_emfits_M_T[m_i, m_l_i, t_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL')]
if save_all_output:
(result_responses[m_i, m_l_i, t_i, :, repet_i], result_targets[m_i, m_l_i, t_i, :, repet_i], result_nontargets[m_i, m_l_i, t_i, :, :t_i, repet_i]) = sampler.collect_responses()
### DONE WORK UNIT
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals(), variables_to_save)
run_counter += 1
print "All finished"
return locals()
def plots_fitmixtmodel_rcscale_effect(data_pbs, generator_module=None):
'''
Reload runs from PBS
'''
#### SETUP
#
savefigs = True
savedata = True
plots_all_T = True
plots_per_T = True
# do_relaunch_bestparams_pbs = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", data_pbs.dataset_infos['parameters']
# parameters: M, ratio_conj, sigmax
# Extract data
T_space = data_pbs.loaded_data['datasets_list'][0]['T_space']
result_em_fits_flat = np.array(data_pbs.dict_arrays['result_em_fits']['results_flat'])
result_precisions_flat = np.array(data_pbs.dict_arrays['result_all_precisions']['results_flat'])
result_dist_bays09_flat = np.array(data_pbs.dict_arrays['result_dist_bays09']['results_flat'])
result_dist_gorgo11_flat = np.array(data_pbs.dict_arrays['result_dist_gorgo11']['results_flat'])
result_dist_bays09_emmixt_KL = np.array(data_pbs.dict_arrays['result_dist_bays09_emmixt_KL']['results_flat'])
result_dist_gorgo11_emmixt_KL = np.array(data_pbs.dict_arrays['result_dist_gorgo11_emmixt_KL']['results_flat'])
result_parameters_flat = np.array(data_pbs.dict_arrays['result_em_fits']['parameters_flat'])
rc_scale_space = data_pbs.loaded_data['parameters_uniques']['rc_scale']
num_repetitions = generator_module.num_repetitions
parameter_names_sorted = data_pbs.dataset_infos['parameters']
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
# Load bays09
data_bays09 = load_experimental_data.load_data_bays09(fit_mixture_model=True)
bays09_nitems = data_bays09['data_to_fit']['n_items']
bays09_em_target = np.nan*np.empty((bays09_nitems.max(), 4)) #kappa, prob_target, prob_nontarget, prob_random
bays09_em_target[bays09_nitems - 1] = data_bays09['em_fits_nitems_arrays']['mean'].T
bays09_emmixt_target = bays09_em_target[:, 1:]
## Compute some stuff
result_parameters_flat = result_parameters_flat.flatten()
result_em_fits_all_avg = utils.nanmean(result_em_fits_flat, axis=-1)
result_em_kappa_allT = result_em_fits_all_avg[..., 0]
result_em_emmixt_allT = result_em_fits_all_avg[..., 1:4]
result_precisions_all_avg = utils.nanmean(result_precisions_flat, axis=-1)
# Square distance to kappa
result_dist_bays09_allT_avg = utils.nanmean(result_dist_bays09_flat, axis=-1)
result_dist_bays09_emmixt_KL_allT_avg = utils.nanmean(result_dist_bays09_emmixt_KL, axis=-1)
result_dist_bays09_kappa_allT = result_dist_bays09_allT_avg[..., 0]
# result_dist_bays09_allT_avg = utils.nanmean((result_em_fits_flat[:, :, :4] - bays09_em_target[np.newaxis, :, :, np.newaxis])**2, axis=-1)
# result_dist_bays09_kappa_sum = np.nansum(result_dist_bays09_allT_avg[:, :, 0], axis=-1)
# result_dist_bays09_kappa_T1_sum = result_dist_bays09_allT_avg[:, 0, 0]
# result_dist_bays09_kappa_T25_sum = np.nansum(result_dist_bays09_allT_avg[:, 1:, 0], axis=-1)
# # Square and KL distance for EM Mixtures
# result_dist_bays09_emmixt_sum = np.nansum(np.nansum(result_dist_bays09_allT_avg[:, :, 1:], axis=-1), axis=-1)
# result_dist_bays09_emmixt_T1_sum = np.nansum(result_dist_bays09_allT_avg[:, 0, 1:], axis=-1)
# result_dist_bays09_emmixt_T25_sum = np.nansum(np.nansum(result_dist_bays09_allT_avg[:, 1:, 1:], axis=-1), axis=-1)
# result_dist_bays09_emmixt_KL = utils.nanmean(utils.KL_div(result_em_fits_flat[:, :, 1:4], bays09_emmixt_target[np.newaxis, :, :, np.newaxis], axis=-2), axis=-1) # KL over dimension of mixtures, then mean over repetitions
# result_dist_bays09_emmixt_KL_sum = np.nansum(result_dist_bays09_emmixt_KL, axis=-1) # sum over T
# result_dist_bays09_emmixt_KL_T1_sum = result_dist_bays09_emmixt_KL[:, 0]
# result_dist_bays09_emmixt_KL_T25_sum = np.nansum(result_dist_bays09_emmixt_KL[:, 1:], axis=-1)
# result_dist_bays09_both_normalised = result_dist_bays09_emmixt_sum/np.max(result_dist_bays09_emmixt_sum) + result_dist_bays09_kappa_sum/np.max(result_dist_bays09_kappa_sum)
# # Mask kappa for performance too bad
# result_dist_bays09_kappa_sum_masked = np.ma.masked_greater(result_dist_bays09_kappa_sum, 2*np.median(result_dist_bays09_kappa_sum))
# result_dist_bays09_emmixt_KL_sum_masked = np.ma.masked_greater(result_dist_bays09_emmixt_KL_sum, 2*np.median(result_dist_bays09_emmixt_KL_sum))
# result_dist_bays09_both_normalised_mult_masked = 1-(1. - result_dist_bays09_emmixt_KL_sum/np.max(result_dist_bays09_emmixt_KL_sum))*(1. - result_dist_bays09_kappa_sum_masked/np.max(result_dist_bays09_kappa_sum_masked))
# Compute optimal rc_scale
all_args = data_pbs.loaded_data['args_list']
specific_arg = all_args[0]
specific_arg['autoset_parameters'] = True
(_, _, _, sampler) = launchers.init_everything(specific_arg)
optimal_rc_scale = sampler.random_network.rc_scale[0]
if plots_all_T:
# Show Kappa evolution wrt rc_scale
f, ax = plt.subplots()
# utils.plot_mean_std_from_samples(result_parameters_flat, np.nansum(result_em_kappa_allT, axis=-1), bins=60, bins_y=150, xlabel='rc_scale', ylabel='EM kappa', title='Kappa, summed T', ax_handle=ax, show_scatter=False)
utils.plot_mean_std_from_samples_rolling(result_parameters_flat, np.nansum(result_em_kappa_allT, axis=-1), window=35, xlabel='rc_scale', ylabel='EM kappa', title='Kappa, summed T', ax_handle=ax, show_scatter=False)
ax.axvline(x=optimal_rc_scale, color='g', linewidth=2)
ax.axvline(x=2*optimal_rc_scale, color='r', linewidth=2)
f.canvas.draw()
if savefigs:
dataio.save_current_figure('rcscaleeffect_kappa_summedT_{label}_{unique_id}.pdf')
# Show Mixt proportions
f, ax = plt.subplots()
for i in xrange(3):
# utils.plot_mean_std_from_samples(result_parameters_flat, np.nansum(result_em_emmixt_allT[..., i], axis=-1), bins=60, bins_y=100, xlabel='rc_scale', ylabel='EM mixt proportions', title='EM mixtures, summed T', ax_handle=ax, show_scatter=False)
utils.plot_mean_std_from_samples_rolling(result_parameters_flat, np.nansum(result_em_emmixt_allT[..., i], axis=-1), window=35, xlabel='rc_scale', ylabel='EM mixt proportions', title='EM mixtures, summed T', ax_handle=ax, show_scatter=False)
ax.axvline(x=optimal_rc_scale, color='g', linewidth=2)
ax.axvline(x=2*optimal_rc_scale, color='r', linewidth=2)
f.canvas.draw()
if savefigs:
dataio.save_current_figure('rcscaleeffect_mixtprop_summedT_{label}_{unique_id}.pdf')
# Show Precision
f, ax = plt.subplots()
# utils.plot_mean_std_from_samples(result_parameters_flat, np.nansum(result_precisions_all_avg, axis=-1), bins=60, bins_y=150, xlabel='rc_scale', ylabel='Precision', title='Precision, summed T', ax_handle=ax, show_scatter=False)
utils.plot_mean_std_from_samples_rolling(result_parameters_flat, np.nansum(result_precisions_all_avg, axis=-1), window=35, xlabel='rc_scale', ylabel='Precision', title='Precision, summed T', ax_handle=ax, show_scatter=False)
ax.axvline(x=optimal_rc_scale, color='g', linewidth=2)
ax.axvline(x=2*optimal_rc_scale, color='r', linewidth=2)
f.canvas.draw()
if savefigs:
dataio.save_current_figure('rcscaleeffect_precision_summedT_{label}_{unique_id}.pdf')
plt.close('all')
if plots_per_T:
for T_i, T in enumerate(T_space):
# Show Kappa evolution wrt rc_scale
f, ax = plt.subplots()
# utils.plot_mean_std_from_samples(result_parameters_flat, result_em_kappa_allT[:, T_i], bins=40, bins_y=100, xlabel='rc_scale', ylabel='EM kappa', title='Kappa, T %d' % T, ax_handle=ax, show_scatter=False)
utils.plot_mean_std_from_samples_rolling(result_parameters_flat, result_em_kappa_allT[:, T_i], window=35, xlabel='rc_scale', ylabel='EM kappa', title='Kappa, T %d' % T, ax_handle=ax, show_scatter=False)
ax.axvline(x=optimal_rc_scale, color='g', linewidth=2)
ax.axvline(x=2*optimal_rc_scale, color='r', linewidth=2)
f.canvas.draw()
if savefigs:
dataio.save_current_figure('rcscaleeffect_kappa_T%d_{label}_{unique_id}.pdf' % T)
# Show Mixt proportions
f, ax = plt.subplots()
for i in xrange(3):
# utils.plot_mean_std_from_samples(result_parameters_flat, result_em_emmixt_allT[:, T_i, i], bins=40, bins_y=100, xlabel='rc_scale', ylabel='EM mixt proportions', title='EM mixtures, T %d' % T, ax_handle=ax, show_scatter=False)
utils.plot_mean_std_from_samples_rolling(result_parameters_flat, result_em_emmixt_allT[:, T_i, i], window=35, xlabel='rc_scale', ylabel='EM mixt proportions', title='EM mixtures, T %d' % T, ax_handle=ax, show_scatter=False)
ax.axvline(x=optimal_rc_scale, color='g', linewidth=2)
ax.axvline(x=2*optimal_rc_scale, color='r', linewidth=2)
f.canvas.draw()
if savefigs:
dataio.save_current_figure('rcscaleeffect_mixtprop_T%d_{label}_{unique_id}.pdf' % T)
# Show Precision
f, ax = plt.subplots()
# utils.plot_mean_std_from_samples(result_parameters_flat, result_precisions_all_avg[:, T_i], bins=40, bins_y=100, xlabel='rc_scale', ylabel='Precision', title='Precision, T %d' % T, ax_handle=ax, show_scatter=False)
utils.plot_mean_std_from_samples_rolling(result_parameters_flat, result_precisions_all_avg[:, T_i], window=35, xlabel='rc_scale', ylabel='Precision', title='Precision, T %d' % T, ax_handle=ax, show_scatter=False)
ax.axvline(x=optimal_rc_scale, color='g', linewidth=2)
ax.axvline(x=2*optimal_rc_scale, color='r', linewidth=2)
f.canvas.draw()
if savefigs:
dataio.save_current_figure('rcscaleeffect_precision_T%d_{label}_{unique_id}.pdf' % T)
plt.close('all')
# # Interpolate
# if plots_interpolate:
# sigmax_target = 0.9
# M_interp_space = np.arange(6, 625, 5)
# ratio_interp_space = np.linspace(0.01, 1.0, 50)
# # sigmax_interp_space = np.linspace(0.01, 1.0, 50)
# sigmax_interp_space = np.array([sigmax_target])
# params_crossspace = np.array(utils.cross(M_interp_space, ratio_interp_space, sigmax_interp_space))
# interpolated_data = rbf_interpolator(params_crossspace[:, 0], params_crossspace[:, 1], params_crossspace[:, 2]).reshape((M_interp_space.size, ratio_interp_space.size))
# utils.pcolor_2d_data(interpolated_data, M_interp_space, ratio_interp_space, 'M', 'ratio', 'interpolated, fixing sigmax= %.2f' % sigmax_target)
# points_closeby = ((result_parameters_flat[:, 2] - sigmax_target)**2)< 0.01
# plt.figure()
# # plt.imshow(interpolated_data, extent=(M_interp_space.min(), M_interp_space.max(), ratio_interp_space.min(), ratio_interp_space.max()))
# plt.imshow(interpolated_data)
# plt.scatter(result_parameters_flat[points_closeby, 0], result_parameters_flat[points_closeby, 1], s=100, c=result_fitexperiments_bic_avg[points_closeby], marker='o')
# 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))
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['parameter_names_sorted']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='rcscale_characterisation')
plt.show()
return locals()
def launcher_do_hierarchical_precision_M_sparsity_sigmaweight_feature(args):
'''
Compare the evolution of the precision curve as the sparsity, sigma and M change, for a hierarchical code with feature base
'''
all_parameters = vars(args)
code_type = 'hierarchical'
dataio = DataIO(output_folder=args.output_directory, label=args.label)
# variables_to_save = ['M_space', 'T_space', 'repet_i', 'num_repetitions', 'results_precision_N', 'result_responses', 'result_targets', 'result_nontargets']
variables_to_save = ['M_space', 'T_space', 'sparsity_space', 'sigma_weights_space', 'repet_i', 'num_repetitions', 'results_precision_N']
save_every = 5
run_counter = 0
num_repetitions = all_parameters['num_repetitions']
# M_space = np.array([all_parameters['M']])
# M_space = np.array([4*4, 5*5, 7*7, 8*8, 9*9, 10*10, 15*15, 20*20])
M_space = np.linspace(5, 500, 10)
sparsity_space = np.linspace(0.01, 1.0, 10.)
sigma_weights_space = np.linspace(0.1, 2.0, 10)
T_space = np.arange(1, all_parameters['T']+1)
results_precision_M_T = np.nan*np.empty((M_space.size, sparsity_space.size, sigma_weights_space.size, T_space.size, num_repetitions), dtype=float)
# result_responses = np.nan*np.empty((M_space.size, sparsity_space.size, sigma_weights_space.size, T_space.size, num_repetitions, all_parameters['N']))
# result_targets = np.nan*np.empty((M_space.size, sparsity_space.size, sigma_weights_space.size, T_space.size, num_repetitions, all_parameters['N']))
# result_nontargets = np.nan*np.empty((M_space.size, sparsity_space.size, sigma_weights_space.size, T_space.size, num_repetitions, all_parameters['N'], all_parameters['T']-1))
all_parameters['type_layer_one'] = 'feature'
# Show the progress
search_progress = progress.Progress(T_space.size*M_space.size*sigma_weights_space.size*sparsity_space.size*num_repetitions)
print M_space
print sparsity_space
print sigma_weights_space
print T_space
for repet_i in xrange(num_repetitions):
for m_i, M in enumerate(M_space):
for s_i, sparsity in enumerate(sparsity_space):
for sw_i, sigma_weights in enumerate(sigma_weights_space):
for t_i, t in enumerate(T_space):
# Will estimate the precision
print "Precision as function of N, hierarchical network, T: %d/%d, M %d, sparsity %.3f, weights: %.2f, (%d/%d). %.2f%%, %s left - %s" % (t, T_space[-1], M, sparsity, sigma_weights, repet_i+1, num_repetitions, search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
# Current parameter values
all_parameters['M'] = M
all_parameters['T'] = t
all_parameters['code_type'] = code_type
all_parameters['sparsity'] = sparsity
all_parameters['sigma_weights'] = sigma_weights
### WORK UNIT
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
if all_parameters['inference_method'] == 'sample':
# Sample thetas
sampler.sample_theta(num_samples=all_parameters['num_samples'], burn_samples=100, selection_method=all_parameters['selection_method'], selection_num_samples=all_parameters['selection_num_samples'], integrate_tc_out=False, debug=False)
elif all_parameters['inference_method'] == 'max_lik':
# Just use the ML value for the theta
sampler.set_theta_max_likelihood(num_points=150, post_optimise=True)
results_precision_M_T[m_i, s_i, sw_i, t_i, repet_i] = sampler.get_precision()
print results_precision_M_T[m_i, s_i, sw_i, t_i, repet_i]
# (result_responses[m_i, s_i, t_i, repet_i], result_targets[m_i, s_i, t_i, repet_i], result_nontargets[m_i, s_i, t_i, repet_i, :, :t_i]) = sampler.collect_responses()
### DONE WORK UNIT
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables(variables_to_save, locals())
run_counter += 1
return locals()
def test_loglike_modelselection():
'''
Check if the LL computation is correct for model selection
Use specific data, generated from a given model. This model should then have max LL.
'''
# Set some parameters and let the others default
experiment_parameters = dict(action_to_do='launcher_do_simple_run',
inference_method='sample',
experiment_id='bays09',
M=100,
N=500,
filter_datapoints_size=500,
filter_datapoints_selection='random',
num_samples=500,
selection_method='last',
sigmax=0.1,
sigma_output=0.5,
renormalize_sigmax=None,
sigmay=0.0001,
code_type='mixed',
slice_width=0.07,
burn_samples=200,
ratio_conj=0.7,
stimuli_generation_recall='random',
autoset_parameters=None,
label='test_fit_experimentallt'
)
experiment_launcher = experimentlauncher.ExperimentLauncher(run=True, arguments_dict=experiment_parameters)
experiment_parameters_full = experiment_launcher.args_dict
sampler = experiment_launcher.all_vars['sampler']
# Keep its dataset and responses
stimuli_correct_to_force = sampler.data_gen.stimuli_correct.copy()
response_to_force = sampler.theta[:, 0].copy()
LL_target = sampler.compute_loglikelihood()
experiment_parameters_full['stimuli_to_use'] = stimuli_correct_to_force
sigmaoutput_space = np.linspace(0.0, 1.0, 10)
LL_all_new = np.empty(sigmaoutput_space.size)
LL_all_conv_new = np.empty(sigmaoutput_space.size)
for sigmaout_i, sigma_output in enumerate(sigmaoutput_space):
experiment_parameters_full['sigma_output'] = sigma_output
_, _, _, samplerbis = launchers.init_everything(experiment_parameters_full)
# Set responses
samplerbis.set_theta(response_to_force)
# Compute LL
LL_all_new[sigmaout_i] = samplerbis.compute_loglikelihood()
LL_all_conv_new[sigmaout_i] = samplerbis.compute_loglikelihood_convolved_output_noise()
# Print result
print LL_all_new[sigmaout_i], LL_all_conv_new[sigmaout_i]
print LL_target
print sigma_output, LL_all_new, LL_all_conv_new
print sigmaoutput_space[np.argmax(LL_all_new)]
print sigmaoutput_space[np.argmax(LL_all_conv_new)]
return locals()
def launcher_do_hierarchical_special_stimuli_varyMMlower(args):
'''
Fit Hierarchical model, varying the ratio of M to Mlower
See how the precision of recall and mixture model parameters evolve
'''
print "Doing a piece of work for launcher_do_mixed_special_stimuli"
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
print all_parameters
# Create DataIO
# (complete label with current variable state)
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Parameters to vary
M_space = np.arange(1, all_parameters['M']+1)
M_lower_space = np.arange(2, all_parameters['M']+1, 2)
MMlower_all = np.array(cross(M_space, M_lower_space))
MMlower_valid_space = MMlower_all[np.nonzero(np.sum(MMlower_all, axis=1) == all_parameters['M'])[0]]
# limit space, not too big...
MMlower_valid_space = MMlower_valid_space[::5]
print "MMlower size", MMlower_valid_space.shape[0]
# Result arrays
result_all_precisions = np.nan*np.ones((MMlower_valid_space.shape[0], all_parameters['num_repetitions']))
result_em_fits = np.nan*np.ones((MMlower_valid_space.shape[0], 5, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll
result_em_resp = np.nan*np.ones((MMlower_valid_space.shape[0], 1+all_parameters['T'], all_parameters['N'], all_parameters['num_repetitions']))
# If desired, will automatically save all Model responses.
if all_parameters['subaction'] == 'collect_responses':
result_responses = np.nan*np.ones((MMlower_valid_space.shape[0], all_parameters['N'], all_parameters['num_repetitions']))
result_target = np.nan*np.ones((MMlower_valid_space.shape[0], all_parameters['N'], all_parameters['num_repetitions']))
result_nontargets = np.nan*np.ones((MMlower_valid_space.shape[0], all_parameters['N'], all_parameters['T']-1, all_parameters['num_repetitions']))
search_progress = progress.Progress(MMlower_valid_space.shape[0]*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for MMlower_i, MMlower in enumerate(MMlower_valid_space):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for M=%d, Mlower=%d, %d/%d" % (MMlower[0], MMlower[1], repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['M'] = MMlower[0]
all_parameters['M_layer_one'] = MMlower[1]
### WORK WORK WORK work? ###
# Generate specific stimuli
all_parameters['stimuli_generation'] = 'specific_stimuli'
all_parameters['code_type'] = 'hierarchical'
# Instantiate
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
result_all_precisions[MMlower_i, repet_i] = sampler.get_precision()
# Fit mixture model
curr_params_fit = em_circularmixture.fit(*sampler.collect_responses())
curr_resp = em_circularmixture.compute_responsibilities(*(sampler.collect_responses() + (curr_params_fit,) ))
print curr_params_fit
result_em_fits[MMlower_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random', 'train_LL')]
result_em_resp[MMlower_i, 0, :, repet_i] = curr_resp['target']
result_em_resp[MMlower_i, 1:-1, :, repet_i] = curr_resp['nontargets'].T
result_em_resp[MMlower_i, -1, :, repet_i] = curr_resp['random']
# If needed, store responses
if all_parameters['subaction'] == 'collect_responses':
(responses, target, nontarget) = sampler.collect_responses()
result_responses[MMlower_i, :, repet_i] = responses
result_target[MMlower_i, :, repet_i] = target
result_nontargets[MMlower_i, ..., repet_i] = nontarget
print "collected responses"
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_fit_mixturemodels(args):
'''
Run the model for 1..T items, computing:
- Precision of samples
- EM mixture model fits
- Theoretical Fisher Information
- EM Mixture model distances to set of currently working datasets.
'''
print "Doing a piece of work for launcher_do_fit_mixturemodels"
all_parameters = utils.argparse_2_dict(args)
print all_parameters
if all_parameters['burn_samples'] + all_parameters['num_samples'] < 200:
print "WARNING> you do not have enough samples I think!", all_parameters['burn_samples'] + all_parameters['num_samples']
# Create DataIO
# (complete label with current variable state)
dataio = DataIO.DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Load datasets to compare against
data_bays2009 = load_experimental_data.load_data_bays09(data_dir=all_parameters['experiment_data_dir'], fit_mixture_model=True)
bays09_experimental_mixtures_mean = data_bays2009['em_fits_nitems_arrays']['mean']
# Assume that T_space >= max(T_space_bays09)
bays09_T_space = np.unique(data_bays2009['n_items'])
data_gorgo11 = load_experimental_data.load_data_simult(data_dir=all_parameters['experiment_data_dir'], fit_mixture_model=True)
gorgo11_experimental_emfits_mean = data_gorgo11['em_fits_nitems_arrays']['mean']
gorgo11_T_space = np.unique(data_gorgo11['n_items'])
# Parameters to vary
T_max = all_parameters['T']
T_space = np.arange(1, T_max+1)
repetitions_axis = -1
# Result arrays
result_all_precisions = np.nan*np.empty((T_space.size, all_parameters['num_repetitions']))
# result_fi_theo = np.nan*np.empty((T_space.size, all_parameters['num_repetitions']))
# result_fi_theocov = np.nan*np.empty((T_space.size, all_parameters['num_repetitions']))
result_em_fits = np.nan*np.empty((T_space.size, 6, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll, bic
# result_em_fits_allnontargets = np.nan*np.empty((T_space.size, 5+(T_max-1), all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget (T-1), mixt_random, ll, bic
result_dist_bays09 = np.nan*np.empty((T_space.size, 4, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random
result_dist_gorgo11 = np.nan*np.empty((T_space.size, 4, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random
result_dist_bays09_emmixt_KL = np.nan*np.empty((T_space.size, all_parameters['num_repetitions']))
result_dist_gorgo11_emmixt_KL = np.nan*np.empty((T_space.size, all_parameters['num_repetitions']))
# If desired, will automatically save all Model responses.
if all_parameters['collect_responses']:
print "--- Collecting all responses..."
result_responses = np.nan*np.ones((T_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_target = np.nan*np.ones((T_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_nontargets = np.nan*np.ones((T_space.size, all_parameters['N'], T_max-1, all_parameters['num_repetitions']))
search_progress = progress.Progress(T_space.size*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for T_i, T in enumerate(T_space):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for T=%d, %d/%d" % (T, repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['T'] = T
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
print "get precision..."
result_all_precisions[T_i, repet_i] = sampler.get_precision()
# Fit mixture model
print "fit mixture model..."
curr_params_fit = sampler.fit_mixture_model(use_all_targets=False)
# curr_params_fit['mixt_nontargets_sum'] = np.sum(curr_params_fit['mixt_nontargets'])
result_em_fits[T_i, :, repet_i] = [curr_params_fit[key] for key in ['kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL', 'bic']]
# result_em_fits_allnontargets[T_i, :2, repet_i] = [curr_params_fit['kappa'], curr_params_fit['mixt_target']]
# result_em_fits_allnontargets[T_i, 2:(2+T-1), repet_i] = curr_params_fit['mixt_nontargets']
# result_em_fits_allnontargets[T_i, -3:, repet_i] = [curr_params_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]
# Compute fisher info
# print "compute fisher info"
# result_fi_theo[T_i, repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=False)
# result_fi_theocov[T_i, repet_i] = sampler.estimate_fisher_info_theocov(use_theoretical_cov=True)
# Compute distances to datasets
if T in bays09_T_space:
result_dist_bays09[T_i, :, repet_i] = (bays09_experimental_mixtures_mean[:, bays09_T_space == T].flatten() - result_em_fits[T_i, :4, repet_i])**2.
result_dist_bays09_emmixt_KL[T_i, repet_i] = utils.KL_div(result_em_fits[T_i, 1:4, repet_i], bays09_experimental_mixtures_mean[1:, bays09_T_space == T].flatten())
if T in gorgo11_T_space:
result_dist_gorgo11[T_i, :, repet_i] = (gorgo11_experimental_emfits_mean[:, gorgo11_T_space == T].flatten() - result_em_fits[T_i, :4, repet_i])**2.
result_dist_gorgo11_emmixt_KL[T_i, repet_i] = utils.KL_div(result_em_fits[T_i, 1:4, repet_i], gorgo11_experimental_emfits_mean[1:, gorgo11_T_space == T].flatten())
# If needed, store responses
if all_parameters['collect_responses']:
(responses, target, nontarget) = sampler.collect_responses()
result_responses[T_i, :, repet_i] = responses
result_target[T_i, :, repet_i] = target
result_nontargets[T_i, :, :T_i, repet_i] = nontarget
print "collected responses"
print "CURRENT RESULTS:\n", result_all_precisions[T_i, repet_i], curr_params_fit, np.sum(result_dist_bays09[T_i, :, repet_i]), np.sum(result_dist_gorgo11[T_i, :, repet_i]), "\n"
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_mixed_special_stimuli(args):
'''
Fit mixed model, varying the ratio_conj
See how the precision of recall and mixture model parameters evolve
'''
print "Doing a piece of work for launcher_do_mixed_special_stimuli"
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
print all_parameters
# Create DataIO
# (complete label with current variable state)
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Parameters to vary
ratio_space = (np.arange(0, all_parameters['M']**0.5)**2.)/all_parameters['M']
# Result arrays
result_all_precisions = np.nan*np.ones((ratio_space.size, all_parameters['num_repetitions']))
result_em_fits = np.nan*np.ones((ratio_space.size, 5, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll
result_em_resp = np.nan*np.ones((ratio_space.size, 1+all_parameters['T'], all_parameters['N'], all_parameters['num_repetitions']))
# If desired, will automatically save all Model responses.
if all_parameters['subaction'] == 'collect_responses':
result_responses = np.nan*np.ones((ratio_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_target = np.nan*np.ones((ratio_space.size, all_parameters['N'], all_parameters['num_repetitions']))
result_nontargets = np.nan*np.ones((ratio_space.size, all_parameters['N'], all_parameters['T']-1, all_parameters['num_repetitions']))
search_progress = progress.Progress(ratio_space.size*all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
for ratio_i, ratio_conj in enumerate(ratio_space):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for ratio_conj=%.2f, %d/%d" % (ratio_conj, repet_i+1, all_parameters['num_repetitions'])
# Update parameter
all_parameters['ratio_conj'] = ratio_conj
### WORK WORK WORK work? ###
# Generate specific stimuli
all_parameters['stimuli_generation'] = 'specific_stimuli'
# Instantiate
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Compute precision
result_all_precisions[ratio_i, repet_i] = sampler.get_precision()
# Fit mixture model
curr_params_fit = em_circularmixture.fit(*sampler.collect_responses())
curr_resp = em_circularmixture.compute_responsibilities(*(sampler.collect_responses() + (curr_params_fit,) ))
result_em_fits[ratio_i, :, repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random', 'train_LL')]
result_em_resp[ratio_i, 0, :, repet_i] = curr_resp['target']
result_em_resp[ratio_i, 1:-1, :, repet_i] = curr_resp['nontargets'].T
result_em_resp[ratio_i, -1, :, repet_i] = curr_resp['random']
print result_all_precisions[ratio_i, repet_i], curr_params_fit
# If needed, store responses
if all_parameters['subaction'] == 'collect_responses':
(responses, target, nontarget) = sampler.collect_responses()
result_responses[ratio_i, :, repet_i] = responses
result_target[ratio_i, :, repet_i] = target
result_nontargets[ratio_i, ..., repet_i] = nontarget
print "collected responses"
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()
def launcher_do_variability_mixture(args):
'''
Compute posterior in no noise case, to see the effect of ratio on the mixture probability
'''
print "Doing a piece of work for launcher_do_variability_mixture"
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
# Create DataIO
# (complete label with current variable state)
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
# Do it for multiple ratios and multiple sigmas
plt.ion()
# Fix some parameters
all_parameters['stimuli_generation'] = 'separated'
all_parameters['stimuli_generation_recall'] = 'random'
all_parameters['enforce_first_stimulus'] = False
all_parameters['num_samples'] = 500
all_parameters['selection_method'] = 'last'
all_parameters['code_type'] = 'mixed'
all_parameters['autoset_parameters'] = True
all_parameters['M'] = 100
all_parameters['N'] = 100
all_parameters['inference_method'] = 'none'
all_parameters['T'] = 2
all_parameters['sigmay'] = 0.0000001
# ratio_space = np.array([0.01, 0.05, 0.1, 0.3, 0.5, 0.7, 0.9])
ratio_space = np.linspace(0.01, 0.7, 20.)
all_parameters['sigmax'] = 0.05
num_points = 500
result_all_posterior = np.nan*np.ones((ratio_space.size, all_parameters['N'], num_points))
result_all_mixture_params = np.nan*np.ones((ratio_space.size, all_parameters['N'], 3))
result_all_bimodality_tests = np.nan*np.ones((ratio_space.size, all_parameters['N'], 2))
search_progress = progress.Progress(ratio_space.size*all_parameters['N'])
save_every = 10
print_every = 10
run_counter = 0
ax_handle = None
all_angles = np.linspace(-np.pi, np.pi, num_points)
for ratio_i, ratio_conj in enumerate(ratio_space):
all_parameters['ratio_conj'] = ratio_conj
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(all_parameters)
### WORK WORK WORK work? ###
print "Average posterior..."
for n in xrange(all_parameters['N']):
if run_counter % print_every == 0:
print "%.2f%% %s/%s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
result_all_posterior[ratio_i, n] = sampler.compute_likelihood_fullspace(n=n, all_angles=all_angles, num_points=num_points, should_exponentiate=True, remove_mean=True)[:, -1].T
result_all_mixture_params[ratio_i, n] = fit_gaussian_mixture_fixedmeans(all_angles, result_all_posterior[ratio_i, n], fixed_means=data_gen.stimuli_correct[n, :, -1], normalise=True, return_fitted_data=False, should_plot=False)
result_all_bimodality_tests[ratio_i, n] = (bimodality_coefficient(all_angles, result_all_posterior[ratio_i, n]),
ashman_d(all_angles, result_all_posterior[ratio_i, n])
)
### /Work ###
search_progress.increment()
# if run_counter % save_every == 0 or search_progress.done():
# dataio.save_variables_default(locals())
print result_all_bimodality_tests
plt.figure()
plt.plot(ratio_space, np.mean(result_all_bimodality_tests[..., 0], axis=1))
plt.title('Bimodality coefficient')
plt.figure()
plt.plot(ratio_space, np.mean(result_all_bimodality_tests[..., 1], axis=1))
plt.title('Ashman D')
plt.figure()
plt.plot(ratio_space, np.mean(result_all_mixture_params[..., 0], axis=1))
plt.title('Mixture proportion')
# plt.figure()
# plt.plot(np.mean(np.abs(result_all_mixture_params[..., 1] - result_all_mixture_params[..., 3]), axis=1))
# plt.title('|mu_1 - mu_2|')
plt.figure()
plt.plot(ratio_space, np.mean(result_all_mixture_params[..., 0]*data_gen.stimuli_correct[:, 0, -1] + (1.-result_all_mixture_params[..., 0])*data_gen.stimuli_correct[:, 1, -1], axis=1))
plt.title('alpha mu_1 + alpha mu_2')
return locals()
def launcher_do_error_distributions(args):
'''
Collect responses for error distribution plots (used in generator/reloader_error_distribution_*.py)
Do it for T items.
Looks like the Bays 2009, used in paper.
'''
print "Doing a piece of work for launcher_do_error_distributions"
try:
# Convert Argparse.Namespace to dict
all_parameters = vars(args)
except TypeError:
# Assume it's already done
assert type(args) is dict, "args is neither Namespace nor dict, WHY?"
all_parameters = args
print all_parameters
# Create DataIO
# (complete label with current variable state)
dataio = DataIO(output_folder=all_parameters['output_directory'], label=all_parameters['label'].format(**all_parameters))
save_every = 1
run_counter = 0
# Result arrays
result_responses = np.nan*np.ones((all_parameters['N'], all_parameters['num_repetitions']))
result_target = np.nan*np.ones((all_parameters['N'], all_parameters['num_repetitions']))
result_nontargets = np.nan*np.ones((all_parameters['N'], all_parameters['T']-1, all_parameters['num_repetitions']))
result_em_fits = np.nan*np.ones((5, all_parameters['num_repetitions'])) # kappa, mixt_target, mixt_nontarget, mixt_random, ll
search_progress = progress.Progress(all_parameters['num_repetitions'])
for repet_i in xrange(all_parameters['num_repetitions']):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for T=%d, %d/%d" % (all_parameters['T'], repet_i+1, all_parameters['num_repetitions'])
# Update parameter
### WORK WORK WORK work? ###
# Instantiate
(_, _, _, sampler) = launchers.init_everything(all_parameters)
# Sample
sampler.run_inference(all_parameters)
# Collect and store responses
(responses, target, nontarget) = sampler.collect_responses()
result_responses[:, repet_i] = responses
result_target[:, repet_i] = target
result_nontargets[..., repet_i] = nontarget
# Fit mixture model
curr_params_fit = em_circularmixture.fit(*sampler.collect_responses())
result_em_fits[..., repet_i] = [curr_params_fit[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random', 'train_LL')]
### /Work ###
search_progress.increment()
if run_counter % save_every == 0 or search_progress.done():
dataio.save_variables_default(locals())
run_counter += 1
# Finished
dataio.save_variables_default(locals())
print "All finished"
return locals()