def compute_result_distfitexpbic(self, all_variables):
'''
Result is the summed BIC score of the FitExperiment result on all datasets
'''
if 'result_fitexperiments' in all_variables:
# We have result_fitexperiments, that's good.
# Make it work for either fixed T or multiple T.
if len(all_variables['result_fitexperiments'].shape) == 2:
# Single T. Average over axis -1
bic_summed = utils.nanmean(all_variables['result_fitexperiments'][0])
elif len(all_variables['result_fitexperiments'].shape) == 3:
# Multiple T. Average over axis -1, then sum
bic_summed = np.nansum(
utils.nanmean(
all_variables['result_fitexperiments'][:, 0], axis=-1))
else:
raise ValueError("wrong shape for result_fitexperiments: {}".format(
all_variables['result_fitexperiments'].shape))
else:
# We do not have it, could instantiate a FitExperiment with "normal" parameters and work from there instead
raise NotImplementedError(
'version without result_fitexperiments not implemented yet')
return bic_summed
def compute_result_dist_prodll_allt(self, all_variables):
'''
Given outputs from FitExperimentAllT, will compute the geometric mean of the LL.
UGLY HACK: in order to keep track of the minLL, we return it here.
You should have a cma_iter_function that cleans it before cma_es.tell() is called...
'''
if 'result_ll_sum' in all_variables:
repetitions_axis = all_variables.get('repetitions_axis', -1)
# Shift to get LL > 0 always
currMinLL = np.min(all_variables['result_ll_sum'])
if currMinLL < all_variables['all_parameters']['shiftMinLL']:
all_variables['all_parameters']['shiftMinLL'] = currMinLL
# Remove the current minLL, to make sure fitness > 0
print 'Before: ', all_variables['result_ll_sum']
all_variables['result_ll_sum'] -= all_variables['all_parameters'][
'shiftMinLL']
all_variables['result_ll_sum'] += 0.001
print 'Shifted: ', all_variables['result_ll_sum']
result_dist_nll_geom = -mstats.gmean(
utils.nanmean(all_variables['result_ll_sum'], axis=repetitions_axis),
axis=-1)
print result_dist_nll_geom
return np.array([
result_dist_nll_geom, all_variables['all_parameters']['shiftMinLL']
])
else:
raise ValueError('result_ll_sum was not found in the outputs')
def _build_graph(self):
self._normalize()
with tf.device(self.device):
_prediction_norm = self._inference(self.x_norm)
self.loss = self._loss(_prediction_norm)
self._optimize()
self.prediction = _prediction_norm * tf.sqrt(self.y_variance) + self.y_mean
self.rmse = tf.sqrt(utils.nanmean(tf.square(self.prediction - self.y)),
name='rmse')
self._summaries()
def _build_graph(self):
self._normalize()
with tf.device(self.device):
_prediction_norm = self._inference(self.x_norm)
self.loss = self._loss(_prediction_norm)
self._optimize()
self.prediction = _prediction_norm * tf.sqrt(
self.y_variance) + self.y_mean
self.rmse = tf.sqrt(utils.nanmean(tf.square(self.prediction - self.y)),
name='rmse')
self._summaries()
def plots_memmixtcurves_fig6fig13(self, num_repetitions=1, cache=True):
"""
Plots the memory fidelity for all T and the mixture proportions for all T
"""
if self.result_em_fits_stats is None:
search_progress = progress.Progress(self.fit_exp.T_space.size * num_repetitions)
# kappa, mixt_target, mixt_nontarget, mixt_random, ll
result_em_fits = np.nan * np.ones((self.fit_exp.T_space.size, 5, num_repetitions))
for T_i, T in enumerate(self.fit_exp.T_space):
self.fit_exp.setup_experimental_stimuli_T(T)
for repet_i in xrange(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" % (T, repet_i + 1, num_repetitions)
self.fit_exp.sampler.force_sampling_round()
# Fit mixture model
curr_params_fit = self.fit_exp.sampler.fit_mixture_model(use_all_targets=False)
result_em_fits[T_i, :, repet_i] = [
curr_params_fit[key]
for key in ("kappa", "mixt_target", "mixt_nontargets_sum", "mixt_random", "train_LL")
]
search_progress.increment()
# Get stats of EM Fits
self.result_em_fits_stats = dict(
mean=utils.nanmean(result_em_fits, axis=-1), std=utils.nanstd(result_em_fits, axis=-1)
)
# Do the plots
if self.do_memcurves_fig6 and self.do_mixtcurves_fig13:
f, axes = plt.subplots(nrows=2, figsize=(14, 18))
else:
f, ax = plt.subplots(figsize=(14, 9))
axes = [ax]
ax_i = 0
if self.do_memcurves_fig6:
self.__plot_memcurves(self.result_em_fits_stats, suptitle_text="Memory fidelity", ax=axes[ax_i])
ax_i += 1
if self.do_mixtcurves_fig13:
self.__plot_mixtcurves(self.result_em_fits_stats, suptitle_text="Mixture proportions", ax=axes[ax_i])
return axes
def compute_result_distfit_givenexp(
self,
all_variables,
experiment_id='bays09',
metric_index=0,
target_array_name='result_fitexperiments_all'):
'''
Result is the summed BIC score of the FitExperiment result on a given dataset
'''
if target_array_name in all_variables:
# We have target_array_name (result_fitexperiments_all or result_fitexperiments_noiseconv_all say), that's good.
# Extract only the Bays09 result
experiment_index = all_variables['all_parameters'][
'experiment_ids'].index(experiment_id)
# Make it work for either fixed T or multiple T.
if len(all_variables[target_array_name].shape) == 3:
# Single T. Average over axis -1
bic_summed = utils.nanmean(
all_variables[target_array_name][metric_index, experiment_index])
elif len(all_variables[target_array_name].shape) == 4:
# Multiple T. Average over axis -1, then sum
bic_summed = np.nansum(
utils.nanmean(
all_variables[target_array_name][:, metric_index,
experiment_index],
axis=-1))
else:
raise ValueError(
"wrong shape for result_fitexperiments_all: {}".format(
all_variables[target_array_name].shape))
else:
# We do not have it, could instantiate a FitExperiment with "normal" parameters and work from there instead
raise NotImplementedError(
'version without result_fitexperiments_all not implemented yet')
return bic_summed
def compute_result_dist_ll_median_allt(self, all_variables):
'''
Use the median of LL to get a score.
'''
if 'result_ll_n' in all_variables:
repetitions_axis = all_variables.get('repetitions_axis', -1)
data_ll = -all_variables['result_ll_n']
data_ll = data_ll.reshape(-1, data_ll.shape[repetitions_axis])
result_dist = utils.nanmean(np.nanmedian(data_ll, axis=0))
print result_dist
return result_dist
else:
raise ValueError("%s was not found in the outputs" % res_variant)
def compute_result_dist_collapsedemfit_gorgo11seq(self, all_variables):
'''
Use the collapsed mixture fits to Gorgo11 Sequential.
Should be precomputed by FitExperimentSequential
'''
res_variant = 'result_emfit_mse'
if res_variant in all_variables:
repetitions_axis = all_variables.get('repetitions_axis', -1)
data_fits = all_variables[res_variant]
result_dist = utils.nanmean(data_fits, axis=repetitions_axis)
print result_dist
return result_dist
else:
raise ValueError("%s was not found in the outputs" % res_variant)
def dice_coefficient(hist):
"""Computes the Sørensen–Dice coefficient, a.k.a the F1 score.
Args:
hist: confusion matrix.
Returns:
avg_dice: the average per-class dice coefficient.
"""
A_inter_B = torch.diag(hist)
A = hist.sum(dim=1)
B = hist.sum(dim=0)
dice = (2 * A_inter_B) / (A + B + EPS)
avg_dice = nanmean(dice)
return avg_dice
def jaccard_index(hist):
"""Computes the Jaccard index, a.k.a the Intersection over Union (IoU).
Args:
hist: confusion matrix.
Returns:
avg_jacc: the average per-class jaccard index.
"""
A_inter_B = torch.diag(hist)
A = hist.sum(dim=1)
B = hist.sum(dim=0)
jaccard = A_inter_B / (A + B - A_inter_B + EPS)
avg_jacc = nanmean(jaccard)
return avg_jacc
def compute_result_dist_emfit_scaled(self, all_variables):
'''
Use the scaled MSE to the mixture model fits.
Should have been precomputed by launchers_fitexperiment_allt.
'''
res_variant = 'result_emfit_mse_scaled'
if res_variant in all_variables:
repetitions_axis = all_variables.get('repetitions_axis', -1)
data_fits = all_variables[res_variant]
result_dist = np.nansum(utils.nanmean(data_fits, axis=repetitions_axis))
print result_dist
return result_dist
else:
raise ValueError("%s was not found in the outputs" % res_variant)
def _compute_dist_llvariant(self, all_variables, variant='ll'):
'''
Given outputs from FitExperimentAllT, will compute the summed LL,
as this seems to be an acceptable metric for data fitting.
'''
res_variant = 'result_%s_sum' % variant
if res_variant in all_variables:
# Average over repetitions and sum over the rest.
repetitions_axis = all_variables.get('repetitions_axis', -1)
result_dist = np.nansum(
utils.nanmean(-all_variables[res_variant], axis=repetitions_axis))
print result_dist
return result_dist
else:
raise ValueError("%s was not found in the outputs" % res_variant)
def per_class_pixel_accuracy(hist):
"""Computes the average per-class pixel accuracy.
The per-class pixel accuracy is a more fine-grained
version of the overall pixel accuracy. A model could
score a relatively high overall pixel accuracy by
correctly predicting the dominant labels or areas
in the image whilst incorrectly predicting the
possibly more important/rare labels. Such a model
will score a low per-class pixel accuracy.
Args:
hist: confusion matrix.
Returns:
avg_per_class_acc: the average per-class pixel accuracy.
"""
correct_per_class = torch.diag(hist)
total_per_class = hist.sum(dim=1)
per_class_acc = correct_per_class / (total_per_class + EPS)
avg_per_class_acc = nanmean(per_class_acc)
return avg_per_class_acc
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 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_kappa = False
plots_kappa_fi_comparison = False
plots_multiple_fisherinfo = False
specific_plot_effect_R = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = (utils.nanmean(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_all_precisions_std = (utils.nanstd(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_em_fits_mean = (utils.nanmean(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
result_em_fits_std = (utils.nanstd(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
result_fisherinfo_mean = (utils.nanmean(data_pbs.dict_arrays['result_fisher_info']['results'], axis=-1))
result_fisherinfo_std = (utils.nanstd(data_pbs.dict_arrays['result_fisher_info']['results'], axis=-1))
all_args = data_pbs.loaded_data['args_list']
result_em_fits_kappa = result_em_fits_mean[..., 0]
result_em_fits_target = result_em_fits_mean[..., 1]
result_em_fits_kappa_valid = np.ma.masked_where(result_em_fits_target < 0.9, result_em_fits_kappa)
M_space = data_pbs.loaded_data['parameters_uniques']['M'].astype(int)
ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']
R_space = data_pbs.loaded_data['parameters_uniques']['R'].astype(int)
num_repetitions = generator_module.num_repetitions
print M_space
print ratio_space
print R_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'])
MAX_DISTANCE = 100.
if plots_pcolor_all:
# Do one pcolor for M and ratio per R
for R_i, R in enumerate(R_space):
# Check evolution of precision given M and ratio
# utils.pcolor_2d_data(result_all_precisions_mean[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='precision, R=%d' % R)
# if savefigs:
# dataio.save_current_figure('pcolor_precision_R%d_log_{label}_{unique_id}.pdf' % R)
# Show kappa
try:
utils.pcolor_2d_data(result_em_fits_kappa_valid[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='kappa, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_kappa_R%d_log_{label}_{unique_id}.pdf' % R)
except ValueError:
pass
# Show probability on target
# utils.pcolor_2d_data(result_em_fits_target[..., R_i], log_scale=False, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='target, R=%d' % R)
# if savefigs:
# dataio.save_current_figure('pcolor_target_R%d_{label}_{unique_id}.pdf' % R)
# # Show Fisher info
# utils.pcolor_2d_data(result_fisherinfo_mean[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='fisher info, R=%d' % R)
# if savefigs:
# dataio.save_current_figure('pcolor_fisherinfo_R%d_log_{label}_{unique_id}.pdf' % R)
plt.close('all')
if plots_effect_M_target_kappa:
def plot_ratio_target_kappa(ratio_target_kappa_given_M, target_kappa, R):
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 kappa %d, R=%d' % (target_kappa, R))
if savefigs:
dataio.save_current_figure('optratio_M_targetkappa%d_R%d_{label}_{unique_id}.pdf' % (target_kappa, R))
target_kappas = np.array([100, 200, 300, 500, 1000, 3000])
for R_i, R in enumerate(R_space):
for target_kappa in target_kappas:
dist_to_target_kappa = (result_em_fits_kappa[..., R_i] - 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, R)
plt.close('all')
if plots_kappa_fi_comparison:
# result_em_fits_kappa and fisher info
if True:
for R_i, R in enumerate(R_space):
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, 1)
axes[0].plot(ratio_space, result_em_fits_kappa[2*M_tot_selected_i, ..., R_i])
axes[0].set_xlabel('ratio')
axes[0].set_title('Fitted kappa')
axes[1].plot(ratio_space, utils.stddev_to_kappa(1./result_fisherinfo_mean[2*M_tot_selected_i, ..., R_i]**0.5))
axes[1].set_xlabel('ratio')
axes[1].set_title('kappa_FI')
f.suptitle('M_tot %d' % M_tot_selected, fontsize=15)
f.set_tight_layout(True)
if savefigs:
dataio.save_current_figure('comparison_kappa_fisher_R%d_M%d_{label}_{unique_id}.pdf' % (R, M_tot_selected))
plt.close(f)
if plots_multiple_fisherinfo:
target_fisherinfos = np.array([100, 200, 300, 500, 1000])
for R_i, R in enumerate(R_space):
for target_fisherinfo in target_fisherinfos:
dist_to_target_fisherinfo = (result_fisherinfo_mean[..., R_i] - target_fisherinfo)**2.
utils.pcolor_2d_data(dist_to_target_fisherinfo, log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='Fisher info, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_distfi%d_R%d_log_{label}_{unique_id}.pdf' % (target_fisherinfo, R))
plt.close('all')
if specific_plot_effect_R:
# Choose a M, find which ratio gives best fit to a given kappa
M_target = 228
M_target_i = np.argmin(np.abs(M_space - M_target))
# target_kappa = np.ma.mean(result_em_fits_kappa_valid[M_target_i])
# target_kappa = 5*1e3
target_kappa = 1e3
dist_target_kappa = (result_em_fits_kappa_valid[M_target_i] - target_kappa)**2.
utils.pcolor_2d_data(dist_target_kappa, log_scale=True, x=ratio_space, y=R_space, xlabel='ratio', ylabel='R', ylabel_format="%d", title='Kappa dist %.2f, M %d' % (target_kappa, M_target))
if savefigs:
dataio.save_current_figure('pcolor_distkappa%d_M%d_log_{label}_{unique_id}.pdf' % (target_kappa, M_target))
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='higher_dimensions_R')
plt.show()
return locals()
def do_sigma_output_plot(variables_launcher_running, plotting_parameters):
dataio = variables_launcher_running['dataio']
sigmaoutput_space = variables_launcher_running['sigmaoutput_space']
result_em_fits_mean = utils.nanmean(variables_launcher_running['result_em_fits'], axis=-1)
result_em_fits_std = utils.nanstd(variables_launcher_running['result_em_fits'], axis=-1)
plt.ion()
# Memory curve kappa
def sigmaoutput_plot_kappa(sigmaoutput_space, result_em_fits_mean, result_em_fits_std=None, exp_name='', ax=None):
if ax is not None:
plt.figure(ax.get_figure().number)
ax.hold(False)
ax = utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 0], result_em_fits_std[..., 0], xlabel='sigma output', ylabel='Memory fidelity', linewidth=3, fmt='o-', markersize=8, label='Noise output effect', ax_handle=ax)
ax.hold(True)
ax.set_title("{{exp_name}} {T} {M} {ratio_conj:.2f} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']).format(exp_name=exp_name))
ax.legend()
# ax.set_xlim([0.9, T_space_exp.max()+0.1])
# ax.set_xticks(range(1, T_space_exp.max()+1))
# ax.set_xticklabels(range(1, T_space_exp.max()+1))
ax.get_figure().canvas.draw()
dataio.save_current_figure('noiseoutput_kappa_%s_T{T}_M{M}_ratio{ratio_conj}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']) % (exp_name))
return ax
# Plot EM Mixtures proportions
def sigmaoutput_plot_mixtures(sigmaoutput_space, result_em_fits_mean, result_em_fits_std, exp_name='', ax=None):
if ax is None:
_, ax = plt.subplots()
if ax is not None:
plt.figure(ax.get_figure().number)
ax.hold(False)
# mixture probabilities
print result_em_fits_mean[..., 1]
result_em_fits_mean[np.isnan(result_em_fits_mean)] = 0.0
result_em_fits_std[np.isnan(result_em_fits_std)] = 0.0
utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 1], result_em_fits_std[..., 1], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target')
ax.hold(True)
utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 2], result_em_fits_std[..., 2], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget')
utils.plot_mean_std_area(sigmaoutput_space, result_em_fits_mean[..., 3], result_em_fits_std[..., 3], xlabel='sigma output', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Random')
ax.legend(prop={'size':15})
ax.set_title("{{exp_name}} {T} {M} {ratio_conj:.2f} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']).format(exp_name=exp_name))
ax.set_ylim([0.0, 1.1])
ax.get_figure().canvas.draw()
dataio.save_current_figure('memorycurves_emfits_%s_T{T}_M{M}_ratio{ratio_conj}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']) % (exp_name))
return ax
# Do plots
plotting_parameters['axes']['ax_sigmaoutput_kappa'] = sigmaoutput_plot_kappa(sigmaoutput_space, result_em_fits_mean, result_em_fits_std, exp_name='kappa', ax=plotting_parameters['axes']['ax_sigmaoutput_kappa'])
plotting_parameters['axes']['ax_sigmaoutput_mixtures'] = sigmaoutput_plot_mixtures(sigmaoutput_space, result_em_fits_mean, result_em_fits_std, exp_name='mixt probs', ax=plotting_parameters['axes']['ax_sigmaoutput_mixtures'])
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_kappa = False
plots_kappa_fi_comparison = False
plots_multiple_fisherinfo = False
specific_plot_effect_R = True
specific_plot_effect_ratio_M = False
convert_M_realsizes = False
plots_pcolor_realsizes_Msubs = True
plots_pcolor_realsizes_Mtot = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
# interpolation_method = 'linear'
interpolation_method = 'nearest'
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = (utils.nanmean(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_all_precisions_std = (utils.nanstd(data_pbs.dict_arrays['result_all_precisions']['results'], axis=-1))
result_em_fits_mean = (utils.nanmean(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
result_em_fits_std = (utils.nanstd(data_pbs.dict_arrays['result_em_fits']['results'], axis=-1))
result_fisherinfo_mean = (utils.nanmean(data_pbs.dict_arrays['result_fisher_info']['results'], axis=-1))
result_fisherinfo_std = (utils.nanstd(data_pbs.dict_arrays['result_fisher_info']['results'], axis=-1))
all_args = data_pbs.loaded_data['args_list']
result_em_fits_kappa = result_em_fits_mean[..., 0]
result_em_fits_target = result_em_fits_mean[..., 1]
result_em_fits_kappa_valid = np.ma.masked_where(result_em_fits_target < 0.8, result_em_fits_kappa)
# flat versions
result_parameters_flat = np.array(data_pbs.dict_arrays['result_all_precisions']['parameters_flat'])
result_all_precisions_mean_flat = np.mean(np.array(data_pbs.dict_arrays['result_all_precisions']['results_flat']), axis=-1)
result_em_fits_mean_flat = np.mean(np.array(data_pbs.dict_arrays['result_em_fits']['results_flat']), axis=-1)
result_fisherinfor_mean_flat = np.mean(np.array(data_pbs.dict_arrays['result_fisher_info']['results_flat']), axis=-1)
result_em_fits_kappa_flat = result_em_fits_mean_flat[..., 0]
result_em_fits_target_flat = result_em_fits_mean_flat[..., 1]
result_em_fits_kappa_valid_flat = np.ma.masked_where(result_em_fits_target_flat < 0.8, result_em_fits_kappa_flat)
M_space = data_pbs.loaded_data['parameters_uniques']['M'].astype(int)
ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']
R_space = data_pbs.loaded_data['parameters_uniques']['R'].astype(int)
num_repetitions = generator_module.num_repetitions
T = generator_module.T
print M_space
print ratio_space
print R_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'])
MAX_DISTANCE = 100.
if convert_M_realsizes:
# alright, currently M*ratio_conj gives the conjunctive subpopulation,
# but only floor(M_conj**1/R) neurons are really used. So we should
# convert to M_conj_real and M_feat_real instead of M and ratio
result_parameters_flat_subM_converted = []
result_parameters_flat_Mtot_converted = []
for params in result_parameters_flat:
M = params[0]; ratio_conj = params[1]; R = int(params[2])
M_conj_prior = int(M*ratio_conj)
M_conj_true = int(np.floor(M_conj_prior**(1./R))**R)
M_feat_true = int(np.floor((M-M_conj_prior)/R)*R)
# result_parameters_flat_subM_converted contains (M_conj, M_feat, R)
result_parameters_flat_subM_converted.append(np.array([M_conj_true, M_feat_true, R]))
# result_parameters_flat_Mtot_converted contains (M_tot, ratio_conj, R)
result_parameters_flat_Mtot_converted.append(np.array([float(M_conj_true+M_feat_true), float(M_conj_true)/float(M_conj_true+M_feat_true), R]))
result_parameters_flat_subM_converted = np.array(result_parameters_flat_subM_converted)
result_parameters_flat_Mtot_converted = np.array(result_parameters_flat_Mtot_converted)
if plots_pcolor_all:
if convert_M_realsizes:
def plot_interp(points, data, currR_indices, title='', points_label='', xlabel='', ylabel=''):
utils.contourf_interpolate_data_interactive_maxvalue(points[currR_indices][..., :2], data[currR_indices], xlabel=xlabel, ylabel=ylabel, title='%s, R=%d' % (title, R), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False)
if savefigs:
dataio.save_current_figure('pcolortrueM%s_%s_R%d_log_%s_{label}_{unique_id}.pdf' % (points_label, title, R, interpolation_method))
all_datas = [dict(name='precision', data=result_all_precisions_mean_flat), dict(name='kappa', data=result_em_fits_kappa_flat), dict(name='kappavalid', data=result_em_fits_kappa_valid_flat), dict(name='target', data=result_em_fits_target_flat), dict(name='fisherinfo', data=result_fisherinfor_mean_flat)]
all_points = []
if plots_pcolor_realsizes_Msubs:
all_points.append(dict(name='sub', data=result_parameters_flat_subM_converted, xlabel='M_conj', ylabel='M_feat'))
if plots_pcolor_realsizes_Mtot:
all_points.append(dict(name='tot', data=result_parameters_flat_Mtot_converted, xlabel='Mtot', ylabel='ratio_conj'))
for curr_points in all_points:
for curr_data in all_datas:
for R_i, R in enumerate(R_space):
currR_indices = curr_points['data'][:, 2] == R
plot_interp(curr_points['data'], curr_data['data'], currR_indices, title=curr_data['name'], points_label=curr_points['name'], xlabel=curr_points['xlabel'], ylabel=curr_points['ylabel'])
else:
# Do one pcolor for M and ratio per R
for R_i, R in enumerate(R_space):
# Check evolution of precision given M and ratio
utils.pcolor_2d_data(result_all_precisions_mean[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='precision, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_precision_R%d_log_{label}_{unique_id}.pdf' % R)
# Show kappa
try:
utils.pcolor_2d_data(result_em_fits_kappa_valid[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='kappa, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_kappa_R%d_log_{label}_{unique_id}.pdf' % R)
except ValueError:
pass
# Show probability on target
utils.pcolor_2d_data(result_em_fits_target[..., R_i], log_scale=False, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='target, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_target_R%d_{label}_{unique_id}.pdf' % R)
# # Show Fisher info
utils.pcolor_2d_data(result_fisherinfo_mean[..., R_i], log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='fisher info, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_fisherinfo_R%d_log_{label}_{unique_id}.pdf' % R)
plt.close('all')
if plots_effect_M_target_kappa:
def plot_ratio_target_kappa(ratio_target_kappa_given_M, target_kappa, R):
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 kappa %d, R=%d' % (target_kappa, R))
if savefigs:
dataio.save_current_figure('optratio_M_targetkappa%d_R%d_{label}_{unique_id}.pdf' % (target_kappa, R))
target_kappas = np.array([100, 200, 300, 500, 1000, 3000])
for R_i, R in enumerate(R_space):
for target_kappa in target_kappas:
dist_to_target_kappa = (result_em_fits_kappa[..., R_i] - 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, R)
plt.close('all')
if plots_kappa_fi_comparison:
# result_em_fits_kappa and fisher info
if True:
for R_i, R in enumerate(R_space):
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, 1)
axes[0].plot(ratio_space, result_em_fits_kappa[2*M_tot_selected_i, ..., R_i])
axes[0].set_xlabel('ratio')
axes[0].set_title('Fitted kappa')
axes[1].plot(ratio_space, utils.stddev_to_kappa(1./result_fisherinfo_mean[2*M_tot_selected_i, ..., R_i]**0.5))
axes[1].set_xlabel('ratio')
axes[1].set_title('kappa_FI')
f.suptitle('M_tot %d' % M_tot_selected, fontsize=15)
f.set_tight_layout(True)
if savefigs:
dataio.save_current_figure('comparison_kappa_fisher_R%d_M%d_{label}_{unique_id}.pdf' % (R, M_tot_selected))
plt.close(f)
if plots_multiple_fisherinfo:
target_fisherinfos = np.array([100, 200, 300, 500, 1000])
for R_i, R in enumerate(R_space):
for target_fisherinfo in target_fisherinfos:
dist_to_target_fisherinfo = (result_fisherinfo_mean[..., R_i] - target_fisherinfo)**2.
utils.pcolor_2d_data(dist_to_target_fisherinfo, log_scale=True, x=M_space, y=ratio_space, xlabel='M', ylabel='ratio', xlabel_format="%d", title='Fisher info, R=%d' % R)
if savefigs:
dataio.save_current_figure('pcolor_distfi%d_R%d_log_{label}_{unique_id}.pdf' % (target_fisherinfo, R))
plt.close('all')
if specific_plot_effect_R:
M_target = 356
if convert_M_realsizes:
M_tot_target = M_target
delta_around_target = 30
filter_points_totM_indices = np.abs(result_parameters_flat_Mtot_converted[:, 0] - M_tot_target) < delta_around_target
# first check landscape
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_totM_indices][..., 1:], result_em_fits_kappa_flat[filter_points_totM_indices], xlabel='ratio_conj', ylabel='R', title='kappa, M_target=%d +- %d' % (M_tot_target, delta_around_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_pcolortrueMtot_kappa_M%d_log_%s_{label}_{unique_id}.pdf' % (M_tot_target, interpolation_method))
# Then plot distance to specific kappa
# target_kappa = 1.2e3
target_kappa = 580
dist_target_kappa_flat = np.abs(result_em_fits_kappa_flat - target_kappa)
mask_greater_than = 5e3
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_totM_indices][..., 1:], dist_target_kappa_flat[filter_points_totM_indices], xlabel='ratio_conj', ylabel='R', title='dist kappa %d, M_target=%d +- %d' % (target_kappa, M_tot_target, delta_around_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_greater_than=mask_greater_than, mask_smaller_than=0)
if savefigs:
dataio.save_current_figure('specific_pcolortrueMtot_distkappa%d_M%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, M_tot_target, interpolation_method))
else:
# Choose a M, find which ratio gives best fit to a given kappa
M_target_i = np.argmin(np.abs(M_space - M_target))
utils.pcolor_2d_data(result_em_fits_kappa[M_target_i], log_scale=True, x=ratio_space, y=R_space, xlabel='ratio', ylabel='R', ylabel_format="%d", title='Kappa, M %d' % (M_target))
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_Reffect_pcolor_kappa_M%dT%d_log_{label}_{unique_id}.pdf' % (M_target, T))
# target_kappa = np.ma.mean(result_em_fits_kappa_valid[M_target_i])
# target_kappa = 5*1e3
# target_kappa = 1.2e3
target_kappa = 580
# dist_target_kappa = np.abs(result_em_fits_kappa_valid[M_target_i] - target_kappa)
dist_target_kappa = result_em_fits_kappa[M_target_i]/target_kappa
dist_target_kappa[dist_target_kappa > 2.0] = 2.0
# dist_target_kappa[dist_target_kappa < 0.5] = 0.5
utils.pcolor_2d_data(dist_target_kappa, log_scale=False, x=ratio_space, y=R_space, xlabel='ratio', ylabel='R', ylabel_format="%d", title='Kappa dist %.2f, M %d' % (target_kappa, M_target), cmap='RdBu_r')
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_Reffect_pcolor_distkappa%d_M%dT%d_log_{label}_{unique_id}.pdf' % (target_kappa, M_target, T))
# Plot the probability of being on-target
utils.pcolor_2d_data(result_em_fits_target[M_target_i], log_scale=False, x=ratio_space, y=R_space, xlabel='ratio', ylabel='R', ylabel_format="%d", title='target mixture proportion, M %d' % (M_target), vmin=0.0, vmax=1.0) #cmap='RdBu_r'
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_Reffect_pcolor_target_M%dT%d_{label}_{unique_id}.pdf' % (M_target, T))
if specific_plot_effect_ratio_M:
# try to do the same plots as in ratio_scaling_M but with the current data
R_target = 2
interpolation_method = 'cubic'
mask_greater_than = 1e3
if convert_M_realsizes:
# filter_points_targetR_indices = np.abs(result_parameters_flat_Mtot_converted[:, -1] - R_target) == 0
filter_points_targetR_indices = np.abs(result_parameters_flat_subM_converted[:, -1] - R_target) == 0
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., :-1], result_em_fits_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False)
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_subM_converted[filter_points_targetR_indices][..., :-1], result_em_fits_kappa_flat[filter_points_targetR_indices], xlabel='M_conj', ylabel='M_feat', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsubM_kappa_R%d_log_%s_{label}_{unique_id}.pdf' % (R_target, interpolation_method))
# Then plot distance to specific kappa
target_kappa = 580
# target_kappa = 2000
dist_target_kappa_flat = np.abs(result_em_fits_kappa_flat - target_kappa)
dist_target_kappa_flat = result_em_fits_kappa_flat/target_kappa
dist_target_kappa_flat[dist_target_kappa_flat > 1.45] = 1.45
dist_target_kappa_flat[dist_target_kappa_flat < 0.5] = 0.5
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_smaller_than=0, show_slider=False, mask_greater_than =mask_greater_than)
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_smaller_than=0, show_slider=False, mask_greater_than =mask_greater_than)
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_subM_converted[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M_conj', ylabel='M_feat', title='Kappa dist %.2f, R=%d' % (target_kappa, R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_greater_than=mask_greater_than, mask_smaller_than=0, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsubM_distkappa%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, R_target, interpolation_method))
## Scatter plot, works better...
f, ax = plt.subplots()
ss = ax.scatter(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], 500, c=(dist_target_kappa_flat[filter_points_targetR_indices]),
norm=matplotlib.colors.LogNorm())
plt.colorbar(ss)
ax.set_xlabel('M')
ax.set_ylabel('ratio')
ax.set_xlim((result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0].min()*0.8, result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0].max()*1.03))
ax.set_ylim((-0.05, result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1].max()*1.05))
ax.set_title('Kappa dist %.2f, R=%d' % (target_kappa, R_target))
if savefigs:
dataio.save_current_figure('specific_ratioM_scattertotM_distkappa%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, R_target, interpolation_method))
## Spline interpolation
distkappa_spline_int_params = spint.bisplrep(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], np.log(dist_target_kappa_flat[filter_points_targetR_indices]), kx=3, ky=3, s=1)
if True:
M_interp_space = np.linspace(100, 740, 100)
ratio_interp_space = np.linspace(0.0, 1.0, 100)
# utils.pcolor_2d_data(spint.bisplev(M_interp_space, ratio_interp_space, distkappa_spline_int_params), y=ratio_interp_space, x=M_interp_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target), ticks_interpolate=11)
utils.pcolor_2d_data(np.exp(spint.bisplev(M_interp_space, ratio_interp_space, distkappa_spline_int_params)), y=ratio_interp_space, x=M_interp_space, ylabel='ratio conjunctivity', xlabel='M', xlabel_format="%d", title='Ratio kappa/%.2f, R %d' % (target_kappa, R_target), ticks_interpolate=11, log_scale=False, cmap='RdBu_r')
# plt.scatter(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], marker='o', c='b', s=5)
# plt.scatter(np.argmin(np.abs(M_interp_space[:, np.newaxis] - result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0]), axis=0), np.argmin(np.abs(ratio_interp_space[:, np.newaxis] - result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1]), axis=0), marker='o', c='b', s=5)
else:
M_interp_space = M_space
ratio_interp_space = ratio_space
utils.pcolor_2d_data(spint.bisplev(M_interp_space, ratio_interp_space, distkappa_spline_int_params), y=ratio_interp_space, x=M_interp_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target))
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsplinetotM_distkappa%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_kappa, R_target, interpolation_method))
### Distance to Fisher Info
target_fi = 2*target_kappa
dist_target_fi_flat = np.abs(result_fisherinfor_mean_flat - target_fi)
dist_target_fi_flat = result_fisherinfor_mean_flat/target_fi
dist_target_fi_flat[dist_target_fi_flat > 1.45] = 1.45
dist_target_fi_flat[dist_target_fi_flat < 0.5] = 0.5
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., :-1], dist_target_kappa_flat[filter_points_targetR_indices], xlabel='M', ylabel='ratio_conj', title='kappa, R=%d' % (R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False)
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat_subM_converted[filter_points_targetR_indices][..., :-1], dist_target_fi_flat[filter_points_targetR_indices], xlabel='M_conj', ylabel='M_feat', title='FI dist %.2f, R=%d' % (target_fi, R_target), interpolation_numpoints=200, interpolation_method=interpolation_method, log_scale=False, mask_greater_than=mask_greater_than, show_slider=False)
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsubM_distfi%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_fi, R_target, interpolation_method))
distFI_spline_int_params = spint.bisplrep(result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 0], result_parameters_flat_Mtot_converted[filter_points_targetR_indices][..., 1], np.log(dist_target_fi_flat[filter_points_targetR_indices]), kx=3, ky=3, s=1)
M_interp_space = np.linspace(100, 740, 100)
ratio_interp_space = np.linspace(0.0, 1.0, 100)
# utils.pcolor_2d_data(spint.bisplev(M_interp_space, ratio_interp_space, spline_int_params), y=ratio_interp_space, x=M_interp_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target), ticks_interpolate=11)
utils.pcolor_2d_data(np.exp(spint.bisplev(M_interp_space, ratio_interp_space, distFI_spline_int_params)), y=ratio_interp_space, x=M_interp_space, ylabel='ratio conjunctivity', xlabel='M', xlabel_format="%d", title='Ratio FI/%.2f, R %d' % (target_fi, R_target), ticks_interpolate=11, log_scale=False, cmap='RdBu_r')
plt.gcf().set_tight_layout(True)
plt.gcf().canvas.draw()
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolorsplinetotM_distfi%d_R%d_log_%s_{label}_{unique_id}.pdf' % (target_fi, R_target, interpolation_method))
else:
R_target_i = np.argmin(np.abs(R_space - R_target))
utils.pcolor_2d_data(result_em_fits_kappa_valid[..., R_target_i], log_scale=True, y=ratio_space, x=M_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa, R %d' % (R_target))
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolor_kappa_R%d_log_{label}_{unique_id}.pdf' % (R_target))
# target_kappa = np.ma.mean(result_em_fits_kappa_valid[R_target_i])
# target_kappa = 5*1e3
target_kappa = 580
dist_target_kappa = np.ma.masked_greater(np.abs(result_em_fits_kappa_valid[..., R_target_i] - target_kappa), mask_greater_than*5)
utils.pcolor_2d_data(dist_target_kappa, log_scale=True, y=ratio_space, x=M_space, ylabel='ratio', xlabel='M', xlabel_format="%d", title='Kappa dist %.2f, R %d' % (target_kappa, R_target))
if savefigs:
dataio.save_current_figure('specific_ratioM_pcolor_distkappa%d_R%d_log_{label}_{unique_id}.pdf' % (target_kappa, R_target))
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='higher_dimensions_R')
plt.show()
return locals()
def plots_specific_stimuli_hierarchical(data_pbs, generator_module=None):
'''
Reload and plot behaviour of mixed population code on specific Stimuli
of 3 items.
'''
#### SETUP
#
savefigs = True
savedata = True
plot_per_min_dist_all = True
specific_plots_paper = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
nb_repetitions = np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']).shape[-1]
enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
MMlower_valid_space = data_pbs.loaded_data['datasets_list'][0]['MMlower_valid_space']
ratio_space = MMlower_valid_space[:, 0]/float(np.sum(MMlower_valid_space[0]))
print enforce_min_distance_space
print sigmax_space
print MMlower_valid_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
if plot_per_min_dist_all:
# Do one plot per min distance.
for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
if savefigs:
dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
if savefigs:
dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated model precision
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated Target, nontarget and random mixture components, in multiple subplots
_, axes = plt.subplots(1, 3, figsize=(18, 6))
plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)
if savefigs:
dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot Log-likelihood of Mixture model, sanity check
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
if specific_plots_paper:
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.09
target_mindist_medium = 0.36
target_mindist_high = 1.5
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
## Do for each distance
# for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
for min_dist_i in xrange(enforce_min_distance_space.size):
# Plot precision
utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa fitted
utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa-stddev fitted. Easier to visualize
utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot LLH
utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot mixture parameters
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
plt.ylim([0.0, 1.1])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot mixture parameters, SEM
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
plt.ylim([0.0, 1.1])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['result_all_precisions_mean', 'result_em_fits_mean', 'result_all_precisions_std', 'result_em_fits_std', 'result_em_kappastddev_mean', 'result_em_kappastddev_std', 'enforce_min_distance_space', 'sigmax_space', 'ratio_space', 'all_args']
if savedata:
dataio.save_variables(variables_to_save, locals())
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')
plt.show()
return locals()
def plots_fit_mixturemodels_random(data_pbs, generator_module=None):
'''
Reload runs from PBS
'''
#### SETUP
#
savefigs = True
savedata = True
savemovies = True
plots_dist_bays09 = True
plots_per_T = True
plots_interpolate = False
# 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_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_parameters_flat = np.array(data_pbs.dict_arrays['result_em_fits']['parameters_flat'])
sigmaoutput_space = data_pbs.loaded_data['parameters_uniques']['sigma_output']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
ratio_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
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_dist_bays09_kappa_T1_avg = utils.nanmean(result_dist_bays09_flat[:, 0, 0], axis=-1)
# result_dist_bays09_kappa_allT_avg = np.nansum(utils.nanmean(result_dist_bays09_flat[:, :, 0], axis=-1), axis=1)
# Square distance to kappa
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)
if plots_dist_bays09:
nb_best_points = 30
size_normal_points = 8
size_best_points = 50
def plot_scatter(all_vars, result_dist_to_use_name, title='', log_color=True, downsampling=1, label_file=''):
fig = plt.figure()
ax = Axes3D(fig)
result_dist_to_use = all_vars[result_dist_to_use_name]
if not log_color:
result_dist_to_use = np.exp(result_dist_to_use)
utils.scatter3d(result_parameters_flat[:, 0], result_parameters_flat[:, 1], result_parameters_flat[:, 2], s=size_normal_points, c=np.log(result_dist_to_use), xlabel=parameter_names_sorted[0], ylabel=parameter_names_sorted[1], zlabel=parameter_names_sorted[2], title=title, ax_handle=ax)
best_points_result_dist_to_use = np.argsort(result_dist_to_use)[:nb_best_points]
utils.scatter3d(result_parameters_flat[best_points_result_dist_to_use, 0], result_parameters_flat[best_points_result_dist_to_use, 1], result_parameters_flat[best_points_result_dist_to_use, 2], c='r', s=size_best_points, ax_handle=ax)
print "Best points, %s:" % title
print '\n'.join(['sigma output %.2f, ratio %.2f, sigmax %.2f: %f' % (result_parameters_flat[i, 0], result_parameters_flat[i, 1], result_parameters_flat[i, 2], result_dist_to_use[i]) for i in best_points_result_dist_to_use])
if savefigs:
dataio.save_current_figure('scatter3d_%s%s_{label}_{unique_id}.pdf' % (result_dist_to_use_name, label_file))
if savemovies:
try:
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s%s_{label}_{unique_id}.mp4' % (result_dist_to_use_name, label_file)), bitrate=8000, min_duration=8)
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s%s_{label}_{unique_id}.gif' % (result_dist_to_use_name, label_file)), nb_frames=30, min_duration=8)
except Exception:
# Most likely wrong aggregator...
print "failed when creating movies for ", result_dist_to_use_name
ax.view_init(azim=90, elev=10)
dataio.save_current_figure('scatter3d_view2_%s%s_{label}_{unique_id}.pdf' % (result_dist_to_use_name, label_file))
return ax
# Distance for kappa, all T
plot_scatter(locals(), 'result_dist_bays09_kappa_sum', 'kappa all T')
# Distance for em fits, all T, Squared distance
plot_scatter(locals(), 'result_dist_bays09_emmixt_sum', 'em fits, all T')
# Distance for em fits, all T, KL distance
plot_scatter(locals(), 'result_dist_bays09_emmixt_KL_sum', 'em fits, all T, KL')
# Distance for sum of normalised em fits + normalised kappa, all T
plot_scatter(locals(), 'result_dist_bays09_both_normalised', 'summed normalised em mixt + kappa')
# Distance kappa T = 1
plot_scatter(locals(), 'result_dist_bays09_kappa_T1_sum', 'Kappa T=1')
# Distance kappa T = 2...5
plot_scatter(locals(), 'result_dist_bays09_kappa_T25_sum', 'Kappa T=2/5')
# Distance em fits T = 1
plot_scatter(locals(), 'result_dist_bays09_emmixt_T1_sum', 'em fits T=1')
# Distance em fits T = 2...5
plot_scatter(locals(), 'result_dist_bays09_emmixt_T25_sum', 'em fits T=2/5')
# Distance em fits T = 1, KL
plot_scatter(locals(), 'result_dist_bays09_emmixt_KL_T1_sum', 'em fits T=1, KL')
# Distance em fits T = 2...5, KL
plot_scatter(locals(), 'result_dist_bays09_emmixt_KL_T25_sum', 'em fits T=2/5, KL')
if plots_per_T:
for T_i, T in enumerate(T_space):
# Kappa per T, fit to Bays09
result_dist_bays09_kappa_currT = result_dist_bays09_allT_avg[:, T_i, 0]
result_dist_bays09_kappa_currT_masked = mask_outliers(result_dist_bays09_kappa_currT)
plot_scatter(locals(), 'result_dist_bays09_kappa_currT_masked', 'kappa T %d masked' % T, label_file="T{}".format(T))
# EM Mixt per T, fit to Bays09
result_dist_bays09_emmixt_sum_currT = np.nansum(result_dist_bays09_allT_avg[:, T_i, 1:], axis=-1)
result_dist_bays09_emmixt_sum_currT_masked = mask_outliers(result_dist_bays09_emmixt_sum_currT)
plot_scatter(locals(), 'result_dist_bays09_emmixt_sum_currT_masked', 'EM mixt T %d masked' % T, label_file="T{}".format(T))
# EM Mixt per T, fit to Bays09 KL divergence
result_dist_bays09_emmixt_KL_sum_currT = result_dist_bays09_emmixt_KL[:, T_i]
plot_scatter(locals(), 'result_dist_bays09_emmixt_KL_sum_currT', 'KL EM mixt T %d masked' % T, label_file="T{}".format(T))
# # 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='output_noise')
plt.show()
return locals()
def plots_specific_stimuli_mixed(data_pbs, generator_module=None):
'''
Reload and plot behaviour of mixed population code on specific Stimuli
of 3 items.
'''
#### SETUP
#
savefigs = True
savedata = True
plot_per_min_dist_all = False
specific_plots_paper = False
plots_emfit_allitems = False
plot_min_distance_effect = True
compute_bootstraps = False
should_fit_allitems_model = True
# caching_emfit_filename = None
mixturemodel_to_use = 'allitems_uniquekappa'
# caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfitallitems_uniquekappa.pickle')
# mixturemodel_to_use = 'allitems_fikappa'
caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfit%s.pickle' % mixturemodel_to_use)
compute_fisher_info_perratioconj = True
caching_fisherinfo_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_fisherinfo.pickle')
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
all_args = data_pbs.loaded_data['args_list']
result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results'])
result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])
nb_repetitions = result_responses_all.shape[-1]
K = result_nontargets_all.shape[-2]
N = result_responses_all.shape[-2]
enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
ratio_space = data_pbs.loaded_data['datasets_list'][0]['ratio_space']
print enforce_min_distance_space
print sigmax_space
print ratio_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape
print result_responses_all.shape
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
# Reload cached emfitallitems
if caching_emfit_filename is not None:
if os.path.exists(caching_emfit_filename):
# Got file, open it and try to use its contents
try:
with open(caching_emfit_filename, 'r') as file_in:
# Load and assign values
print "Reloader EM fits from cache", caching_emfit_filename
cached_data = pickle.load(file_in)
result_emfitallitems = cached_data['result_emfitallitems']
mixturemodel_used = cached_data.get('mixturemodel_used', '')
if mixturemodel_used != mixturemodel_to_use:
print "warning, reloaded model used a different mixture model class"
should_fit_allitems_model = False
except IOError:
print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits"
# Load the Fisher Info from cache if exists. If not, compute it.
if caching_fisherinfo_filename is not None:
if os.path.exists(caching_fisherinfo_filename):
# Got file, open it and try to use its contents
try:
with open(caching_fisherinfo_filename, 'r') as file_in:
# Load and assign values
cached_data = pickle.load(file_in)
result_fisherinfo_mindist_sigmax_ratio = cached_data['result_fisherinfo_mindist_sigmax_ratio']
compute_fisher_info_perratioconj = False
except IOError:
print "Error while loading ", caching_fisherinfo_filename, "falling back to computing the Fisher Info"
if compute_fisher_info_perratioconj:
# We did not save the Fisher info, but need it if we want to fit the mixture model with fixed kappa. So recompute them using the args_dicts
result_fisherinfo_mindist_sigmax_ratio = np.empty((enforce_min_distance_space.size, sigmax_space.size, ratio_space.size))
# Invert the all_args_i -> min_dist, sigmax indexing
parameters_indirections = data_pbs.loaded_data['parameters_dataset_index']
# min_dist_i, sigmax_level_i, ratio_i
for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
for sigmax_i, sigmax in enumerate(sigmax_space):
# Get index of first dataset with the current (min_dist, sigmax) (no need for the others, I think)
arg_index = parameters_indirections[(min_dist, sigmax)][0]
# Now using this dataset, reconstruct a RandomFactorialNetwork and compute the fisher info
curr_args = all_args[arg_index]
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# Update param
curr_args['ratio_conj'] = ratio_conj
# curr_args['stimuli_generation'] = 'specific_stimuli'
(_, _, _, sampler) = launchers.init_everything(curr_args)
# Theo Fisher info
result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_i, ratio_conj_i] = sampler.estimate_fisher_info_theocov()
print "Min dist: %.2f, Sigmax: %.2f, Ratio: %.2f: %.3f" % (min_dist, sigmax, ratio_conj, result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_i, ratio_conj_i])
# Save everything to a file, for faster later plotting
if caching_fisherinfo_filename is not None:
try:
with open(caching_fisherinfo_filename, 'w') as filecache_out:
data_cache = dict(result_fisherinfo_mindist_sigmax_ratio=result_fisherinfo_mindist_sigmax_ratio)
pickle.dump(data_cache, filecache_out, protocol=2)
except IOError:
print "Error writing out to caching file ", caching_fisherinfo_filename
if plot_per_min_dist_all:
# Do one plot per min distance.
for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
if savefigs:
dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
if savefigs:
dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated model precision
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated Target, nontarget and random mixture components, in multiple subplots
_, axes = plt.subplots(1, 3, figsize=(18, 6))
plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)
if savefigs:
dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot Log-likelihood of Mixture model, sanity check
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
if specific_plots_paper:
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.15
target_mindist_medium = 0.36
target_mindist_high = 1.5
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
## Do for each distance
# for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
for min_dist_i in xrange(enforce_min_distance_space.size):
# Plot precision
if False:
utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])])
if savefigs:
dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa fitted
ax_handle = utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
# Add distance between items in kappa units
dist_items_kappa = utils.stddev_to_kappa(enforce_min_distance_space[min_dist_i])
ax_handle.plot(ratio_space, dist_items_kappa*np.ones(ratio_space.size), 'k--', linewidth=3)
plt.ylim([-0.1, np.max((np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]), 1.1*dist_items_kappa))])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa-stddev fitted. Easier to visualize
ax_handle = utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
# Add distance between items in std dev units
dist_items_std = (enforce_min_distance_space[min_dist_i])
ax_handle.plot(ratio_space, dist_items_std*np.ones(ratio_space.size), 'k--', linewidth=3)
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
plt.ylim([0, 1.1*np.max((np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i]), dist_items_std))])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
if False:
# Plot LLH
utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot mixture parameters, std
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
plt.ylim([0.0, 1.1])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Mixture parameters, SEM
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
plt.ylim([0.0, 1.1])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
if plots_emfit_allitems:
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.15
target_mindist_medium = 0.36
target_mindist_high = 1.5
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i])
if should_fit_allitems_model:
# kappa, mixt_target, mixt_nontargets (K), mixt_random, LL, bic
# result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan
result_emfitallitems = np.empty((enforce_min_distance_space.size, ratio_space.size, K+5))*np.nan
## Do for each distance
# for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
for min_dist_i in xrange(enforce_min_distance_space.size):
# Fit the mixture model
for ratio_i, ratio in enumerate(ratio_space):
print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i]
if mixturemodel_to_use == 'allitems_uniquekappa':
em_fit = em_circularmixture_allitems_uniquekappa.fit(
result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)))
elif mixturemodel_to_use == 'allitems_fikappa':
em_fit = em_circularmixture_allitems_kappafi.fit(result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)),
kappa=result_fisherinfo_mindist_sigmax_ratio[min_dist_i, sigmax_level_i, ratio_i])
else:
raise ValueError("Wrong mixturemodel_to_use, %s" % mixturemodel_to_use)
result_emfitallitems[min_dist_i, ratio_i] = [em_fit['kappa'], em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]
# Save everything to a file, for faster later plotting
if caching_emfit_filename is not None:
try:
with open(caching_emfit_filename, 'w') as filecache_out:
data_em = dict(result_emfitallitems=result_emfitallitems, target_sigmax=target_sigmax)
pickle.dump(data_em, filecache_out, protocol=2)
except IOError:
print "Error writing out to caching file ", caching_emfit_filename
## Plots now, for each distance!
# for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
for min_dist_i in xrange(enforce_min_distance_space.size):
# Plot now
_, ax = plt.subplots()
ax.plot(ratio_space, result_emfitallitems[min_dist_i, :, 1:5], linewidth=3)
plt.ylim([0.0, 1.1])
plt.legend(['Target', 'Nontarget 1', 'Nontarget 2', 'Random'], loc='upper left')
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
if plot_min_distance_effect:
conj_receptive_field_size = 2.*np.pi/((all_args[0]['M']*ratio_space)**0.5)
target_vs_nontargets_mindist_ratio = result_emfitallitems[..., 1]/np.sum(result_emfitallitems[..., 1:4], axis=-1)
nontargetsmean_vs_targnontarg_mindist_ratio = np.mean(result_emfitallitems[..., 2:4]/np.sum(result_emfitallitems[..., 1:4], axis=-1)[..., np.newaxis], axis=-1)
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# Do one plot per ratio, putting the receptive field size on each
f, ax = plt.subplots()
ax.plot(enforce_min_distance_space[1:], target_vs_nontargets_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='target mixture')
ax.plot(enforce_min_distance_space[1:], nontargetsmean_vs_targnontarg_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='non-target mixture')
# ax.plot(enforce_min_distance_space[1:], result_emfitallitems[1:, ratio_conj_i, 1:5], linewidth=3)
ax.axvline(x=conj_receptive_field_size[ratio_conj_i]/2., color='k', linestyle='--', linewidth=2)
ax.axvline(x=conj_receptive_field_size[ratio_conj_i]*2., color='r', linestyle='--', linewidth=2)
plt.legend(loc='upper left')
plt.grid()
# ax.set_xlabel('Stimuli separation')
# ax.set_ylabel('Ratio Target to Non-targets')
plt.axis('tight')
ax.set_ylim([0.0, 1.0])
ax.set_xlim([enforce_min_distance_space[1:].min(), enforce_min_distance_space[1:].max()])
if savefigs:
dataio.save_current_figure('ratio%.2f_mindistpred_ratiotargetnontarget_{label}_{unique_id}.pdf' % ratio_conj)
if compute_bootstraps:
## Bootstrap evaluation
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.15
target_mindist_medium = 0.5
target_mindist_high = 1.
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
# cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap.pickle')
cache_bootstrap_fn = '/Users/loicmatthey/Dropbox/UCL/1-phd/Work/Visual_working_memory/code/git-bayesian-visual-working-memory/Experiments/specific_stimuli/specific_stimuli_corrected_mixed_sigmaxmindistance_autoset_repetitions5mult_collectall_281113_outputs/cache_bootstrap.pickle'
try:
with open(cache_bootstrap_fn, 'r') as file_in:
# Load and assign values
cached_data = pickle.load(file_in)
bootstrap_ecdf_bays_sigmax_T = cached_data['bootstrap_ecdf_bays_sigmax_T']
bootstrap_ecdf_allitems_sum_sigmax_T = cached_data['bootstrap_ecdf_allitems_sum_sigmax_T']
bootstrap_ecdf_allitems_all_sigmax_T = cached_data['bootstrap_ecdf_allitems_all_sigmax_T']
should_fit_bootstrap = False
except IOError:
print "Error while loading ", cache_bootstrap_fn
ratio_i = 0
# bootstrap_allitems_nontargets_allitems_uniquekappa = em_circularmixture_allitems_uniquekappa.bootstrap_nontarget_stat(
# result_responses_all[min_dist_level_low_i, sigmax_level_i, ratio_i].flatten(),
# result_target_all[min_dist_level_low_i, sigmax_level_i, ratio_i].flatten(),
# result_nontargets_all[min_dist_level_low_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)),
# sumnontargets_bootstrap_ecdf=bootstrap_ecdf_allitems_sum_sigmax_T[sigmax_level_i][K]['ecdf'],
# allnontargets_bootstrap_ecdf=bootstrap_ecdf_allitems_all_sigmax_T[sigmax_level_i][K]['ecdf']
# TODO FINISH HERE
variables_to_save = ['nb_repetitions']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')
plt.show()
return locals()
def plots_fitting_experiments_random(data_pbs, generator_module=None):
'''
Reload 2D volume runs from PBS and plot them
'''
#### SETUP
#
savefigs = True
savedata = True
savemovies = True
plots_per_T = True
scatter3d_all_T = True
# do_relaunch_bestparams_pbs = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
# parameters: ratio_conj, sigmax, T
# Extract data
result_fitexperiments_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['results_flat'])
result_fitexperiments_all_flat = np.array(data_pbs.dict_arrays['result_fitexperiments_all']['results_flat'])
result_parameters_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['parameters_flat'])
# Extract order of datasets
experiment_ids = data_pbs.loaded_data['datasets_list'][0]['fitexperiment_parameters']['experiment_ids']
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'])
# Compute some stuff
result_fitexperiments_bic_avg = utils.nanmean(result_fitexperiments_flat[:,0], axis=-1)
T_space = np.unique(result_parameters_flat[:, 2])
if plots_per_T:
for T in T_space:
currT_indices = result_parameters_flat[:, 2] == T
utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat[currT_indices][..., :2], result_fitexperiments_bic_avg[currT_indices], xlabel='Ratio_conj', ylabel='sigma x', title='BIC, T %d' % T, interpolation_numpoints=200, interpolation_method='nearest', log_scale=False)
if savefigs:
dataio.save_current_figure('contourf_fittingexp_bic_random_ratiosigma_T%d_{label}_{unique_id}.pdf' % (T))
if scatter3d_all_T:
nb_best_points_per_T = 2
size_normal_points = 8
size_best_points = 50
# Get the best points for a specific T
def best_points_T(result_dist_to_use, T):
currT_indices = result_parameters_flat[:, 2] == T
best_points_indices_currT = np.argsort(result_dist_to_use[currT_indices])[:nb_best_points_per_T]
# Indirect it all
return np.nonzero(currT_indices)[0][best_points_indices_currT]
def best_points_allT(result_dist_to_use):
return np.array(utils.flatten_list([best_points_T(result_dist_to_use, T) for T in T_space]))
def plot_scatter(all_vars, result_dist_to_use_name, title=''):
fig = plt.figure()
ax = Axes3D(fig)
result_dist_to_use = all_vars[result_dist_to_use_name]
utils.scatter3d(result_parameters_flat[:, 0], result_parameters_flat[:, 1], result_parameters_flat[:, 2], s=size_normal_points, c=np.log(result_dist_to_use), xlabel=parameter_names_sorted[0], ylabel=parameter_names_sorted[1], zlabel=parameter_names_sorted[2], title=title, ax_handle=ax)
best_points_result_dist_to_use = best_points_allT(result_dist_to_use)
utils.scatter3d(result_parameters_flat[best_points_result_dist_to_use, 0], result_parameters_flat[best_points_result_dist_to_use, 1], result_parameters_flat[best_points_result_dist_to_use, 2], c='r', s=size_best_points, ax_handle=ax)
print "Best points, %s:" % title
print '\n'.join(['ratio %.2f, sigmax %.2f, T %d: %f' % (result_parameters_flat[i, 0], result_parameters_flat[i, 1], result_parameters_flat[i, 2], result_dist_to_use[i]) for i in best_points_result_dist_to_use])
if savefigs:
dataio.save_current_figure('scatter3d_%s_{label}_{unique_id}.pdf' % result_dist_to_use_name)
if savemovies:
try:
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_{label}_{unique_id}.mp4' % result_dist_to_use_name), bitrate=8000, min_duration=8)
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_{label}_{unique_id}.gif' % result_dist_to_use_name), nb_frames=30, min_duration=8)
except Exception:
# Most likely wrong aggregator...
print "failed when creating movies for ", result_dist_to_use_name
ax.view_init(azim=90, elev=10)
dataio.save_current_figure('scatter3d_view2_%s_{label}_{unique_id}.pdf' % result_dist_to_use_name)
return ax
plot_scatter(locals(), 'result_fitexperiments_bic_avg', 'BIC all T')
# 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 = ['experiment_ids', 'parameter_names_sorted']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='fitting_experiments')
plt.show()
return locals()
def plots_specific_stimuli_mixed(data_pbs, generator_module=None):
'''
Reload and plot behaviour of mixed population code on specific Stimuli
of 3 items.
'''
#### SETUP
#
savefigs = True
savedata = True
plot_per_min_dist_all = False
specific_plots_paper = False
specific_plots_emfits = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results'])
result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])
nb_repetitions = result_responses_all.shape[-1]
K = result_nontargets_all.shape[-2]
N = result_responses_all.shape[-2]
enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
ratio_space = data_pbs.loaded_data['datasets_list'][0]['ratio_space']
print enforce_min_distance_space
print sigmax_space
print ratio_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape
print result_responses_all.shape
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
if plot_per_min_dist_all:
# Do one plot per min distance.
for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
if savefigs:
dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
if savefigs:
dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated model precision
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated Target, nontarget and random mixture components, in multiple subplots
_, axes = plt.subplots(1, 3, figsize=(18, 6))
plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)
if savefigs:
dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot Log-likelihood of Mixture model, sanity check
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
if specific_plots_paper:
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.15
target_mindist_medium = 0.36
target_mindist_high = 1.5
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
## Do for each distance
for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
# for min_dist_i in xrange(enforce_min_distance_space.size):
# Plot precision
utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])])
if savefigs:
dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa fitted
utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
plt.ylim([-0.1, np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0])])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa-stddev fitted. Easier to visualize
utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
plt.ylim([0, 1.1*np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i])])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot LLH
utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot mixture parameters, std
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
plt.ylim([0.0, 1.1])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Mixture parameters, SEM
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
plt.ylim([0.0, 1.1])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
if specific_plots_emfits:
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.15
target_mindist_medium = 0.36
target_mindist_high = 1.5
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i])
# kappa (K+1), mixt_target, mixt_nontargets (K), mixt_random, LL, bic
result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan
## Do for each distance
for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
# Fit the mixture model
for ratio_i, ratio in enumerate(ratio_space):
print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i]
em_fit = em_circularmixture_allitems.fit(
result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)))
result_emfitallitems[min_dist_plotting_i, ratio_i] = em_fit['kappa'].tolist() + [em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]
# Plot now
_, ax = plt.subplots()
ax.plot(ratio_space, result_emfitallitems[min_dist_plotting_i, :, 3:7])
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['nb_repetitions']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')
plt.show()
return locals()
def plots_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 fit_transform(self, X, verbose=True, report_interval=500, X_true=None):
"""
Imputes missing values using a batched OT loss
Parameters
----------
X : torch.DoubleTensor or torch.cuda.DoubleTensor
Contains non-missing and missing data at the indices given by the
"mask" argument. Missing values can be arbitrarily assigned
(e.g. with NaNs).
mask : torch.DoubleTensor or torch.cuda.DoubleTensor
mask[i,j] == 1 if X[i,j] is missing, else mask[i,j] == 0.
verbose: bool, default=True
If True, output loss to log during iterations.
X_true: torch.DoubleTensor or None, default=None
Ground truth for the missing values. If provided, will output a
validation score during training, and return score arrays.
For validation/debugging only.
Returns
-------
X_filled: torch.DoubleTensor or torch.cuda.DoubleTensor
Imputed missing data (plus unchanged non-missing data).
"""
X = X.clone()
n, d = X.shape
if self.batchsize > n // 2:
e = int(np.log2(n // 2))
self.batchsize = 2**e
if verbose:
logging.info(
f"Batchsize larger that half size = {len(X) // 2}. Setting batchsize to {self.batchsize}."
)
mask = torch.isnan(X).double()
imps = (self.noise * torch.randn(mask.shape).double() +
nanmean(X, 0))[mask.bool()]
imps.requires_grad = True
optimizer = self.opt([imps], lr=self.lr)
if verbose:
logging.info(
f"batchsize = {self.batchsize}, epsilon = {self.eps:.4f}")
if X_true is not None:
maes = np.zeros(self.niter)
rmses = np.zeros(self.niter)
for i in range(self.niter):
X_filled = X.detach().clone()
X_filled[mask.bool()] = imps
loss = 0
for _ in range(self.n_pairs):
idx1 = np.random.choice(n, self.batchsize, replace=False)
idx2 = np.random.choice(n, self.batchsize, replace=False)
X1 = X_filled[idx1]
X2 = X_filled[idx2]
loss = loss + self.sk(X1, X2)
if torch.isnan(loss).any() or torch.isinf(loss).any():
### Catch numerical errors/overflows (should not happen)
logging.info("Nan or inf loss")
break
optimizer.zero_grad()
loss.backward()
optimizer.step()
if X_true is not None:
maes[i] = MAE(X_filled, X_true, mask).item()
rmses[i] = RMSE(X_filled, X_true, mask).item()
if verbose and (i % report_interval == 0):
if X_true is not None:
logging.info(
f'Iteration {i}:\t Loss: {loss.item() / self.n_pairs:.4f}\t '
f'Validation MAE: {maes[i]:.4f}\t'
f'RMSE: {rmses[i]:.4f}')
else:
logging.info(
f'Iteration {i}:\t Loss: {loss.item() / self.n_pairs:.4f}'
)
X_filled = X.detach().clone()
X_filled[mask.bool()] = imps
if X_true is not None:
return X_filled, maes, rmses
else:
return X_filled
def plots_fitting_experiments_random(data_pbs, generator_module=None):
'''
Reload 2D volume runs from PBS and plot them
'''
#### SETUP
#
savefigs = True
savedata = True
savemovies = False
do_bays09 = True
do_gorgo11 = True
scatter3d_sumT = False
plots_flat_sorted_performance = False
plots_memorycurves_fits_best = True
nb_best_points = 20
nb_best_points_per_T = nb_best_points/6
size_normal_points = 8
size_best_points = 50
downsampling = 2
# do_relaunch_bestparams_pbs = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
# parameters: ratio_conj, sigmax, T
# Extract data
result_fitexperiments_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['results_flat'])
result_fitexperiments_all_flat = np.array(data_pbs.dict_arrays['result_fitexperiments_all']['results_flat'])
result_fitexperiments_noiseconv_flat = np.array(data_pbs.dict_arrays['result_fitexperiments_noiseconv']['results_flat'])
result_fitexperiments_noiseconv_all_flat = np.array(data_pbs.dict_arrays['result_fitexperiments_noiseconv_all']['results_flat'])
result_parameters_flat = np.array(data_pbs.dict_arrays['result_fitexperiments']['parameters_flat'])
all_repeats_completed = data_pbs.dict_arrays['result_fitexperiments']['repeats_completed']
all_args = data_pbs.loaded_data['args_list']
all_args_arr = np.array(all_args)
num_repetitions = generator_module.num_repetitions
# Extract order of datasets
experiment_ids = data_pbs.loaded_data['datasets_list'][0]['fitexperiment_parameters']['experiment_ids']
parameter_names_sorted = data_pbs.dataset_infos['parameters']
T_space = data_pbs.loaded_data['datasets_list'][0]['T_space']
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
# filter_data = (result_parameters_flat[:, -1] < 1.0) & (all_repeats_completed == num_repetitions - 1)
# filter_data = (all_repeats_completed == num_repetitions - 1)
# result_fitexperiments_flat = result_fitexperiments_flat[filter_data]
# result_fitexperiments_all_flat = result_fitexperiments_all_flat[filter_data]
# result_fitexperiments_noiseconv_flat = result_fitexperiments_noiseconv_flat[filter_data]
# result_fitexperiments_noiseconv_all_flat = result_fitexperiments_noiseconv_all_flat[filter_data]
# result_parameters_flat = result_parameters_flat[filter_data]
# Compute some stuff
# Data is summed over all experiments for _flat, contains bic, ll and ll90.
# for _all_flat, contains bic, ll and ll90 per experiment. Given that Gorgo11 and Bays09 are incompatible, shouldn't really use the combined version directly!
result_fitexperiments_noiseconv_bic_avg_allT = utils.nanmean(result_fitexperiments_noiseconv_flat, axis=-1)[..., 0]
result_fitexperiments_noiseconv_allexp_bic_avg_allT = utils.nanmean(result_fitexperiments_noiseconv_all_flat, axis=-1)[:, :, 0]
result_fitexperiments_noiseconv_allexp_ll90_avg_allT = -utils.nanmean(result_fitexperiments_noiseconv_all_flat, axis=-1)[:, :, -1]
### BIC
# result_fitexperiments_noiseconv_allexp_bic_avg_allT: N x T x exp
result_fitexperiments_noiseconv_bays09_bic_avg_allT = result_fitexperiments_noiseconv_allexp_bic_avg_allT[..., 0]
result_fitexperiments_noiseconv_gorgo11_bic_avg_allT = result_fitexperiments_noiseconv_allexp_bic_avg_allT[..., 1]
result_fitexperiments_noiseconv_dualrecall_bic_avg_allT = result_fitexperiments_noiseconv_allexp_bic_avg_allT[..., 2]
# Summed T
result_fitexperiments_noiseconv_bays09_bic_avg_sumT = np.nansum(result_fitexperiments_noiseconv_bays09_bic_avg_allT, axis=-1)
result_fitexperiments_noiseconv_gorgo11_bic_avg_sumT = np.nansum(result_fitexperiments_noiseconv_gorgo11_bic_avg_allT, axis=-1)
result_fitexperiments_noiseconv_dualrecall_bic_avg_sumT = np.nansum(result_fitexperiments_noiseconv_dualrecall_bic_avg_allT, axis=-1)
### LL90
# N x T x exp
result_fitexperiments_noiseconv_bays09_ll90_avg_allT = result_fitexperiments_noiseconv_allexp_ll90_avg_allT[..., 0]
result_fitexperiments_noiseconv_gorgo11_ll90_avg_allT = result_fitexperiments_noiseconv_allexp_ll90_avg_allT[..., 1]
result_fitexperiments_noiseconv_dualrecall_ll90_avg_allT = result_fitexperiments_noiseconv_allexp_ll90_avg_allT[..., 2]
# Summed T
result_fitexperiments_noiseconv_bays09_ll90_avg_sumT = np.nansum(result_fitexperiments_noiseconv_bays09_ll90_avg_allT, axis=-1)
result_fitexperiments_noiseconv_gorgo11_ll90_avg_sumT = np.nansum(result_fitexperiments_noiseconv_gorgo11_ll90_avg_allT, axis=-1)
result_fitexperiments_noiseconv_dualrecall_ll90_avg_sumT = np.nansum(result_fitexperiments_noiseconv_dualrecall_ll90_avg_allT, axis=-1)
def mask_outliers_array(result_dist_to_use, sigma_outlier=3):
'''
Mask outlier datapoints.
Compute the mean of the results and assume that points with:
result > mean + sigma_outlier*std
are outliers.
As we want the minimum values, do not mask small values
'''
return np.ma.masked_greater(result_dist_to_use, np.mean(result_dist_to_use) + sigma_outlier*np.std(result_dist_to_use))
def best_points_allT(result_dist_to_use):
'''
Best points for all T
'''
return np.argsort(result_dist_to_use)[:nb_best_points]
def str_best_params(best_i, result_dist_to_use):
return ' '.join(["%s %.4f" % (parameter_names_sorted[param_i], result_parameters_flat[best_i, param_i]) for param_i in xrange(len(parameter_names_sorted))]) + ' >> %f' % result_dist_to_use[best_i]
def plot_scatter(all_vars, result_dist_to_use_name, title='', log_color=True, downsampling=1, label_file='', mask_outliers=True):
result_dist_to_use = all_vars[result_dist_to_use_name]
result_parameters_flat = all_vars['result_parameters_flat']
# Filter if downsampling
filter_downsampling = np.arange(0, result_dist_to_use.size, downsampling)
result_dist_to_use = result_dist_to_use[filter_downsampling]
result_parameters_flat = result_parameters_flat[filter_downsampling]
if mask_outliers:
result_dist_to_use = mask_outliers_array(result_dist_to_use)
best_points_result_dist_to_use = np.argsort(result_dist_to_use)[:nb_best_points]
# Construct all permutations of 3 parameters, for 3D scatters
params_permutations = set([tuple(np.sort(np.random.choice(result_parameters_flat.shape[-1], 3, replace=False)).tolist()) for i in xrange(1000)])
for param_permut in params_permutations:
fig = plt.figure()
ax = Axes3D(fig)
# One plot per parameter permutation
if log_color:
color_points = np.log(result_dist_to_use)
else:
color_points = result_dist_to_use
utils.scatter3d(result_parameters_flat[:, param_permut[0]], result_parameters_flat[:, param_permut[1]], result_parameters_flat[:, param_permut[2]], s=size_normal_points, c=color_points, xlabel=parameter_names_sorted[param_permut[0]], ylabel=parameter_names_sorted[param_permut[1]], zlabel=parameter_names_sorted[param_permut[2]], title=title, ax_handle=ax)
utils.scatter3d(result_parameters_flat[best_points_result_dist_to_use, param_permut[0]], result_parameters_flat[best_points_result_dist_to_use, param_permut[1]], result_parameters_flat[best_points_result_dist_to_use, param_permut[2]], c='r', s=size_best_points, ax_handle=ax)
if savefigs:
dataio.save_current_figure('scatter3d_%s_%s%s_{label}_{unique_id}.pdf' % (result_dist_to_use_name, '_'.join([parameter_names_sorted[i] for i in param_permut]), label_file))
if savemovies:
try:
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_%s%s_{label}_{unique_id}.mp4' % (result_dist_to_use_name, '_'.join([parameter_names_sorted[i] for i in param_permut]), label_file)), bitrate=8000, min_duration=8)
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_%s%s_{label}_{unique_id}.gif' % (result_dist_to_use_name, '_'.join([parameter_names_sorted[i] for i in param_permut]), label_file)), nb_frames=30, min_duration=8)
except Exception:
# Most likely wrong aggregator...
print "failed when creating movies for ", result_dist_to_use_name
if False and savefigs:
ax.view_init(azim=90, elev=10)
dataio.save_current_figure('scatter3d_view2_%s_%s%s_{label}_{unique_id}.pdf' % (result_dist_to_use_name, '_'.join([parameter_names_sorted[i] for i in param_permut]), label_file))
# plt.close('all')
print "Parameters: %s" % ', '.join(parameter_names_sorted)
print "Best points, %s:" % title
print '\n'.join([str_best_params(best_i, result_dist_to_use) for best_i in best_points_result_dist_to_use])
if scatter3d_sumT:
plot_scatter(locals(), 'result_fitexperiments_noiseconv_bays09_bic_avg_sumT', 'BIC Bays09')
plot_scatter(locals(), 'result_fitexperiments_noiseconv_bays09_ll90_avg_sumT', 'LL90 Bays09')
plot_scatter(locals(), 'result_fitexperiments_noiseconv_gorgo11_bic_avg_sumT', 'BIC Gorgo11')
plot_scatter(locals(), 'result_fitexperiments_noiseconv_gorgo11_ll90_avg_sumT', 'LL90 Gorgo11')
plot_scatter(locals(), 'result_fitexperiments_noiseconv_dualrecall_bic_avg_sumT', 'BIC Dual recall')
plot_scatter(locals(), 'result_fitexperiments_noiseconv_dualrecall_ll90_avg_sumT', 'LL90 Dual recall')
if plots_flat_sorted_performance:
result_dist_to_try = []
if do_bays09:
result_dist_to_try.extend(['result_fitexperiments_noiseconv_bays09_bic_avg_sumT', 'result_fitexperiments_noiseconv_bays09_ll90_avg_sumT'])
if do_gorgo11:
result_dist_to_try.extend(['result_fitexperiments_noiseconv_gorgo11_bic_avg_sumT', 'result_fitexperiments_noiseconv_gorgo11_ll90_avg_sumT'])
for result_dist in result_dist_to_try:
order_indices = np.argsort(locals()[result_dist])[::-1]
f, axes = plt.subplots(2, 1)
axes[0].plot(np.arange(4) + result_parameters_flat[order_indices]/np.max(result_parameters_flat[order_indices], axis=0))
axes[0].legend(parameter_names_sorted, loc='upper left')
axes[0].set_ylabel('Parameters')
axes[1].plot(locals()[result_dist][order_indices])
axes[1].set_ylabel(result_dist.split('result_dist_')[-1])
axes[0].set_title('Distance ordered ' + result_dist.split('result_dist_')[-1])
f.canvas.draw()
if savefigs:
dataio.save_current_figure('plot_sortedperf_full_%s_{label}_{unique_id}.pdf' % (result_dist))
if plots_memorycurves_fits_best:
# Alright, will actually reload the data from another set of runs, and find the closest parameter set to the ones found here.
data = utils.load_npy('normalisedsigmaxsigmaoutput_random_fitmixturemodels_sigmaxMratiosigmaoutput_repetitions3_280814/outputs/global_plots_fitmixtmodel_random_sigmaoutsigmaxnormMratio-plots_fit_mixturemodels_random-75eb9c74-72e0-4165-8014-92c1ef446f0a.npy')
result_em_fits_flat_fitmixture = data['result_em_fits_flat']
result_parameters_flat_fitmixture = data['result_parameters_flat']
all_args_arr_fitmixture = data['all_args_arr']
data_dir = None
if not os.environ.get('WORKDIR_DROP'):
data_dir = '../experimental_data/'
plotting_parameters = launchers_memorycurves_marginal_fi.load_prepare_datasets()
def plot_memorycurves_fits_fromexternal(all_vars, result_dist_to_use_name, nb_best_points=10):
result_dist_to_use = all_vars[result_dist_to_use_name]
result_em_fits_flat_fitmixture = all_vars['result_em_fits_flat_fitmixture']
result_parameters_flat_fitmixture = all_vars['result_parameters_flat_fitmixture']
all_args_arr_fitmixture = all_vars['all_args_arr_fitmixture']
best_point_indices_result_dist = np.argsort(result_dist_to_use)[:nb_best_points]
for best_point_index in best_point_indices_result_dist:
print "extended plot desired for: " + str_best_params(best_point_index, result_dist_to_use)
dist_best_points_fitmixture = np.abs(result_parameters_flat_fitmixture - result_parameters_flat[best_point_index])
dist_best_points_fitmixture -= np.min(dist_best_points_fitmixture, axis=0)
dist_best_points_fitmixture /= np.max(dist_best_points_fitmixture, axis=0)
best_point_index_fitmixture = np.argmax(np.prod(1-dist_best_points_fitmixture, axis=-1))
print "found closest: " + ' '.join(["%s %.4f" % (parameter_names_sorted[param_i], result_parameters_flat_fitmixture[best_point_index_fitmixture, param_i]) for param_i in xrange(len(parameter_names_sorted))])
# Update arguments
all_args_arr_fitmixture[best_point_index_fitmixture].update(dict(zip(parameter_names_sorted, result_parameters_flat_fitmixture[best_point_index_fitmixture])))
packed_data = dict(T_space=T_space, result_em_fits=result_em_fits_flat_fitmixture[best_point_index_fitmixture], all_parameters=all_args_arr_fitmixture[best_point_index_fitmixture])
plotting_parameters['suptitle'] = result_dist_to_use_name
plotting_parameters['reuse_axes'] = False
if savefigs:
packed_data['dataio'] = dataio
launchers_memorycurves_marginal_fi.do_memory_plots(packed_data, plotting_parameters)
plot_memorycurves_fits_fromexternal(locals(), 'result_external_fitexperiments_noiseconv_bays09_ll90_avg_sumT', nb_best_points=3)
plot_memorycurves_fits_fromexternal(locals(), 'result_external_fitexperiments_noiseconv_gorgo11_ll90_avg_sumT', nb_best_points=3)
plot_memorycurves_fits_fromexternal(locals(), 'result_external_fitexperiments_noiseconv_dualrecall_ll90_avg_sumT', nb_best_points=3)
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['experiment_ids', 'parameter_names_sorted', 'T_space', 'all_args_arr', 'all_repeats_completed', 'filter_data']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='sigmaoutput_normalisedsigmax_random')
plt.show()
return locals()
def plots_memory_curves(data_pbs, generator_module=None):
"""
Reload and plot memory curve of a Mixed code.
Can use Marginal Fisher Information and fitted Mixture Model as well
"""
#### SETUP
#
savefigs = True
savedata = True
do_error_distrib_fits = True
plot_pcolor_fit_precision_to_fisherinfo = True
plot_selected_memory_curves = False
plot_best_memory_curves = False
plot_best_error_distrib = True
colormap = None # or 'cubehelix'
plt.rcParams["font.size"] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = utils.nanmean(data_pbs.dict_arrays["result_all_precisions"]["results"], axis=-1)
result_all_precisions_std = utils.nanstd(data_pbs.dict_arrays["result_all_precisions"]["results"], axis=-1)
result_em_fits_mean = utils.nanmean(data_pbs.dict_arrays["result_em_fits"]["results"], axis=-1)
result_em_fits_std = utils.nanstd(data_pbs.dict_arrays["result_em_fits"]["results"], axis=-1)
result_marginal_inv_fi_mean = utils.nanmean(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1)
result_marginal_inv_fi_std = utils.nanstd(data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1)
result_marginal_fi_mean = utils.nanmean(1.0 / data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1)
result_marginal_fi_std = utils.nanstd(1.0 / data_pbs.dict_arrays["result_marginal_inv_fi"]["results"], axis=-1)
result_responses_all = data_pbs.dict_arrays["result_responses"]["results"]
result_target_all = data_pbs.dict_arrays["result_target"]["results"]
result_nontargets_all = data_pbs.dict_arrays["result_nontargets"]["results"]
M_space = data_pbs.loaded_data["parameters_uniques"]["M"].astype(int)
sigmax_space = data_pbs.loaded_data["parameters_uniques"]["sigmax"]
T_space = data_pbs.loaded_data["datasets_list"][0]["T_space"]
nb_repetitions = result_responses_all.shape[-1]
print M_space
print sigmax_space
print T_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape, result_marginal_inv_fi_mean.shape
dataio = DataIO.DataIO(
output_folder=generator_module.pbs_submission_infos["simul_out_dir"] + "/outputs/",
label="global_" + dataset_infos["save_output_filename"],
)
## Load Experimental data
experim_datadir = os.environ.get("WORKDIR_DROP", os.path.split(load_experimental_data.__file__)[0])
data_simult = load_experimental_data.load_data_simult(
data_dir=os.path.normpath(os.path.join(experim_datadir, "../../experimental_data/")), fit_mixture_model=True
)
gorgo11_experimental_precision = data_simult["precision_nitems_theo"]
gorgo11_experimental_kappa = np.array([data["kappa"] for _, data in data_simult["em_fits_nitems"]["mean"].items()])
gorgo11_experimental_emfits_mean = np.array(
[
[data[key] for _, data in data_simult["em_fits_nitems"]["mean"].items()]
for key in ["kappa", "mixt_target", "mixt_nontargets", "mixt_random"]
]
)
gorgo11_experimental_emfits_std = np.array(
[
[data[key] for _, data in data_simult["em_fits_nitems"]["std"].items()]
for key in ["kappa", "mixt_target", "mixt_nontargets", "mixt_random"]
]
)
gorgo11_experimental_emfits_sem = gorgo11_experimental_emfits_std / np.sqrt(np.unique(data_simult["subject"]).size)
experim_datadir = os.environ.get("WORKDIR_DROP", os.path.split(load_experimental_data.__file__)[0])
data_bays2009 = load_experimental_data.load_data_bays09(
data_dir=os.path.normpath(os.path.join(experim_datadir, "../../experimental_data/")), fit_mixture_model=True
)
bays09_experimental_mixtures_mean = data_bays2009["em_fits_nitems_arrays"]["mean"]
bays09_experimental_mixtures_std = data_bays2009["em_fits_nitems_arrays"]["std"]
# add interpolated points for 3 and 5 items
emfit_mean_intpfct = spint.interp1d(np.unique(data_bays2009["n_items"]), bays09_experimental_mixtures_mean)
bays09_experimental_mixtures_mean_compatible = emfit_mean_intpfct(np.arange(1, 7))
emfit_std_intpfct = spint.interp1d(np.unique(data_bays2009["n_items"]), bays09_experimental_mixtures_std)
bays09_experimental_mixtures_std_compatible = emfit_std_intpfct(np.arange(1, 7))
T_space_bays09 = np.arange(1, 6)
# Boost non-targets
# bays09_experimental_mixtures_mean_compatible[1] *= 1.5
# bays09_experimental_mixtures_mean_compatible[2] /= 1.5
# bays09_experimental_mixtures_mean_compatible /= np.sum(bays09_experimental_mixtures_mean_compatible, axis=0)
# Compute some landscapes of fit!
# dist_diff_precision_margfi = np.sum(np.abs(result_all_precisions_mean*2. - result_marginal_fi_mean[..., 0])**2., axis=-1)
# dist_ratio_precision_margfi = np.sum(np.abs((result_all_precisions_mean*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1)
# dist_diff_emkappa_margfi = np.sum(np.abs(result_em_fits_mean[..., 0]*2. - result_marginal_fi_mean[..., 0])**2., axis=-1)
# dist_ratio_emkappa_margfi = np.sum(np.abs((result_em_fits_mean[..., 0]*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1)
dist_diff_precision_experim = np.sum(
np.abs(result_all_precisions_mean[..., : gorgo11_experimental_kappa.size] - gorgo11_experimental_precision)
** 2.0,
axis=-1,
)
dist_diff_emkappa_experim = np.sum(
np.abs(result_em_fits_mean[..., 0, : gorgo11_experimental_kappa.size] - gorgo11_experimental_kappa) ** 2.0,
axis=-1,
)
dist_diff_em_mixtures_bays09 = np.sum(
np.sum((result_em_fits_mean[..., 1:4] - bays09_experimental_mixtures_mean_compatible[1:].T) ** 2.0, axis=-1),
axis=-1,
)
dist_diff_modelfits_experfits_bays09 = np.sum(
np.sum((result_em_fits_mean[..., :4] - bays09_experimental_mixtures_mean_compatible.T) ** 2.0, axis=-1), axis=-1
)
if do_error_distrib_fits:
print "computing error distribution histograms fits"
# Now try to fit histograms of errors to target/nontargets
bays09_hist_target_mean = data_bays2009["hist_cnts_target_nitems_stats"]["mean"]
bays09_hist_target_std = data_bays2009["hist_cnts_target_nitems_stats"]["std"]
bays09_hist_nontarget_mean = data_bays2009["hist_cnts_nontarget_nitems_stats"]["mean"]
bays09_hist_nontarget_std = data_bays2009["hist_cnts_nontarget_nitems_stats"]["std"]
T_space_bays09_filt = np.unique(data_bays2009["n_items"])
angle_space = np.linspace(-np.pi, np.pi, bays09_hist_target_mean.shape[-1] + 1)
bins_center = angle_space[:-1] + np.diff(angle_space)[0] / 2
errors_targets = utils.wrap_angles(result_responses_all - result_target_all)
hist_targets_all = np.empty(
(M_space.size, sigmax_space.size, T_space_bays09_filt.size, angle_space.size - 1, nb_repetitions)
)
errors_nontargets = np.nan * np.empty(result_nontargets_all.shape)
hist_nontargets_all = np.empty(
(M_space.size, sigmax_space.size, T_space_bays09_filt.size, angle_space.size - 1, nb_repetitions)
)
for M_i, M in enumerate(M_space):
for sigmax_i, sigmax in enumerate(sigmax_space):
for T_bays_i, T_bays in enumerate(T_space_bays09_filt):
for repet_i in xrange(nb_repetitions):
# Could do a nicer indexing but f**k it
# Histogram errors to targets
hist_targets_all[M_i, sigmax_i, T_bays_i, :, repet_i], x, bins = utils.histogram_binspace(
utils.dropnan(errors_targets[M_i, sigmax_i, T_bays - 1, ..., repet_i]),
bins=angle_space,
norm="density",
)
# Compute the error between the responses and nontargets.
errors_nontargets[M_i, sigmax_i, T_bays - 1, :, :, repet_i] = utils.wrap_angles(
(
result_responses_all[M_i, sigmax_i, T_bays - 1, :, repet_i, np.newaxis]
- result_nontargets_all[M_i, sigmax_i, T_bays - 1, :, :, repet_i]
)
)
# Histogram it
hist_nontargets_all[M_i, sigmax_i, T_bays_i, :, repet_i], x, bins = utils.histogram_binspace(
utils.dropnan(errors_nontargets[M_i, sigmax_i, T_bays - 1, ..., repet_i]),
bins=angle_space,
norm="density",
)
hist_targets_mean = utils.nanmean(hist_targets_all, axis=-1).filled(np.nan)
hist_targets_std = utils.nanstd(hist_targets_all, axis=-1).filled(np.nan)
hist_nontargets_mean = utils.nanmean(hist_nontargets_all, axis=-1).filled(np.nan)
hist_nontargets_std = utils.nanstd(hist_nontargets_all, axis=-1).filled(np.nan)
# Compute distances to experimental histograms
dist_diff_hist_target_bays09 = np.nansum(
np.nansum((hist_targets_mean - bays09_hist_target_mean) ** 2.0, axis=-1), axis=-1
)
dist_diff_hist_nontargets_bays09 = np.nansum(
np.nansum((hist_nontargets_mean - bays09_hist_nontarget_mean) ** 2.0, axis=-1), axis=-1
)
dist_diff_hist_nontargets_5_6items_bays09 = np.nansum(
np.nansum((hist_nontargets_mean[:, :, -2:] - bays09_hist_nontarget_mean[-2:]) ** 2.0, axis=-1), axis=-1
)
if plot_pcolor_fit_precision_to_fisherinfo:
# Check fit between precision and Experiments
utils.pcolor_2d_data(
dist_diff_precision_experim,
log_scale=True,
x=M_space,
y=sigmax_space,
xlabel="M",
ylabel="sigmax",
xlabel_format="%d",
)
if savefigs:
dataio.save_current_figure("match_precision_exper_log_pcolor_{label}_{unique_id}.pdf")
utils.pcolor_2d_data(
dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel="M", ylabel="sigmax", xlabel_format="%d"
)
if savefigs:
dataio.save_current_figure("match_emkappa_model_exper_pcolor_{label}_{unique_id}.pdf")
utils.pcolor_2d_data(
dist_diff_em_mixtures_bays09,
x=M_space,
y=sigmax_space,
xlabel="M",
ylabel="sigmax",
log_scale=True,
xlabel_format="%d",
)
if savefigs:
dataio.save_current_figure("match_emmixtures_experbays09_log_pcolor_{label}_{unique_id}.pdf")
utils.pcolor_2d_data(
dist_diff_modelfits_experfits_bays09,
log_scale=True,
x=M_space,
y=sigmax_space,
xlabel="M",
ylabel="sigmax",
xlabel_format="%d",
)
if savefigs:
dataio.save_current_figure("match_diff_emfits_experbays09_pcolor_{label}_{unique_id}.pdf")
if do_error_distrib_fits:
utils.pcolor_2d_data(
dist_diff_hist_target_bays09,
x=M_space,
y=sigmax_space,
xlabel="M",
ylabel="sigmax",
log_scale=True,
xlabel_format="%d",
)
if savefigs:
dataio.save_current_figure("match_hist_targets_experbays09_log_pcolor_{label}_{unique_id}.pdf")
utils.pcolor_2d_data(
dist_diff_hist_nontargets_bays09,
x=M_space,
y=sigmax_space,
xlabel="M",
ylabel="sigmax",
log_scale=True,
xlabel_format="%d",
)
if savefigs:
dataio.save_current_figure("match_hist_nontargets_experbays09_log_pcolor_{label}_{unique_id}.pdf")
utils.pcolor_2d_data(
dist_diff_hist_nontargets_5_6items_bays09,
x=M_space,
y=sigmax_space,
xlabel="M",
ylabel="sigmax",
log_scale=True,
xlabel_format="%d",
)
if savefigs:
dataio.save_current_figure(
"match_hist_nontargets_6items_experbays09_log_pcolor_{label}_{unique_id}.pdf"
)
# Macro plot
def mem_plot_precision(sigmax_i, M_i, mem_exp_prec):
ax = utils.plot_mean_std_area(
T_space[: mem_exp_prec.size],
mem_exp_prec,
np.zeros(mem_exp_prec.size),
linewidth=3,
fmt="o-",
markersize=8,
label="Experimental data",
)
ax = utils.plot_mean_std_area(
T_space[: mem_exp_prec.size],
result_all_precisions_mean[M_i, sigmax_i, : mem_exp_prec.size],
result_all_precisions_std[M_i, sigmax_i, : mem_exp_prec.size],
ax_handle=ax,
linewidth=3,
fmt="o-",
markersize=8,
label="Precision of samples",
)
# ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][M_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information')
# ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][M_i, sigmax_i], result_em_fits_std[..., 0][M_i, sigmax_i], ax_handle=ax, xlabel='Number of items', ylabel="Inverse variance $[rad^{-2}]$", linewidth=3, fmt='o-', markersize=8, label='Fitted kappa')
ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i]))
ax.legend()
ax.set_xlim([0.9, mem_exp_prec.size + 0.1])
ax.set_xticks(range(1, mem_exp_prec.size + 1))
ax.set_xticklabels(range(1, mem_exp_prec.size + 1))
if savefigs:
dataio.save_current_figure(
"memorycurves_precision_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i])
)
def mem_plot_kappa(sigmax_i, M_i, exp_kappa_mean, exp_kappa_std=None):
ax = utils.plot_mean_std_area(
T_space[: exp_kappa_mean.size],
exp_kappa_mean,
exp_kappa_std,
linewidth=3,
fmt="o-",
markersize=8,
label="Experimental data",
)
ax = utils.plot_mean_std_area(
T_space[: exp_kappa_mean.size],
result_em_fits_mean[..., : exp_kappa_mean.size, 0][M_i, sigmax_i],
result_em_fits_std[..., : exp_kappa_mean.size, 0][M_i, sigmax_i],
xlabel="Number of items",
ylabel="Memory error $[rad^{-2}]$",
linewidth=3,
fmt="o-",
markersize=8,
label="Fitted kappa",
ax_handle=ax,
)
# ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][M_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information')
ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i]))
ax.legend()
ax.set_xlim([0.9, exp_kappa_mean.size + 0.1])
ax.set_xticks(range(1, exp_kappa_mean.size + 1))
ax.set_xticklabels(range(1, exp_kappa_mean.size + 1))
ax.get_figure().canvas.draw()
if savefigs:
dataio.save_current_figure(
"memorycurves_kappa_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i])
)
def em_plot(sigmax_i, M_i):
f, ax = plt.subplots()
ax2 = ax.twinx()
# left axis, kappa
ax = utils.plot_mean_std_area(
T_space,
result_em_fits_mean[..., 0][M_i, sigmax_i],
result_em_fits_std[..., 0][M_i, sigmax_i],
xlabel="Number of items",
ylabel="Inverse variance $[rad^{-2}]$",
ax_handle=ax,
linewidth=3,
fmt="o-",
markersize=8,
label="Fitted kappa",
color="k",
)
# Right axis, mixture probabilities
utils.plot_mean_std_area(
T_space,
result_em_fits_mean[..., 1][M_i, sigmax_i],
result_em_fits_std[..., 1][M_i, sigmax_i],
xlabel="Number of items",
ylabel="Mixture probabilities",
ax_handle=ax2,
linewidth=3,
fmt="o-",
markersize=8,
label="Target",
)
utils.plot_mean_std_area(
T_space,
result_em_fits_mean[..., 2][M_i, sigmax_i],
result_em_fits_std[..., 2][M_i, sigmax_i],
xlabel="Number of items",
ylabel="Mixture probabilities",
ax_handle=ax2,
linewidth=3,
fmt="o-",
markersize=8,
label="Nontarget",
)
utils.plot_mean_std_area(
T_space,
result_em_fits_mean[..., 3][M_i, sigmax_i],
result_em_fits_std[..., 3][M_i, sigmax_i],
xlabel="Number of items",
ylabel="Mixture probabilities",
ax_handle=ax2,
linewidth=3,
fmt="o-",
markersize=8,
label="Random",
)
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2)
ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i]))
ax.set_xlim([0.9, T_space.size])
ax.set_xticks(range(1, T_space.size + 1))
ax.set_xticklabels(range(1, T_space.size + 1))
f.canvas.draw()
if savefigs:
dataio.save_current_figure(
"memorycurves_emfits_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (M_space[M_i], sigmax_space[sigmax_i])
)
def em_plot_paper(sigmax_i, M_i):
f, ax = plt.subplots()
# Right axis, mixture probabilities
utils.plot_mean_std_area(
T_space_bays09,
result_em_fits_mean[..., 1][M_i, sigmax_i][: T_space_bays09.size],
result_em_fits_std[..., 1][M_i, sigmax_i][: T_space_bays09.size],
xlabel="Number of items",
ylabel="Mixture probabilities",
ax_handle=ax,
linewidth=3,
fmt="o-",
markersize=5,
label="Target",
)
utils.plot_mean_std_area(
T_space_bays09,
result_em_fits_mean[..., 2][M_i, sigmax_i][: T_space_bays09.size],
result_em_fits_std[..., 2][M_i, sigmax_i][: T_space_bays09.size],
xlabel="Number of items",
ylabel="Mixture probabilities",
ax_handle=ax,
linewidth=3,
fmt="o-",
markersize=5,
label="Nontarget",
)
utils.plot_mean_std_area(
T_space_bays09,
result_em_fits_mean[..., 3][M_i, sigmax_i][: T_space_bays09.size],
result_em_fits_std[..., 3][M_i, sigmax_i][: T_space_bays09.size],
xlabel="Number of items",
ylabel="Mixture probabilities",
ax_handle=ax,
linewidth=3,
fmt="o-",
markersize=5,
label="Random",
)
ax.legend(prop={"size": 15})
ax.set_title("M %d, sigmax %.2f" % (M_space[M_i], sigmax_space[sigmax_i]))
ax.set_xlim([1.0, T_space_bays09.size])
ax.set_ylim([0.0, 1.1])
ax.set_xticks(range(1, T_space_bays09.size + 1))
ax.set_xticklabels(range(1, T_space_bays09.size + 1))
f.canvas.draw()
if savefigs:
dataio.save_current_figure(
"memorycurves_emfits_paper_M%dsigmax%.2f_{label}_{unique_id}.pdf"
% (M_space[M_i], sigmax_space[sigmax_i])
)
def hist_errors_targets_nontargets(hists_toplot_mean, hists_toplot_std, title="", M=0, sigmax=0, yaxis_lim="auto"):
f1, axes1 = plt.subplots(
ncols=hists_toplot_mean.shape[-2], figsize=(hists_toplot_mean.shape[-2] * 6, 6), sharey=True
)
for T_bays_i, T_bays in enumerate(T_space_bays09_filt):
if not np.all(np.isnan(hists_toplot_mean[T_bays_i])):
axes1[T_bays_i].bar(
bins_center,
hists_toplot_mean[T_bays_i],
width=2.0 * np.pi / (angle_space.size - 1),
align="center",
yerr=hists_toplot_std[T_bays_i],
)
axes1[T_bays_i].set_title("N=%d" % T_bays)
# axes1[T_{}]
axes1[T_bays_i].set_xlim(
[bins_center[0] - np.pi / (angle_space.size - 1), bins_center[-1] + np.pi / (angle_space.size - 1)]
)
if yaxis_lim == "target":
axes1[T_bays_i].set_ylim([0.0, 2.0])
elif yaxis_lim == "nontarget":
axes1[T_bays_i].set_ylim([0.0, 0.3])
else:
axes1[T_bays_i].set_ylim([0.0, np.nanmax(hists_toplot_mean + hists_toplot_std) * 1.1])
axes1[T_bays_i].set_xticks((-np.pi, -np.pi / 2, 0, np.pi / 2.0, np.pi))
axes1[T_bays_i].set_xticklabels(
(r"$-\pi$", r"$-\frac{\pi}{2}$", r"$0$", r"$\frac{\pi}{2}$", r"$\pi$"), fontsize=16
)
f1.canvas.draw()
if savefigs:
dataio.save_current_figure(
"memorycurves_hist_%s_paper_M%dsigmax%.2f_{label}_{unique_id}.pdf" % (title, M, sigmax)
)
#################################
if plot_selected_memory_curves:
selected_values = [[0.84, 0.23], [0.84, 0.19]]
for current_values in selected_values:
# Find the indices
M_i = np.argmin(np.abs(current_values[0] - M_space))
sigmax_i = np.argmin(np.abs(current_values[1] - sigmax_space))
mem_plot_precision(sigmax_i, M_i)
mem_plot_kappa(sigmax_i, M_i)
if plot_best_memory_curves:
# Best precision fit
best_axis2_i_all = np.argmin(dist_diff_precision_experim, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_precision(best_axis2_i, axis1_i, gorgo11_experimental_precision)
# Best kappa fit
best_axis2_i_all = np.argmin(dist_diff_emkappa_experim, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_kappa(
best_axis2_i, axis1_i, gorgo11_experimental_emfits_mean[0], gorgo11_experimental_emfits_std[0]
)
# em_plot(best_axis2_i, axis1_i)
# Best em parameters fit to Bays09
best_axis2_i_all = np.argmin(dist_diff_modelfits_experfits_bays09, axis=1)
# best_axis2_i_all = np.argmin(dist_diff_em_mixtures_bays09, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_kappa(
best_axis2_i,
axis1_i,
bays09_experimental_mixtures_mean_compatible[0, : T_space_bays09.size],
bays09_experimental_mixtures_std_compatible[0, : T_space_bays09.size],
)
# em_plot(best_axis2_i, axis1_i)
em_plot_paper(best_axis2_i, axis1_i)
if plot_best_error_distrib and do_error_distrib_fits:
# Best target histograms
best_axis2_i_all = np.argmin(dist_diff_hist_target_bays09, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
hist_errors_targets_nontargets(
hist_targets_mean[axis1_i, best_axis2_i],
hist_targets_std[axis1_i, best_axis2_i],
"target",
M=M_space[axis1_i],
sigmax=sigmax_space[best_axis2_i],
yaxis_lim="target",
)
# Best nontarget histograms
best_axis2_i_all = np.argmin(dist_diff_hist_nontargets_bays09, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
hist_errors_targets_nontargets(
hist_nontargets_mean[axis1_i, best_axis2_i],
hist_nontargets_std[axis1_i, best_axis2_i],
"nontarget",
M=M_space[axis1_i],
sigmax=sigmax_space[best_axis2_i],
yaxis_lim="nontarget",
)
all_args = data_pbs.loaded_data["args_list"]
variables_to_save = [
"gorgo11_experimental_precision",
"gorgo11_experimental_kappa",
"bays09_experimental_mixtures_mean_compatible",
"T_space",
]
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder="memory_curves")
plt.show()
return locals()
def transform(self, X, mask, verbose=True, report_interval=1, X_true=None):
"""
Impute missing values on new data. Assumes models have been previously
fitted on other data.
Parameters
----------
X : torch.DoubleTensor or torch.cuda.DoubleTensor, shape (n, d)
Contains non-missing and missing data at the indices given by the
"mask" argument. Missing values can be arbitrarily assigned
(e.g. with NaNs).
mask : torch.DoubleTensor or torch.cuda.DoubleTensor, shape (n, d)
mask[i,j] == 1 if X[i,j] is missing, else mask[i,j] == 0.
verbose: bool, default=True
If True, output loss to log during iterations.
report_interval : int, default=1
Interval between loss reports (if verbose).
X_true: torch.DoubleTensor or None, default=None
Ground truth for the missing values. If provided, will output a
validation score during training. For debugging only.
Returns
-------
X_filled: torch.DoubleTensor or torch.cuda.DoubleTensor
Imputed missing data (plus unchanged non-missing data).
"""
assert self.is_fitted, "The model has not been fitted yet."
n, d = X.shape
normalized_tol = self.tol * torch.max(torch.abs(X[~mask.bool()]))
order_ = torch.argsort(mask.sum(0))
X[mask] = nanmean(X)
X_filled = X.clone()
for i in range(self.max_iter):
if self.order == 'random':
order_ = np.random.choice(d, d, replace=False)
X_old = X_filled.clone().detach()
for l in range(d):
j = order_[l].item()
with torch.no_grad():
X_filled[mask[:, j].bool(), j] = self.models[j](
X_filled[mask[:,
j].bool(), :][:,
np.r_[0:j,
j + 1:d]]).squeeze()
if verbose and (i % report_interval == 0):
if X_true is not None:
logging.info(
f'Iteration {i}:\t '
f'Validation MAE: {MAE(X_filled, X_true, mask).item():.4f}\t'
f'RMSE: {RMSE(X_filled, X_true, mask).item():.4f}')
if torch.norm(X_filled - X_old, p=np.inf) < normalized_tol:
break
if i == (self.max_iter - 1) and verbose:
logging.info('Early stopping criterion not reached')
return X_filled
def plots_memory_curves(data_pbs, generator_module=None):
'''
Reload and plot memory curve of a Mixed code.
Can use Marginal Fisher Information and fitted Mixture Model as well
'''
#### SETUP
#
savefigs = True
savedata = True
plot_pcolor_fit_precision_to_fisherinfo = False
plot_selected_memory_curves = False
plot_best_memory_curves = False
plot_subplots_persigmax = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
# ratio_space, sigmax_space, T, 5
result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_marginal_inv_fi_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
result_marginal_inv_fi_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
result_marginal_fi_mean = utils.nanmean(np.squeeze(1./data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
result_marginal_fi_std = utils.nanstd(np.squeeze(1./data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
ratioconj_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
T_space = data_pbs.loaded_data['datasets_list'][0]['T_space']
print ratioconj_space
print sigmax_space
print T_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape, result_marginal_inv_fi_mean.shape
dataio = DataIO.DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
## Load Experimental data
data_simult = load_experimental_data.load_data_simult(data_dir=os.path.normpath(os.path.join(os.path.split(load_experimental_data.__file__)[0], '../../experimental_data/')), fit_mixture_model=True)
memory_experimental_precision = data_simult['precision_nitems_theo']
memory_experimental_kappa = np.array([data['kappa'] for _, data in data_simult['em_fits_nitems']['mean'].items()])
memory_experimental_kappa_std = np.array([data['kappa'] for _, data in data_simult['em_fits_nitems']['std'].items()])
experim_datadir = os.environ.get('WORKDIR_DROP', os.path.split(load_experimental_data.__file__)[0])
data_bays2009 = load_experimental_data.load_data_bays09(data_dir=os.path.normpath(os.path.join(experim_datadir, '../../experimental_data/')), fit_mixture_model=True)
bays09_experimental_mixtures_mean = data_bays2009['em_fits_nitems_arrays']['mean']
# add interpolated points for 3 and 5 items
bays3 = (bays09_experimental_mixtures_mean[:, 2] + bays09_experimental_mixtures_mean[:, 1])/2.
bays5 = (bays09_experimental_mixtures_mean[:, -1] + bays09_experimental_mixtures_mean[:, -2])/2.
bays09_experimental_mixtures_mean_compatible = np.c_[bays09_experimental_mixtures_mean[:,:2], bays3, bays09_experimental_mixtures_mean[:, 2], bays5]
# Boost non-targets
# bays09_experimental_mixtures_mean_compatible[1] *= 1.5
# bays09_experimental_mixtures_mean_compatible[2] /= 1.5
# bays09_experimental_mixtures_mean_compatible /= np.sum(bays09_experimental_mixtures_mean_compatible, axis=0)
# Compute some landscapes of fit!
dist_diff_precision_margfi = np.sum(np.abs(result_all_precisions_mean*2. - result_marginal_fi_mean[..., 0])**2., axis=-1)
dist_ratio_precision_margfi = np.sum(np.abs((result_all_precisions_mean*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1)
dist_diff_emkappa_margfi = np.sum(np.abs(result_em_fits_mean[..., 0]*2. - result_marginal_fi_mean[..., 0])**2., axis=-1)
dist_ratio_emkappa_margfi = np.sum(np.abs((result_em_fits_mean[..., 0]*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1)
dist_diff_precision_experim = np.sum(np.abs(result_all_precisions_mean - memory_experimental_precision)**2., axis=-1)
dist_diff_emkappa_experim = np.sum(np.abs(result_em_fits_mean[..., 0] - memory_experimental_kappa)**2., axis=-1)
dist_diff_precision_experim_1item = np.abs(result_all_precisions_mean[..., 0] - memory_experimental_precision[0])**2.
dist_diff_precision_experim_2item = np.abs(result_all_precisions_mean[..., 1] - memory_experimental_precision[1])**2.
dist_diff_precision_margfi_1item = np.abs(result_all_precisions_mean[..., 0]*2. - result_marginal_fi_mean[..., 0, 0])**2.
dist_diff_emkappa_experim_1item = np.abs(result_em_fits_mean[..., 0, 0] - memory_experimental_kappa[0])**2.
dist_diff_margfi_experim_1item = np.abs(result_marginal_fi_mean[..., 0, 0] - memory_experimental_precision[0])**2.
dist_diff_emkappa_mixtures_bays09 = np.sum(np.sum((result_em_fits_mean[..., 1:4] - bays09_experimental_mixtures_mean_compatible[1:].T)**2., axis=-1), axis=-1)
dist_diff_modelfits_experfits_bays09 = np.sum(np.sum((result_em_fits_mean[..., :4] - bays09_experimental_mixtures_mean_compatible.T)**2., axis=-1), axis=-1)
if plot_pcolor_fit_precision_to_fisherinfo:
# Check fit between precision and fisher info
utils.pcolor_2d_data(dist_diff_precision_margfi, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_precision_margfi_log_pcolor_{label}_{unique_id}.pdf')
# utils.pcolor_2d_data(dist_diff_precision_margfi, x=ratioconj_space, y=sigmax_space[2:], xlabel='ratio conj', ylabel='sigmax')
# if savefigs:
# dataio.save_current_figure('match_precision_margfi_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_ratio_precision_margfi, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_ratio_precision_margfi_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_emkappa_margfi, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_diff_emkappa_margfi_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_ratio_emkappa_margfi, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_ratio_emkappa_margfi_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_experim, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_diff_precision_experim_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_emkappa_experim, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_diff_emkappa_experim_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_margfi*dist_diff_emkappa_margfi*dist_diff_precision_experim*dist_diff_emkappa_experim, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_bigmultiplication_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_margfi_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_precision_margfi_1item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_experim_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_precision_experim_1item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_emkappa_experim_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_emkappa_experim_1item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_margfi_experim_1item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_margfi_experim_1item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_experim_2item, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_precision_experim_2item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_emkappa_mixtures_bays09, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_mixtures_experbays09_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_modelfits_experfits_bays09, log_scale=True, x=ratioconj_space, y=sigmax_space, xlabel='ratio conj', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_emfits_experbays09_pcolor_{label}_{unique_id}.pdf')
# Macro plot
def mem_plot_precision(sigmax_i, ratioconj_i):
ax = utils.plot_mean_std_area(T_space, memory_experimental_precision, np.zeros(T_space.size), linewidth=3, fmt='o-', markersize=8, label='Experimental data')
ax = utils.plot_mean_std_area(T_space, result_all_precisions_mean[ratioconj_i, sigmax_i], result_all_precisions_std[ratioconj_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Precision of samples')
# ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][ratioconj_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][ratioconj_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information')
# ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_i, sigmax_i], ax_handle=ax, xlabel='Number of items', ylabel="Inverse variance $[rad^{-2}]$", linewidth=3, fmt='o-', markersize=8, label='Fitted kappa')
ax.set_title('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
ax.legend()
ax.set_xlim([0.9, 5.1])
ax.set_xticks(range(1, 6))
ax.set_xticklabels(range(1, 6))
if savefigs:
dataio.save_current_figure('memorycurves_precision_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
def mem_plot_kappa(sigmax_i, ratioconj_i):
ax = utils.plot_mean_std_area(T_space, memory_experimental_kappa, memory_experimental_kappa_std, linewidth=3, fmt='o-', markersize=8, label='Experimental data')
ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Memory error $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa')
# ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][ratioconj_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][ratioconj_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information')
ax.set_title('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
ax.legend()
ax.set_xlim([0.9, 5.1])
ax.set_xticks(range(1, 6))
ax.set_xticklabels(range(1, 6))
if savefigs:
dataio.save_current_figure('memorycurves_kappa_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
def em_plot(sigmax_i, ratioconj_i):
# TODO finish checking this up.
f, ax = plt.subplots()
ax2 = ax.twinx()
# left axis, kappa
ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratioconj_i, sigmax_i], result_em_fits_std[..., 0][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Inverse variance $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa', color='k')
# Right axis, mixture probabilities
utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 1][ratioconj_i, sigmax_i], result_em_fits_std[..., 1][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Target')
utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 2][ratioconj_i, sigmax_i], result_em_fits_std[..., 2][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Nontarget')
utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 3][ratioconj_i, sigmax_i], result_em_fits_std[..., 3][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Random')
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2)
ax.set_title('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
ax.set_xlim([0.9, 5.1])
ax.set_xticks(range(1, 6))
ax.set_xticklabels(range(1, 6))
f.canvas.draw()
if savefigs:
dataio.save_current_figure('memorycurves_emfits_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
def em_plot_paper(sigmax_i, ratioconj_i):
f, ax = plt.subplots()
# Right axis, mixture probabilities
utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 1][ratioconj_i, sigmax_i], result_em_fits_std[..., 1][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target')
utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 2][ratioconj_i, sigmax_i], result_em_fits_std[..., 2][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget')
utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 3][ratioconj_i, sigmax_i], result_em_fits_std[..., 3][ratioconj_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Random')
ax.legend(prop={'size':15})
ax.set_title('ratio_conj %.2f, sigmax %.2f' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
ax.set_xlim([1.0, 5.0])
ax.set_ylim([0.0, 1.1])
ax.set_xticks(range(1, 6))
ax.set_xticklabels(range(1, 6))
f.canvas.draw()
if savefigs:
dataio.save_current_figure('memorycurves_emfits_paper_ratioconj%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratioconj_space[ratioconj_i], sigmax_space[sigmax_i]))
if plot_selected_memory_curves:
selected_values = [[0.84, 0.23], [0.84, 0.19]]
for current_values in selected_values:
# Find the indices
ratioconj_i = np.argmin(np.abs(current_values[0] - ratioconj_space))
sigmax_i = np.argmin(np.abs(current_values[1] - sigmax_space))
mem_plot_precision(sigmax_i, ratioconj_i)
mem_plot_kappa(sigmax_i, ratioconj_i)
if plot_best_memory_curves:
# Best precision fit
best_axis2_i_all = np.argmin(dist_diff_precision_experim, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_precision(best_axis2_i, axis1_i)
# Best kappa fit
best_axis2_i_all = np.argmin(dist_diff_emkappa_experim, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_kappa(best_axis2_i, axis1_i)
em_plot(best_axis2_i, axis1_i)
# Best em parameters fit to Bays09
best_axis2_i_all = np.argmin(dist_diff_modelfits_experfits_bays09, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_kappa(best_axis2_i, axis1_i)
# em_plot(best_axis2_i, axis1_i)
em_plot_paper(best_axis2_i, axis1_i)
if plot_subplots_persigmax:
# Do subplots with ratio_conj on x, one plot per T and different figures per sigmax. Looks a bit like reloader_hierarchical_constantMMlower_maxlik_allresponses_221213.py
sigmax_selected_indices = np.array([np.argmin((sigmax_space - sigmax_select)**2.) for sigmax_select in [0.1, 0.2, 0.3, 0.4, 0.5]])
for sigmax_select_i in sigmax_selected_indices:
# two plots per sigmax.
f, axes = plt.subplots(nrows=T_space[-1], ncols=1, sharex=True, figsize=(10, 12))
# all_lines_bis = []
for T_i, T in enumerate(T_space):
# Plot EM mixtures
utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 1], result_em_fits_std[:, sigmax_select_i, T_i, 1], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5)
utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 2], result_em_fits_std[:, sigmax_select_i, T_i, 2], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5)
utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 3], result_em_fits_std[:, sigmax_select_i, T_i, 3], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5)
# ratio_MMlower_space, result_emfits_filtered[:, i, 1:4], 0*result_emfits_filtered[:, i, 1:4], ax_handle=axes[T_i], linewidth=2) #, color=all_lines[T_i].get_color())
# curr_lines = axes[T_i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[T_i].get_color())
axes[T_i].grid()
axes[T_i].set_xticks(np.linspace(0., 1.0, 5))
axes[T_i].set_xlim((0.0, 1.0))
# axes[T_i].set_yticks([])
# axes[T_i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1))
axes[T_i].set_ylim((0.0, 1.05))
axes[T_i].locator_params(axis='y', tight=True, nbins=4)
# all_lines_bis.extend(curr_lines)
axes[0].set_title('Sigmax: %.3f' % sigmax_space[sigmax_select_i])
axes[-1].set_xlabel('Proportion of conjunctive units')
if savefigs:
dataio.save_current_figure('results_subplots_emmixtures_sigmax%.2f_{label}_global_{unique_id}.pdf' % sigmax_space[sigmax_select_i])
f, axes = plt.subplots(nrows=T_space[-1], ncols=1, sharex=True, figsize=(10, 12))
# Kappa kappa
for T_i, T in enumerate(T_space):
# Plot kappa mixture
utils.plot_mean_std_area(ratioconj_space, result_em_fits_mean[:, sigmax_select_i, T_i, 0], result_em_fits_std[:, sigmax_select_i, T_i, 0], ax_handle=axes[T_i], linewidth=3, fmt='o-', markersize=5)
# utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 0], 0*result_emfits_filtered[:, i, 0], ax_handle=axes[T_i], linewidth=2) #, color=all_lines[T_i].get_color())
# curr_lines = axes[T_i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[T_i].get_color())
axes[T_i].grid()
axes[T_i].set_xticks(np.linspace(0., 1.0, 5))
axes[T_i].set_xlim((0.0, 1.0))
# axes[T_i].set_yticks([])
# axes[T_i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1))
# axes[T_i].set_ylim((0.0, 1.0))
axes[T_i].locator_params(axis='y', tight=True, nbins=4)
# all_lines_bis.extend(curr_lines)
axes[0].set_title('Sigmax: %.3f' % sigmax_space[sigmax_select_i])
axes[-1].set_xlabel('Proportion of conjunctive units')
# f.subplots_adjust(right=0.75)
# plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5))
if savefigs:
dataio.save_current_figure('results_subplots_emkappa_sigmax%.2f_{label}_global_{unique_id}.pdf' % sigmax_space[sigmax_select_i])
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['memory_experimental_precision', 'memory_experimental_kappa', 'bays09_experimental_mixtures_mean_compatible']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='memory_curves')
plt.show()
return locals()
def postprocess_dualrecall_fitmixturemodel(data_pbs, generator_module=None):
'''
Reload runs from PBS
To be plotted in Ipython later
'''
#### SETUP
#
savedata = 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
result_em_fits = np.array(data_pbs.dict_arrays['result_em_fits']['results_flat'])
result_dist_dualrecall_angle = np.array(data_pbs.dict_arrays['result_dist_dualrecall_angle']['results_flat'])
result_dist_dualrecall_angle_emmixt_KL = np.array(data_pbs.dict_arrays['result_dist_dualrecall_angle_emmixt_KL']['results_flat'])
result_dist_dualrecall_colour = np.array(data_pbs.dict_arrays['result_dist_dualrecall_colour']['results_flat'])
result_dist_dualrecall_colour_emmixt_KL = np.array(data_pbs.dict_arrays['result_dist_dualrecall_colour_emmixt_KL']['results_flat'])
result_parameters_flat = np.array(data_pbs.dict_arrays['result_em_fits']['parameters_flat'])
all_repeats_completed = data_pbs.dict_arrays['result_em_fits']['repeats_completed']
all_args_arr = np.array(data_pbs.loaded_data['args_list'])
M_space = data_pbs.loaded_data['parameters_uniques']['M']
ratio_conj_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
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 ground truth
data_dualrecall = load_experimental_data.load_data_dualrecall(fit_mixture_model=True)
## Filter everything with repeats_completed == num_repet
filter_data = all_repeats_completed == num_repetitions - 1
result_parameters_flat = result_parameters_flat[filter_data]
result_em_fits = result_em_fits[filter_data]
result_dist_dualrecall_angle = result_dist_dualrecall_angle[filter_data]
result_dist_dualrecall_angle_emmixt_KL = result_dist_dualrecall_angle_emmixt_KL[filter_data]
result_dist_dualrecall_colour = result_dist_dualrecall_colour[filter_data]
result_dist_dualrecall_colour_emmixt_KL = result_dist_dualrecall_colour_emmixt_KL[filter_data]
all_args_arr = all_args_arr[filter_data]
all_repeats_completed = all_repeats_completed[filter_data]
print "Size post-filter: ", result_parameters_flat.shape[0]
# Compute lots of averages over the repetitions
result_em_fits_avg = utils.nanmean(result_em_fits, axis=-1)
result_dist_dualrecall_angle_avg = utils.nanmean(result_dist_dualrecall_angle, axis=-1)
result_dist_dualrecall_angle_emmixt_KL_avg = utils.nanmean(result_dist_dualrecall_angle_emmixt_KL, axis=-1)
result_dist_dualrecall_colour_avg = utils.nanmean(result_dist_dualrecall_colour, axis=-1)
result_dist_dualrecall_colour_emmixt_KL_avg = utils.nanmean(result_dist_dualrecall_colour_emmixt_KL, axis=-1)
# all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['parameter_names_sorted', 'all_args_arr', 'all_repeats_completed', 'filter_data']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='dualrecall_fitmixturemodel')
plt.show()
return locals()
def compute_result_distemfits_dataset(self,
all_variables,
experiment_id='bays09',
cache_array_name='result_dist_bays09',
variable_selection_slice=slice(
None, 4),
variable_selection_slice_cache=slice(
None, None),
metric='mse'):
'''
Result is the distance (sum squared) to experimental data fits
Assume that:
- result_em_fits exists. Does an average over repetitions_axis and sums over all others.
- metric = 'mse' | 'kl'
- variable_selection_slice: slice(0, 1) for kappa, slice(1, 4) for mixt proportions, slice(none, 4) for all EM params
'''
assert metric == 'mse' or metric == 'kl', "Metric should be {mse, kl}"
repetitions_axis = all_variables.get('repetitions_axis', -1)
if cache_array_name in all_variables:
# If already computed, use it
result_dist_allT = utils.nanmean(
all_variables[cache_array_name][:, variable_selection_slice_cache],
axis=repetitions_axis)
elif 'result_em_fits' in all_variables:
# Do some annoying slice manipulation
slice_valid_indices = variable_selection_slice.indices(
all_variables['result_em_fits'].shape[1])
# Create output variables
if metric == 'mse':
result_dist_allT = np.nan * np.empty(
(all_variables['T_space'].size,
slice_valid_indices[1] - slice_valid_indices[0]))
elif metric == 'kl':
result_dist_allT = np.nan * np.empty((all_variables['T_space'].size))
### Result computation
if experiment_id == 'bays09':
data_loaded = load_experimental_data.load_data_bays09(
fit_mixture_model=True)
elif experiment_id == 'gorgo11':
data_loaded = load_experimental_data.load_data_gorgo11(
fit_mixture_model=True)
else:
raise ValueError('wrong experiment_id {}'.format(experiment_id))
experimental_mixtures_mean = data_loaded['em_fits_nitems_arrays']['mean']
experimental_T_space = np.unique(data_loaded['n_items'])
curr_result = np.nan
for T_i, T in enumerate(all_variables['T_space']):
if T in experimental_T_space:
if metric == 'mse':
curr_result = (
experimental_mixtures_mean[variable_selection_slice,
experimental_T_space == T] -
all_variables['result_em_fits'][T_i, variable_selection_slice]
)**2.
elif metric == 'kl':
curr_result = utils.KL_div(
all_variables['result_em_fits'][T_i, variable_selection_slice],
experimental_mixtures_mean[variable_selection_slice,
experimental_T_space == T],
axis=0)
result_dist_allT[T_i] = utils.nanmean(
curr_result, axis=repetitions_axis)
else:
raise ValueError(
'array {}/result_em_fits not present, bad'.format(cache_array_name))
print result_dist_allT
# return the overall distance, over all parameters and number of items
return np.nansum(result_dist_allT)
def plots_3dvolume_hierarchical_M_Mlayerone(data_pbs, generator_module=None):
'''
Reload 3D volume runs from PBS and plot them
'''
#### SETUP
#
savefigs = True
savedata = False
plots_pcolors = False
plots_singleaxe = False
plots_multipleaxes = True
plots_multipleaxes_emfits = True
load_fit_mixture_model = True
# caching_emfit_filename = None
caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_emfit.pickle')
plt.rcParams['font.size'] = 16
#
#### /SETUP
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
print "Order parameters: ", generator_module.dict_parameters_range.keys()
results_precision_constant_M_Mlower = np.squeeze(utils.nanmean(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1))
results_precision_constant_M_Mlower_std = np.squeeze(utils.nanstd(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1))
results_responses = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
results_targets = np.squeeze(data_pbs.dict_arrays['result_targets']['results'])
results_nontargets = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])
results_emfits_M_T = np.squeeze(data_pbs.dict_arrays['results_emfits_M_T']['results'])
M_space = data_pbs.loaded_data['parameters_uniques']['M']
M_layer_one_space = data_pbs.loaded_data['parameters_uniques']['M_layer_one']
ratio_MMlower_space = M_space/generator_module.filtering_function_parameters['target_M_total']
filtering_indices = (np.arange(M_space.size), np.arange(-M_layer_one_space.size, 0)[::-1])
T = results_precision_constant_M_Mlower.shape[-1]
T_space = np.arange(T)
print M_space
print M_layer_one_space
print results_precision_constant_M_Mlower.shape
# print results_precision_constant_M_Mlower
T = results_precision_constant_M_Mlower.shape[-1]
results_precision_filtered = results_precision_constant_M_Mlower[filtering_indices]
results_precision_filtered_std = results_precision_constant_M_Mlower_std[filtering_indices]
results_responses_filtered = results_responses[filtering_indices]
results_targets_filtered = results_targets[filtering_indices]
results_nontargets_filtered = results_nontargets[filtering_indices]
results_emfits_M_T_filtered = results_emfits_M_T[filtering_indices]
results_precision_filtered_smoothed = np.apply_along_axis(smooth, 0, results_precision_filtered, *(10, 'bartlett'))
if load_fit_mixture_model:
# Fit the mixture model on the samples
if caching_emfit_filename is not None:
if os.path.exists(caching_emfit_filename):
# Got file, open it and try to use its contents
try:
with open(caching_emfit_filename, 'r') as file_in:
# Load and assign values
cached_data = pickle.load(file_in)
result_emfits_filtered = cached_data['result_emfits_filtered']
print "Loading from cache file %s" % caching_emfit_filename
load_fit_mixture_model = False
except IOError:
print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits"
load_fit_mixture_model = False
if load_fit_mixture_model:
result_emfits_filtered = np.nan*np.empty((ratio_MMlower_space.size, T, 5))
# Fit EM model
print "fitting EM model"
for ratio_MMlower_i, ratio_MMlower in enumerate(ratio_MMlower_space):
for T_i in T_space:
if np.any(~np.isnan(results_responses_filtered[ratio_MMlower_i, T_i])):
print "ratio MM, T:", ratio_MMlower, T_i+1
curr_em_fits = em_circularmixture_allitems_uniquekappa.fit(results_responses_filtered[ratio_MMlower_i, T_i], results_targets_filtered[ratio_MMlower_i, T_i], results_nontargets_filtered[ratio_MMlower_i, T_i, :, :T_i])
curr_em_fits['mixt_nontargets_sum'] = np.sum(curr_em_fits['mixt_nontargets'])
result_emfits_filtered[ratio_MMlower_i, T_i] = [curr_em_fits[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL')]
# Save everything to a file, for faster later plotting
if caching_emfit_filename is not None:
try:
with open(caching_emfit_filename, 'w') as filecache_out:
data_emfit = dict(result_emfits_filtered=result_emfits_filtered)
pickle.dump(data_emfit, filecache_out, protocol=2)
print "cache file %s written" % caching_emfit_filename
except IOError:
print "Error writing out to caching file ", caching_emfit_filename
if plots_pcolors:
utils.pcolor_2d_data(results_precision_filtered, log_scale=True, x=ratio_MMlower_space, y=np.arange(1, T+1), xlabel="$\\frac{M}{M+M_{layer one}}$", ylabel='$T$', ticks_interpolate=10)
plt.plot(np.argmax(results_precision_filtered, axis=0), np.arange(results_precision_filtered.shape[-1]), 'ko', markersize=10)
if savefigs:
dataio.save_current_figure('results_2dlog_{label}_global_{unique_id}.pdf')
utils.pcolor_2d_data(results_precision_filtered/np.max(results_precision_filtered, axis=0), x=ratio_MMlower_space, y=np.arange(1, T+1), xlabel="$\\frac{M}{M+M_{layer one}}$", ylabel='$T$', ticks_interpolate=10)
plt.plot(np.argmax(results_precision_filtered, axis=0), np.arange(results_precision_filtered.shape[-1]), 'ko', markersize=10)
if savefigs:
dataio.save_current_figure('results_2dnorm_{label}_global_{unique_id}.pdf')
utils.pcolor_2d_data(results_precision_filtered_smoothed/np.max(results_precision_filtered_smoothed, axis=0), x=ratio_MMlower_space, y=np.arange(1, T+1), xlabel="$\\frac{M}{M+M_{layer one}}$", ylabel='$T$', ticks_interpolate=10)
plt.plot(np.argmax(results_precision_filtered_smoothed, axis=0), np.arange(results_precision_filtered_smoothed.shape[-1]), 'ko', markersize=10)
if savefigs:
dataio.save_current_figure('results_2dsmoothnorm_{label}_global_{unique_id}.pdf')
if plots_singleaxe:
plt.figure()
plt.plot(ratio_MMlower_space, results_precision_filtered_smoothed/np.max(results_precision_filtered_smoothed, axis=0), linewidth=2)
plt.plot(ratio_MMlower_space[np.argmax(results_precision_filtered_smoothed, axis=0)], np.ones(results_precision_filtered_smoothed.shape[-1]), 'ro', markersize=10)
plt.grid()
plt.ylim((0., 1.1))
plt.subplots_adjust(right=0.8)
plt.legend(['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='center right', bbox_to_anchor=(1.3, 0.5))
plt.xticks(np.linspace(0, 1.0, 5))
if savefigs:
dataio.save_current_figure('results_1dsmoothnormsame_{label}_global_{unique_id}.pdf')
plt.figure()
moved_results_precision_filtered_smoothed = 1.2*np.arange(results_precision_filtered_smoothed.shape[-1]) + results_precision_filtered_smoothed/np.max(results_precision_filtered_smoothed, axis=0)
all_lines = []
for i, max_i in enumerate(np.argmax(results_precision_filtered_smoothed, axis=0)):
curr_lines = plt.plot(ratio_MMlower_space, moved_results_precision_filtered_smoothed[:, i], linewidth=2)
plt.plot(ratio_MMlower_space[max_i], moved_results_precision_filtered_smoothed[max_i, i], 'o', markersize=10, color=curr_lines[0].get_color())
all_lines.extend(curr_lines)
plt.plot(np.linspace(0.0, 1.0, 100), np.outer(np.ones(100), 1.2*np.arange(1, results_precision_filtered_smoothed.shape[-1])), 'k:')
plt.grid()
plt.legend(all_lines, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='best')
plt.ylim((0., moved_results_precision_filtered_smoothed.max()*1.05))
plt.yticks([])
plt.xticks(np.linspace(0, 1.0, 5))
if savefigs:
dataio.save_current_figure('results_1dsmoothnorm_{label}_global_{unique_id}.pdf')
if plots_multipleaxes:
# Plot smooth precisions, all T on multiple subplots.
# all_lines = []
f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12))
for i, max_ind in enumerate(np.argmax(results_precision_filtered_smoothed, axis=0)):
curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered_smoothed[:, i], linewidth=2) # , color=all_lines[i].get_color())
axes[i].plot(ratio_MMlower_space[max_ind], results_precision_filtered_smoothed[max_ind, i], 'o', markersize=10, color=curr_lines[0].get_color())
axes[i].grid()
axes[i].set_xticks(np.linspace(0., 1.0, 5))
axes[i].set_xlim((0.0, 1.0))
# axes[i].set_yticks([])
axes[i].set_ylim((np.min(results_precision_filtered_smoothed[:, i]), results_precision_filtered_smoothed[max_ind, i]*1.1))
axes[i].locator_params(axis='y', tight=True, nbins=4)
# all_lines.extend(curr_lines)
f.subplots_adjust(right=0.75)
# plt.figlegend(all_lines, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5))
# f.tight_layout()
if savefigs:
dataio.save_current_figure('results_subplots_1dsmoothnorm_{label}_global_{unique_id}.pdf')
# Plot precisions with standard deviation around
f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12))
# all_lines_bis = []
for i, max_ind in enumerate(np.argmax(results_precision_filtered, axis=0)):
utils.plot_mean_std_area(ratio_MMlower_space, results_precision_filtered[:, i], results_precision_filtered_std[:, i], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color())
# curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color())
axes[i].grid()
axes[i].set_xticks(np.linspace(0., 1.0, 5))
axes[i].set_xlim((0.0, 1.0))
# axes[i].set_yticks([])
axes[i].set_ylim((np.min(results_precision_filtered[:, i]), results_precision_filtered[max_ind, i]*1.1))
axes[i].locator_params(axis='y', tight=True, nbins=4)
# all_lines_bis.extend(curr_lines)
f.subplots_adjust(right=0.75)
# plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5))
if savefigs:
dataio.save_current_figure('results_subplots_1dnorm_{label}_global_{unique_id}.pdf')
if plots_multipleaxes_emfits:
f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12))
all_lines_bis = []
for i, max_ind in enumerate(np.nanargmax(result_emfits_filtered[..., 0], axis=0)):
utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 0], 0*result_emfits_filtered[:, i, 0], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color())
# curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color())
axes[i].grid()
axes[i].set_xticks(np.linspace(0., 1.0, 5))
axes[i].set_xlim((0.0, 1.0))
# axes[i].set_yticks([])
# axes[i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1))
axes[i].locator_params(axis='y', tight=True, nbins=4)
# all_lines_bis.extend(curr_lines)
f.subplots_adjust(right=0.75)
# plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5))
if savefigs:
dataio.save_current_figure('results_subplots_emkappa_{label}_global_{unique_id}.pdf')
variables_to_save = []
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='hierarchicalrandomnetwork_characterisation')
plt.show()
return locals()
def plots_memory_curves(data_pbs, generator_module=None):
'''
Reload and plot memory curve of a Mixed code.
Can use Marginal Fisher Information and fitted Mixture Model as well
'''
#### SETUP
#
savefigs = True
savedata = True
plot_pcolor_fit_precision_to_fisherinfo = True
plot_selected_memory_curves = False
plot_best_memory_curves = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
ratiohier_space = data_pbs.loaded_data['parameters_uniques']['ratio_hierarchical']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
T_space = data_pbs.loaded_data['datasets_list'][0]['T_space']
print ratiohier_space
print sigmax_space
print T_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'])
## Load Experimental data
data_simult = load_experimental_data.load_data_simult(data_dir=os.path.normpath(os.path.join(os.path.split(load_experimental_data.__file__)[0], '../../experimental_data/')), fit_mixture_model=True)
gorgo11_experimental_precision = data_simult['precision_nitems_theo']
gorgo11_experimental_kappa = np.array([data['kappa'] for _, data in data_simult['em_fits_nitems']['mean'].items()])
gorgo11_experimental_kappa_std = np.array([data['kappa'] for _, data in data_simult['em_fits_nitems']['std'].items()])
gorgo11_experimental_emfits_mean = np.array([[data[key] for _, data in data_simult['em_fits_nitems']['mean'].items()] for key in ['kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random']])
gorgo11_experimental_emfits_std = np.array([[data[key] for _, data in data_simult['em_fits_nitems']['std'].items()] for key in ['kappa', 'mixt_target', 'mixt_nontargets', 'mixt_random']])
gorgo11_experimental_emfits_sem = gorgo11_experimental_emfits_std/np.sqrt(np.unique(data_simult['subject']).size)
gorgo11_T_space = data_simult['data_to_fit']['n_items']
data_bays2009 = load_experimental_data.load_data_bays09(fit_mixture_model=True)
bays09_experimental_mixtures_mean = data_bays2009['em_fits_nitems_arrays']['mean']
bays09_experimental_mixtures_std = data_bays2009['em_fits_nitems_arrays']['std']
# add interpolated points for 3 and 5 items
emfit_mean_intpfct = spint.interp1d(np.unique(data_bays2009['n_items']), bays09_experimental_mixtures_mean)
bays09_experimental_mixtures_mean_compatible = emfit_mean_intpfct(np.arange(1, 7))
emfit_std_intpfct = spint.interp1d(np.unique(data_bays2009['n_items']), bays09_experimental_mixtures_std)
bays09_experimental_mixtures_std_compatible = emfit_std_intpfct(np.arange(1, 7))
bays09_T_space_interp = np.arange(1, 7)
bays09_T_space = data_bays2009['data_to_fit']['n_items']
# Compute some landscapes of fit!
dist_diff_precision_experim = np.sum(np.abs(result_all_precisions_mean[..., :gorgo11_experimental_precision.size] - gorgo11_experimental_precision)**2., axis=-1)
dist_diff_emkappa_experim = np.sum(np.abs(result_em_fits_mean[..., :gorgo11_experimental_precision.size, 0] - gorgo11_experimental_kappa)**2., axis=-1)
dist_diff_precision_experim_1item = np.abs(result_all_precisions_mean[..., 0] - gorgo11_experimental_precision[0])**2.
dist_diff_precision_experim_2item = np.abs(result_all_precisions_mean[..., 1] - gorgo11_experimental_precision[1])**2.
dist_diff_emkappa_experim_1item = np.abs(result_em_fits_mean[..., 0, 0] - gorgo11_experimental_kappa[0])**2.
dist_diff_em_mixtures_bays09 = np.sum(np.sum((result_em_fits_mean[..., 1:4] - bays09_experimental_mixtures_mean_compatible[1:].T)**2., axis=-1), axis=-1)
dist_diff_modelfits_experfits_bays09 = np.sum(np.sum((result_em_fits_mean[..., :4] - bays09_experimental_mixtures_mean_compatible.T)**2., axis=-1), axis=-1)
if plot_pcolor_fit_precision_to_fisherinfo:
utils.pcolor_2d_data(dist_diff_precision_experim, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_diff_precision_experim_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_emkappa_experim, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_diff_emkappa_experim_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_experim*dist_diff_emkappa_experim, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax', log_scale=True)
if savefigs:
dataio.save_current_figure('match_bigmultiplication_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_experim_1item, log_scale=True, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_precision_experim_1item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_emkappa_experim_1item, log_scale=True, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_emkappa_experim_1item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_precision_experim_2item, log_scale=True, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_precision_experim_2item_log_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_em_mixtures_bays09, log_scale=True, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_mixtures_experbays09_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_diff_modelfits_experfits_bays09, log_scale=True, x=ratiohier_space, y=sigmax_space, xlabel='ratio hier', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_diff_emfits_experbays09_pcolor_{label}_{unique_id}.pdf')
# Macro plot
def mem_plot_precision(sigmax_i, ratiohier_i, mem_exp_prec):
ax = utils.plot_mean_std_area(T_space[:mem_exp_prec.size], mem_exp_prec, np.zeros(mem_exp_prec.size), linewidth=3, fmt='o-', markersize=8, label='Experimental data')
ax = utils.plot_mean_std_area(T_space[:mem_exp_prec.size], result_all_precisions_mean[ratiohier_i, sigmax_i, :mem_exp_prec.size], result_all_precisions_std[ratiohier_i, sigmax_i, :mem_exp_prec.size], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Precision of samples')
# ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][ratiohier_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][ratiohier_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Marginal Fisher Information')
# ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratiohier_i, sigmax_i], result_em_fits_std[..., 0][ratiohier_i, sigmax_i], ax_handle=ax, xlabel='Number of items', ylabel="Inverse variance $[rad^{-2}]$", linewidth=3, fmt='o-', markersize=8, label='Fitted kappa')
ax.set_title('ratio_hier %.2f, sigmax %.2f' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
ax.legend()
ax.set_xlim([0.9, mem_exp_prec.size + 0.1])
ax.set_xticks(range(1, mem_exp_prec.size + 1))
ax.set_xticklabels(range(1, mem_exp_prec.size + 1))
if savefigs:
dataio.save_current_figure('memorycurves_precision_ratiohier%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
def mem_plot_kappa(sigmax_i, ratiohier_i, T_space_exp, exp_kappa_mean, exp_kappa_std=None):
ax = utils.plot_mean_std_area(T_space_exp, exp_kappa_mean, exp_kappa_std, linewidth=3, fmt='o-', markersize=8, label='Experimental data')
ax = utils.plot_mean_std_area(T_space[:T_space_exp.max()], result_em_fits_mean[..., :T_space_exp.max(), 0][ratiohier_i, sigmax_i], result_em_fits_std[..., :T_space_exp.max(), 0][ratiohier_i, sigmax_i], xlabel='Number of items', ylabel="Memory error $[rad^{-2}]$", linewidth=3, fmt='o-', markersize=8, label='Fitted kappa', ax_handle=ax)
ax.set_title('ratio_hier %.2f, sigmax %.2f' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
ax.legend()
ax.set_xlim([0.9, T_space_exp.max()+0.1])
ax.set_xticks(range(1, T_space_exp.max()+1))
ax.set_xticklabels(range(1, T_space_exp.max()+1))
ax.get_figure().canvas.draw()
if savefigs:
dataio.save_current_figure('memorycurves_kappa_ratiohier%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
# def em_plot(sigmax_i, ratiohier_i):
# # TODO finish checking this up.
# f, ax = plt.subplots()
# ax2 = ax.twinx()
# # left axis, kappa
# ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][ratiohier_i, sigmax_i], result_em_fits_std[..., 0][ratiohier_i, sigmax_i], xlabel='Number of items', ylabel="Inverse variance $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa', color='k')
# # Right axis, mixture probabilities
# utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 1][ratiohier_i, sigmax_i], result_em_fits_std[..., 1][ratiohier_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Target')
# utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 2][ratiohier_i, sigmax_i], result_em_fits_std[..., 2][ratiohier_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Nontarget')
# utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 3][ratiohier_i, sigmax_i], result_em_fits_std[..., 3][ratiohier_i, sigmax_i], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Random')
# lines, labels = ax.get_legend_handles_labels()
# lines2, labels2 = ax2.get_legend_handles_labels()
# ax.legend(lines + lines2, labels + labels2)
# ax.set_title('ratio_hier %.2f, sigmax %.2f' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
# ax.set_xlim([0.9, 5.1])
# ax.set_xticks(range(1, 6))
# ax.set_xticklabels(range(1, 6))
# f.canvas.draw()
# if savefigs:
# dataio.save_current_figure('memorycurves_emfits_ratiohier%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
def em_plot_paper(sigmax_i, ratiohier_i):
f, ax = plt.subplots()
# mixture probabilities
utils.plot_mean_std_area(bays09_T_space_interp, result_em_fits_mean[..., 1][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], result_em_fits_std[..., 1][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target')
utils.plot_mean_std_area(bays09_T_space_interp, result_em_fits_mean[..., 2][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], result_em_fits_std[..., 2][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget')
utils.plot_mean_std_area(bays09_T_space_interp, result_em_fits_mean[..., 3][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], result_em_fits_std[..., 3][ratiohier_i, sigmax_i][:bays09_T_space_interp.size], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Random')
ax.legend(prop={'size':15})
ax.set_title('ratio_hier %.2f, sigmax %.2f' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
ax.set_xlim([1.0, bays09_T_space_interp.size])
ax.set_ylim([0.0, 1.1])
ax.set_xticks(range(1, bays09_T_space_interp.size+1))
ax.set_xticklabels(range(1, bays09_T_space_interp.size+1))
f.canvas.draw()
if savefigs:
dataio.save_current_figure('memorycurves_emfits_paper_ratiohier%.2fsigmax%.2f_{label}_{unique_id}.pdf' % (ratiohier_space[ratiohier_i], sigmax_space[sigmax_i]))
if plot_selected_memory_curves:
selected_values = [[0.84, 0.23], [0.84, 0.19]]
for current_values in selected_values:
# Find the indices
ratiohier_i = np.argmin(np.abs(current_values[0] - ratiohier_space))
sigmax_i = np.argmin(np.abs(current_values[1] - sigmax_space))
# mem_plot_precision(sigmax_i, ratiohier_i)
# mem_plot_kappa(sigmax_i, ratiohier_i)
if plot_best_memory_curves:
# Best precision fit
best_axis2_i_all = np.argmin(dist_diff_precision_experim, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_precision(best_axis2_i, axis1_i, gorgo11_experimental_precision)
# Best kappa fit
best_axis2_i_all = np.argmin(dist_diff_emkappa_experim, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_kappa(best_axis2_i, axis1_i, gorgo11_T_space, gorgo11_experimental_emfits_mean[0], gorgo11_experimental_emfits_std[0])
# em_plot(best_axis2_i, axis1_i)
# Best em parameters fit to Bays09
best_axis2_i_all = np.argmin(dist_diff_modelfits_experfits_bays09, axis=1)
# best_axis2_i_all = np.argmin(dist_diff_em_mixtures_bays09, axis=1)
for axis1_i, best_axis2_i in enumerate(best_axis2_i_all):
mem_plot_kappa(best_axis2_i, axis1_i, bays09_T_space, bays09_experimental_mixtures_mean[0], bays09_experimental_mixtures_std[0])
# em_plot(best_axis2_i, axis1_i)
em_plot_paper(best_axis2_i, axis1_i)
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['gorgo11_experimental_precision', 'gorgo11_experimental_kappa', 'bays09_experimental_mixtures_mean_compatible']
if savedata:
dataio.save_variables_default(locals(), additional_variables=variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='memory_curves')
plt.show()
return locals()
def plots_specific_stimuli_hierarchical(data_pbs, generator_module=None):
'''
Reload and plot behaviour of mixed population code on specific Stimuli
of 3 items.
'''
#### SETUP
#
savefigs = True
savedata = True
plot_per_min_dist_all = False
specific_plots_paper = False
plots_emfit_allitems = False
plot_min_distance_effect = True
should_fit_allitems_model = True
# caching_emfit_filename = None
caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_emfitallitems_uniquekappa.pickle')
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_kappastddev_mean = utils.nanmean(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
result_em_kappastddev_std = utils.nanstd(utils.kappa_to_stddev(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results'])[..., 0, :]), axis=-1)
result_responses_all = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
result_target_all = np.squeeze(data_pbs.dict_arrays['result_target']['results'])
result_nontargets_all = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])
all_args = data_pbs.loaded_data['args_list']
nb_repetitions = np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']).shape[-1]
print nb_repetitions
nb_repetitions = result_responses_all.shape[-1]
print nb_repetitions
K = result_nontargets_all.shape[-2]
N = result_responses_all.shape[-2]
enforce_min_distance_space = data_pbs.loaded_data['parameters_uniques']['enforce_min_distance']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
MMlower_valid_space = data_pbs.loaded_data['datasets_list'][0]['MMlower_valid_space']
ratio_space = MMlower_valid_space[:, 0]/float(np.sum(MMlower_valid_space[0]))
print enforce_min_distance_space
print sigmax_space
print MMlower_valid_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
# Relaod cached emfitallitems
if caching_emfit_filename is not None:
if os.path.exists(caching_emfit_filename):
# Got file, open it and try to use its contents
try:
with open(caching_emfit_filename, 'r') as file_in:
# Load and assign values
cached_data = pickle.load(file_in)
result_emfitallitems = cached_data['result_emfitallitems']
should_fit_allitems_model = False
except IOError:
print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits"
if plot_per_min_dist_all:
# Do one plot per min distance.
for min_dist_i, min_dist in enumerate(enforce_min_distance_space):
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist)
if savefigs:
dataio.save_current_figure('precision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Show log precision
utils.pcolor_2d_data(result_all_precisions_mean[min_dist_i].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='Precision, min_dist=%.3f' % min_dist, log_scale=True)
if savefigs:
dataio.save_current_figure('logprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated model precision (kappa)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 0].T, x=ratio_space, y=sigmax_space, xlabel='ratio layer two', ylabel='sigma_x', title='EM precision, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('logemprecision_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot estimated Target, nontarget and random mixture components, in multiple subplots
_, axes = plt.subplots(1, 3, figsize=(18, 6))
plt.subplots_adjust(left=0.05, right=0.97, wspace = 0.3, bottom=0.15)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Target, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[0], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 2].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Nontarget, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[1], ticks_interpolate=5)
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., 3].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='Random, min_dist=%.3f' % min_dist, log_scale=False, ax_handle=axes[2], ticks_interpolate=5)
if savefigs:
dataio.save_current_figure('em_mixtureprobs_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
# Plot Log-likelihood of Mixture model, sanity check
utils.pcolor_2d_data(result_em_fits_mean[min_dist_i, ..., -1].T, x=ratio_space, y=sigmax_space, xlabel='ratio', ylabel='sigma_x', title='EM loglik, min_dist=%.3f' % min_dist, log_scale=False)
if savefigs:
dataio.save_current_figure('em_loglik_permindist_mindist%.2f_ratiosigmax_{label}_{unique_id}.pdf' % min_dist)
if specific_plots_paper:
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.09
target_mindist_medium = 0.36
target_mindist_high = 1.5
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
## Do for each distance
# for min_dist_i in [min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i]:
for min_dist_i in xrange(enforce_min_distance_space.size):
# Plot precision
if False:
utils.plot_mean_std_area(ratio_space, result_all_precisions_mean[min_dist_i, sigmax_level_i], result_all_precisions_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Precision of recall')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
plt.ylim([0, np.max(result_all_precisions_mean[min_dist_i, sigmax_level_i] + result_all_precisions_std[min_dist_i, sigmax_level_i])])
if savefigs:
dataio.save_current_figure('mindist%.2f_precisionrecall_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa fitted
ax_handle = utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0], result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa')
# Add distance between items in kappa units
dist_items_kappa = utils.stddev_to_kappa(enforce_min_distance_space[min_dist_i])
ax_handle.plot(ratio_space, dist_items_kappa*np.ones(ratio_space.size), 'k--', linewidth=3)
plt.ylim([-0.1, np.max((np.max(result_em_fits_mean[min_dist_i, sigmax_level_i, :, 0] + result_em_fits_std[min_dist_i, sigmax_level_i, :, 0]), 1.1*dist_items_kappa))])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappa_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot kappa-stddev fitted. Easier to visualize
ax_handle = utils.plot_mean_std_area(ratio_space, result_em_kappastddev_mean[min_dist_i, sigmax_level_i], result_em_kappastddev_std[min_dist_i, sigmax_level_i]) #, xlabel='Ratio conjunctivity', ylabel='Fitted kappa_stddev')
# Add distance between items in std dev units
dist_items_std = (enforce_min_distance_space[min_dist_i])
ax_handle.plot(ratio_space, dist_items_std*np.ones(ratio_space.size), 'k--', linewidth=3)
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
plt.ylim([0, 1.1*np.max((np.max(result_em_kappastddev_mean[min_dist_i, sigmax_level_i] + result_em_kappastddev_std[min_dist_i, sigmax_level_i]), dist_items_std))])
if savefigs:
dataio.save_current_figure('mindist%.2f_emkappastddev_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
if False:
# Plot LLH
utils.plot_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, -1], result_em_fits_std[min_dist_i, sigmax_level_i, :, -1]) #, xlabel='Ratio conjunctivity', ylabel='Loglikelihood of Mixture model fit')
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
if savefigs:
dataio.save_current_figure('mindist%.2f_emllh_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Plot mixture parameters, std
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T)
plt.ylim([0.0, 1.1])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
# Mixture parameters, SEM
utils.plot_multiple_mean_std_area(ratio_space, result_em_fits_mean[min_dist_i, sigmax_level_i, :, 1:4].T, result_em_fits_std[min_dist_i, sigmax_level_i, :, 1:4].T/np.sqrt(nb_repetitions))
plt.ylim([0.0, 1.1])
# plt.title('Min distance %.3f' % enforce_min_distance_space[min_dist_i])
# plt.legend("Target", "Non-target", "Random")
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobs_forpaper_sem_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
if plots_emfit_allitems:
# We need to choose 3 levels of min_distances
target_sigmax = 0.25
target_mindist_low = 0.15
target_mindist_medium = 0.36
target_mindist_high = 1.5
sigmax_level_i = np.argmin(np.abs(sigmax_space - target_sigmax))
min_dist_level_low_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_low))
min_dist_level_medium_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_medium))
min_dist_level_high_i = np.argmin(np.abs(enforce_min_distance_space - target_mindist_high))
min_dist_i_plotting_space = np.array([min_dist_level_low_i, min_dist_level_medium_i, min_dist_level_high_i])
if should_fit_allitems_model:
# kappa, mixt_target, mixt_nontargets (K), mixt_random, LL, bic
# result_emfitallitems = np.empty((min_dist_i_plotting_space.size, ratio_space.size, 2*K+5))*np.nan
result_emfitallitems = np.empty((enforce_min_distance_space.size, ratio_space.size, K+5))*np.nan
## Do for each distance
# for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
for min_dist_i in xrange(enforce_min_distance_space.size):
# Fit the mixture model
for ratio_i, ratio in enumerate(ratio_space):
print "Refitting EM all items. Ratio:", ratio, "Dist:", enforce_min_distance_space[min_dist_i]
em_fit = em_circularmixture_allitems_uniquekappa.fit(
result_responses_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_target_all[min_dist_i, sigmax_level_i, ratio_i].flatten(),
result_nontargets_all[min_dist_i, sigmax_level_i, ratio_i].transpose((0, 2, 1)).reshape((N*nb_repetitions, K)))
result_emfitallitems[min_dist_i, ratio_i] = [em_fit['kappa'], em_fit['mixt_target']] + em_fit['mixt_nontargets'].tolist() + [em_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]
# Save everything to a file, for faster later plotting
if caching_emfit_filename is not None:
try:
with open(caching_emfit_filename, 'w') as filecache_out:
data_em = dict(result_emfitallitems=result_emfitallitems)
pickle.dump(data_em, filecache_out, protocol=2)
except IOError:
print "Error writing out to caching file ", caching_emfit_filename
## Plots now, for each distance!
# for min_dist_plotting_i, min_dist_i in enumerate(min_dist_i_plotting_space):
for min_dist_i in xrange(enforce_min_distance_space.size):
# Plot now
_, ax = plt.subplots()
ax.plot(ratio_space, result_emfitallitems[min_dist_i, :, 1:5], linewidth=3)
plt.ylim([0.0, 1.1])
plt.legend(['Target', 'Nontarget 1', 'Nontarget 2', 'Random'], loc='upper left')
if savefigs:
dataio.save_current_figure('mindist%.2f_emprobsfullitems_{label}_{unique_id}.pdf' % enforce_min_distance_space[min_dist_i])
if plot_min_distance_effect:
conj_receptive_field_size = 2.*np.pi/((all_args[0]['M']*ratio_space)**0.5)
target_vs_nontargets_mindist_ratio = result_emfitallitems[..., 1]/np.sum(result_emfitallitems[..., 1:4], axis=-1)
nontargetsmean_vs_targnontarg_mindist_ratio = np.mean(result_emfitallitems[..., 2:4]/np.sum(result_emfitallitems[..., 1:4], axis=-1)[..., np.newaxis], axis=-1)
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# Do one plot per ratio, putting the receptive field size on each
f, ax = plt.subplots()
ax.plot(enforce_min_distance_space[1:], target_vs_nontargets_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='target mixture')
ax.plot(enforce_min_distance_space[1:], nontargetsmean_vs_targnontarg_mindist_ratio[1:, ratio_conj_i], linewidth=3, label='non-target mixture')
# ax.plot(enforce_min_distance_space[1:], result_emfitallitems[1:, ratio_conj_i, 1:5], linewidth=3)
ax.axvline(x=conj_receptive_field_size[ratio_conj_i]/2., color='k', linestyle='--', linewidth=2)
ax.axvline(x=conj_receptive_field_size[ratio_conj_i]*2., color='r', linestyle='--', linewidth=2)
plt.legend(loc='upper left')
plt.grid()
# ax.set_xlabel('Stimuli separation')
# ax.set_ylabel('Ratio Target to Non-targets')
plt.axis('tight')
ax.set_ylim([0.0, 1.0])
ax.set_xlim([enforce_min_distance_space[1:].min(), enforce_min_distance_space[1:].max()])
if savefigs:
dataio.save_current_figure('ratio%.2f_mindistpred_ratiotargetnontarget_{label}_{unique_id}.pdf' % ratio_conj)
variables_to_save = ['nb_repetitions']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='specific_stimuli')
plt.show()
return locals()
def plots_3dvolume_hierarchical_M_Mlayerone(data_pbs, generator_module=None):
'''
Reload 3D volume runs from PBS and plot them
'''
#### SETUP
#
savefigs = True
savedata = False # warning, huge file created...
plots_pcolors = False
plots_singleaxe = False
plots_multipleaxes = True
plots_multipleaxes_emfits = True
load_fit_mixture_model = True
# caching_emfit_filename = None
caching_emfit_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_emfit_newshapes.pickle')
plt.rcParams['font.size'] = 16
#
#### /SETUP
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
print "Order parameters: ", generator_module.dict_parameters_range.keys()
results_precision_constant_M_Mlower = np.squeeze(utils.nanmean(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1))
results_precision_constant_M_Mlower_std = np.squeeze(utils.nanstd(data_pbs.dict_arrays['results_precision_M_T']['results'], axis=-1))
results_responses = np.squeeze(data_pbs.dict_arrays['result_responses']['results'])
results_targets = np.squeeze(data_pbs.dict_arrays['result_targets']['results'])
results_nontargets = np.squeeze(data_pbs.dict_arrays['result_nontargets']['results'])
# results_emfits_M_T = np.squeeze(data_pbs.dict_arrays['results_emfits_M_T']['results'])
M_space = data_pbs.loaded_data['parameters_uniques']['M']
M_layer_one_space = data_pbs.loaded_data['parameters_uniques']['M_layer_one']
ratio_MMlower_space = M_space/generator_module.filtering_function_parameters['target_M_total']
filtering_indices = (np.arange(M_space.size), np.arange(-M_layer_one_space.size, 0)[::-1])
T = results_precision_constant_M_Mlower.shape[-1]
T_space = np.arange(T)
N = results_nontargets.shape[-2]
num_repetitions = data_pbs.loaded_data['args_list'][0]['num_repetitions']
# Fix after @3625585, now the shape are:
# results_responses, results_targets: M_space.size, M_layer_one_space.size, T, wrong num_repet, N
# results_nontargets: M_space.size, M_layer_one_space.size, T, wrong num_repet, N, T-1
if data_pbs.loaded_data['nb_datasets_per_parameters'] > 1:
num_repetitions = data_pbs.loaded_data['nb_datasets_per_parameters']
# Filter and reshape
results_responses = results_responses[:, :, :, :num_repetitions, :]
results_responses.shape = (M_space.size, M_layer_one_space.size, T, num_repetitions*N)
results_targets = results_targets[:, :, :, :num_repetitions, :]
results_targets.shape = (M_space.size, M_layer_one_space.size, T, num_repetitions*N)
results_nontargets = results_nontargets[:, :, :, :num_repetitions, :, :]
results_nontargets.shape = (M_space.size, M_layer_one_space.size, T, num_repetitions*N, T-1)
print M_space
print M_layer_one_space
print results_precision_constant_M_Mlower.shape
# print results_precision_constant_M_Mlower
results_precision_filtered = results_precision_constant_M_Mlower[filtering_indices]
del results_precision_constant_M_Mlower
results_precision_filtered_std = results_precision_constant_M_Mlower_std[filtering_indices]
del results_precision_constant_M_Mlower_std
results_responses_filtered = results_responses[filtering_indices]
del results_responses
results_targets_filtered = results_targets[filtering_indices]
del results_targets
results_nontargets_filtered = results_nontargets[filtering_indices]
del results_nontargets
# results_emfits_M_T_filtered = results_emfits_M_T[filtering_indices]
# del results_emfits_M_T
results_precision_filtered_smoothed = np.apply_along_axis(smooth, 0, results_precision_filtered, *(10, 'bartlett'))
if load_fit_mixture_model:
# Fit the mixture model on the samples
if caching_emfit_filename is not None:
if os.path.exists(caching_emfit_filename):
# Got file, open it and try to use its contents
try:
with open(caching_emfit_filename, 'r') as file_in:
# Load and assign values
cached_data = pickle.load(file_in)
result_emfits_filtered = cached_data['result_emfits_filtered']
results_responses_sha1_loaded = cached_data.get('results_responses_sha1', '')
# Check that the sha1 is the same, if not recompute!
if results_responses_sha1_loaded == hashlib.sha1(results_responses_filtered).hexdigest():
print "Loading from cache file %s" % caching_emfit_filename
load_fit_mixture_model = False
else:
print "Tried loading from cache file %s, but data changed, recomputing..." % caching_emfit_filename
load_fit_mixture_model = True
except IOError:
print "Error while loading ", caching_emfit_filename, "falling back to computing the EM fits"
load_fit_mixture_model = False
if load_fit_mixture_model:
result_emfits_filtered = np.nan*np.empty((ratio_MMlower_space.size, T, 5))
# Fit EM model
print "fitting EM model"
for ratio_MMlower_i, ratio_MMlower in enumerate(ratio_MMlower_space):
for T_i in T_space:
if np.any(~np.isnan(results_responses_filtered[ratio_MMlower_i, T_i])):
print "ratio MM, T:", ratio_MMlower, T_i+1
curr_em_fits = em_circularmixture_allitems_uniquekappa.fit(results_responses_filtered[ratio_MMlower_i, T_i], results_targets_filtered[ratio_MMlower_i, T_i], results_nontargets_filtered[ratio_MMlower_i, T_i, :, :T_i])
curr_em_fits['mixt_nontargets_sum'] = np.sum(curr_em_fits['mixt_nontargets'])
result_emfits_filtered[ratio_MMlower_i, T_i] = [curr_em_fits[key] for key in ('kappa', 'mixt_target', 'mixt_nontargets_sum', 'mixt_random', 'train_LL')]
# Save everything to a file, for faster later plotting
if caching_emfit_filename is not None:
try:
with open(caching_emfit_filename, 'w') as filecache_out:
results_responses_sha1 = hashlib.sha1(results_responses_filtered).hexdigest()
data_emfit = dict(result_emfits_filtered=result_emfits_filtered, results_responses_sha1=results_responses_sha1)
pickle.dump(data_emfit, filecache_out, protocol=2)
print "cache file %s written" % caching_emfit_filename
except IOError:
print "Error writing out to caching file ", caching_emfit_filename
if plots_multipleaxes:
# Plot precisions with standard deviation around
f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12))
# all_lines_bis = []
for i, max_ind in enumerate(np.argmax(results_precision_filtered, axis=0)):
utils.plot_mean_std_area(ratio_MMlower_space, results_precision_filtered[:, i], results_precision_filtered_std[:, i], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color())
# curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color())
axes[i].grid()
axes[i].set_xticks(np.linspace(0., 1.0, 5))
axes[i].set_xlim((0.0, 1.0))
# axes[i].set_yticks([])
axes[i].set_ylim((np.min(results_precision_filtered[:, i]), results_precision_filtered[max_ind, i]*1.1))
axes[i].locator_params(axis='y', tight=True, nbins=4)
# all_lines_bis.extend(curr_lines)
f.subplots_adjust(right=0.75)
# plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5))
if savefigs:
dataio.save_current_figure('results_subplots_1dnorm_{label}_global_{unique_id}.pdf')
if plots_multipleaxes_emfits:
f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12))
all_lines_bis = []
for i, max_ind in enumerate(np.nanargmax(result_emfits_filtered[..., 0], axis=0)):
# Plot Target mixture
utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 1:4], 0*result_emfits_filtered[:, i, 1:4], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color())
# curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color())
axes[i].grid()
axes[i].set_xticks(np.linspace(0., 1.0, 5))
axes[i].set_xlim((0.0, 1.0))
# axes[i].set_yticks([])
# axes[i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1))
axes[i].set_ylim((0.0, 1.05))
axes[i].locator_params(axis='y', tight=True, nbins=4)
# all_lines_bis.extend(curr_lines)
if savefigs:
dataio.save_current_figure('results_subplots_emtarget_{label}_global_{unique_id}.pdf')
f, axes = plt.subplots(nrows=T, ncols=1, sharex=True, figsize=(10, 12))
for i, max_ind in enumerate(np.nanargmax(result_emfits_filtered[..., 0], axis=0)):
# Plot kappa mixture
utils.plot_mean_std_area(ratio_MMlower_space, result_emfits_filtered[:, i, 0], 0*result_emfits_filtered[:, i, 0], ax_handle=axes[i], linewidth=2) #, color=all_lines[i].get_color())
# curr_lines = axes[i].plot(ratio_MMlower_space, results_precision_filtered[:, i], linewidth=2, color=all_lines[i].get_color())
axes[i].grid()
axes[i].set_xticks(np.linspace(0., 1.0, 5))
axes[i].set_xlim((0.0, 1.0))
# axes[i].set_yticks([])
# axes[i].set_ylim((np.min(result_emfits_filtered[:, i, 0]), result_emfits_filtered[max_ind, i, 0]*1.1))
# axes[i].set_ylim((0.0, 1.0))
axes[i].locator_params(axis='y', tight=True, nbins=4)
# all_lines_bis.extend(curr_lines)
# f.subplots_adjust(right=0.75)
# plt.figlegend(all_lines_bis, ['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='right', bbox_to_anchor=(1.0, 0.5))
if savefigs:
dataio.save_current_figure('results_subplots_emkappa_{label}_global_{unique_id}.pdf')
variables_to_save = []
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='hierarchicalrandomnetwork_characterisation')
plt.show()
return locals()
def plots_fit_mixturemodels_random(data_pbs, generator_module=None):
'''
Reload runs from PBS
'''
#### SETUP
#
savefigs = True
savedata = True
savemovies = False
plots_dist_bays09 = True
do_scatters_3d = True
do_best_points_extended_plots = 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
result_em_fits_flat = np.array(data_pbs.dict_arrays['result_em_fits']['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_parameters_flat = np.array(data_pbs.dict_arrays['result_em_fits']['parameters_flat'])
M_space = data_pbs.loaded_data['parameters_uniques']['M']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
ratio_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
num_repetitions = generator_module.num_repetitions
parameter_names_sorted = data_pbs.dataset_infos['parameters']
T_space = data_pbs.loaded_data['datasets_list'][0]['T_space']
all_args = data_pbs.loaded_data['args_list']
all_repeats_completed = data_pbs.dict_arrays['result_em_fits']['repeats_completed']
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:]
# All parameters info
plotting_parameters = launcher_memorycurve.load_prepare_datasets()
## Compute some stuff
# result_dist_bays09_kappa_T1_avg = utils.nanmean(result_dist_bays09_flat[:, 0, 0], axis=-1)
# result_dist_bays09_kappa_allT_avg = np.nansum(utils.nanmean(result_dist_bays09_flat[:, :, 0], axis=-1), axis=1)
# Square distance to kappa
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_sum_masked = np.ma.masked_greater(result_dist_bays09_kappa_sum, 1e8)
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)
result_dist_bays09_kappaKL_normalised_summed = result_dist_bays09_emmixt_KL_sum/np.max(result_dist_bays09_emmixt_KL_sum) + result_dist_bays09_kappa_sum/np.max(result_dist_bays09_kappa_sum)
if plots_dist_bays09:
nb_best_points = 30
size_normal_points = 8
size_best_points = 50
nb_best_points_extended_plots = 3
def plot_memorycurve(result_em_fits, args_used, suptitle=''):
packed_data = dict(T_space=T_space, result_em_fits=result_em_fits, all_parameters=args_used)
if suptitle:
plotting_parameters['suptitle'] = suptitle
if savefigs:
packed_data['dataio'] = dataio
plotting_parameters['reuse_axes'] = False
launcher_memorycurve.do_memory_plots(packed_data, plotting_parameters)
def plot_scatter(result_dist_to_use, best_points_result_dist_to_use, result_dist_to_use_name='', title=''):
fig = plt.figure()
ax = Axes3D(fig)
utils.scatter3d(result_parameters_flat[:, 0], result_parameters_flat[:, 1], result_parameters_flat[:, 2], s=size_normal_points, c=np.log(result_dist_to_use), xlabel=parameter_names_sorted[0], ylabel=parameter_names_sorted[1], zlabel=parameter_names_sorted[2], title=title, ax_handle=ax)
utils.scatter3d(result_parameters_flat[best_points_result_dist_to_use, 0], result_parameters_flat[best_points_result_dist_to_use, 1], result_parameters_flat[best_points_result_dist_to_use, 2], c='r', s=size_best_points, ax_handle=ax)
print "Best points, %s:" % title
print '\n'.join(['M %d, ratio %.2f, sigmax %.2f: %f' % (result_parameters_flat[i, 0], result_parameters_flat[i, 1], result_parameters_flat[i, 2], result_dist_to_use[i]) for i in best_points_result_dist_to_use])
if savefigs:
dataio.save_current_figure('scatter3d_%s_{label}_{unique_id}.pdf' % result_dist_to_use_name)
if savemovies:
try:
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_{label}_{unique_id}.mp4' % result_dist_to_use_name), bitrate=8000, min_duration=8)
utils.rotate_plot3d(ax, dataio.create_formatted_filename('scatter3d_%s_{label}_{unique_id}.gif' % result_dist_to_use_name), nb_frames=30, min_duration=8)
except Exception:
# Most likely wrong aggregator...
print "failed when creating movies for ", result_dist_to_use_name
ax.view_init(azim=90, elev=10)
dataio.save_current_figure('scatter3d_%s_view2_{label}_{unique_id}.pdf' % result_dist_to_use_name)
return ax
def plots_redirects(all_vars, result_dist_to_use_name, log_color=True, title='', avoid_incomplete_repeats=True):
result_dist_to_use = all_vars[result_dist_to_use_name]
if avoid_incomplete_repeats:
result_dist_to_use = np.ma.masked_where(~(all_repeats_completed == num_repetitions-1), result_dist_to_use)
if not log_color:
result_dist_to_use = np.exp(result_dist_to_use)
best_points_result_dist_to_use = np.argsort(result_dist_to_use)[:nb_best_points]
# Scatter
if do_scatters_3d:
plot_scatter(result_dist_to_use, best_points_result_dist_to_use, result_dist_to_use_name, title=title)
# Now do the additional plots if required
if do_best_points_extended_plots:
for best_point_index in best_points_result_dist_to_use[:nb_best_points_extended_plots]:
print "extended plot for M %d, ratio %.2f, sigmax %.2f: score %f" % (result_parameters_flat[best_point_index, 0], result_parameters_flat[best_point_index, 1], result_parameters_flat[best_point_index, 2], result_dist_to_use[best_point_index])
plot_memorycurve(result_em_fits_flat[best_point_index], all_args[best_point_index], suptitle=result_dist_to_use_name)
# Distance for kappa, all T
plots_redirects(locals(), 'result_dist_bays09_kappa_sum', title='kappa all T')
# Distance for em fits, all T, Squared distance
plots_redirects(locals(), 'result_dist_bays09_emmixt_sum', title='em fits, all T')
# Distance for em fits, all T, KL distance
plots_redirects(locals(), 'result_dist_bays09_emmixt_KL_sum', title='em fits, all T, KL')
# Distance for sum of normalised em fits + normalised kappa, all T
plots_redirects(locals(), 'result_dist_bays09_both_normalised', title='summed normalised em mixt + kappa')
# Distance kappa T = 1
plots_redirects(locals(), 'result_dist_bays09_kappa_T1_sum', title='Kappa T=1')
# Distance kappa T = 2...5
plots_redirects(locals(), 'result_dist_bays09_kappa_T25_sum', title='Kappa T=2/5')
# Distance em fits T = 1
plots_redirects(locals(), 'result_dist_bays09_emmixt_T1_sum', title='em fits T=1')
# Distance em fits T = 2...5
plots_redirects(locals(), 'result_dist_bays09_emmixt_T25_sum', title='em fits T=2/5')
# Distance em fits T = 1, KL
plots_redirects(locals(), 'result_dist_bays09_emmixt_KL_T1_sum', title='em fits T=1, KL')
# Distance em fits T = 2...5, KL
plots_redirects(locals(), 'result_dist_bays09_emmixt_KL_T25_sum', title='em fits T=2/5, KL')
# if plots_per_T:
# for T in T_space:
# currT_indices = result_parameters_flat[:, 2] == T
# utils.contourf_interpolate_data_interactive_maxvalue(result_parameters_flat[currT_indices][..., :2], result_fitexperiments_bic_avg[currT_indices], xlabel='Ratio_conj', ylabel='sigma x', title='BIC, T %d' % T, interpolation_numpoints=200, interpolation_method='nearest', log_scale=False)
# # 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))
variables_to_save = ['parameter_names_sorted', 'all_repeats_completed', 'T_space']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='fit_mixturemodels')
plt.show()
return locals()
def plots_memory_curves(data_pbs, generator_module=None):
'''
Reload and plot memory curve of a feature code.
Can use Marginal Fisher Information and fitted Mixture Model as well
'''
#### SETUP
#
savefigs = True
savedata = True
plot_pcolor_fit_precision_to_fisherinfo = True
plot_selected_memory_curves = True
colormap = None # or 'cubehelix'
plt.rcParams['font.size'] = 16
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_precisions_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_all_precisions_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_all_precisions']['results']), axis=-1)
result_em_fits_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_em_fits_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_em_fits']['results']), axis=-1)
result_marginal_inv_fi_mean = utils.nanmean(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
result_marginal_inv_fi_std = utils.nanstd(np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
result_marginal_fi_mean = utils.nanmean(1./np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
result_marginal_fi_std = utils.nanstd(1./np.squeeze(data_pbs.dict_arrays['result_marginal_inv_fi']['results']), axis=-1)
M_space = data_pbs.loaded_data['parameters_uniques']['M']
sigmax_space = data_pbs.loaded_data['parameters_uniques']['sigmax']
T_space = data_pbs.loaded_data['datasets_list'][0]['T_space']
print M_space
print sigmax_space
print T_space
print result_all_precisions_mean.shape, result_em_fits_mean.shape, result_marginal_inv_fi_mean.shape
dataio = DataIO.DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
## Load Experimental data
data_simult = load_experimental_data.load_data_simult(data_dir=os.path.normpath(os.path.join(os.path.split(load_experimental_data.__file__)[0], '../../experimental_data/')))
memory_experimental = data_simult['precision_nitems_theo']
# Compute some landscapes of fit!
dist_diff_precision_margfi = np.sum(np.abs(result_all_precisions_mean*2. - result_marginal_fi_mean[..., 0])**2., axis=-1)
dist_diff_precision_margfi_1item = np.abs(result_all_precisions_mean[..., 0]*2. - result_marginal_fi_mean[..., 0, 0])**2.
dist_diff_all_precision_margfi = np.abs(result_all_precisions_mean*2. - result_marginal_fi_mean[..., 0])**2.
dist_ratio_precision_margfi = np.sum(np.abs((result_all_precisions_mean*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1)
dist_diff_emkappa_margfi = np.sum(np.abs(result_em_fits_mean[..., 0]*2. - result_marginal_fi_mean[..., 0])**2., axis=-1)
dist_ratio_emkappa_margfi = np.sum(np.abs((result_em_fits_mean[..., 0]*2.)/result_marginal_fi_mean[..., 0] - 1.0)**2., axis=-1)
dist_diff_precision_experim = np.sum(np.abs(result_all_precisions_mean - memory_experimental)**2., axis=-1)
dist_diff_emkappa_experim = np.sum(np.abs(result_em_fits_mean[..., 0] - memory_experimental)**2., axis=-1)
if plot_pcolor_fit_precision_to_fisherinfo:
# Check fit between precision and fisher info
utils.pcolor_2d_data(dist_diff_precision_margfi, log_scale=True, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax')
if savefigs:
dataio.save_current_figure('match_precision_margfi_log_pcolor_{label}_{unique_id}.pdf')
# utils.pcolor_2d_data(dist_diff_precision_margfi, x=M_space, y=sigmax_space[2:], xlabel='M', ylabel='sigmax')
# if savefigs:
# dataio.save_current_figure('match_precision_margfi_pcolor_{label}_{unique_id}.pdf')
utils.pcolor_2d_data(dist_ratio_precision_margfi[4:], x=M_space[4:], y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True)
utils.pcolor_2d_data(dist_diff_emkappa_margfi, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True)
utils.pcolor_2d_data(dist_ratio_emkappa_margfi[4:], x=M_space[4:], y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True)
utils.pcolor_2d_data(dist_diff_precision_experim, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True)
utils.pcolor_2d_data(dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True)
utils.pcolor_2d_data(dist_diff_precision_margfi*dist_diff_emkappa_margfi*dist_diff_precision_experim*dist_diff_emkappa_experim, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax', log_scale=True)
utils.pcolor_2d_data(dist_diff_precision_margfi_1item, log_scale=True, x=M_space, y=sigmax_space, xlabel='M', ylabel='sigmax')
if plot_selected_memory_curves:
selected_values = [[20, 0.19], [40, 0.15], [100, 0.44], [140, 0.15], [40, 0.17], [60, 0.50]]
for current_values in selected_values:
# Find the indices
M_i = np.argmin(np.abs(current_values[0] - M_space))
sigmax_i = np.argmin(np.abs(current_values[1] - sigmax_space))
ax = utils.plot_mean_std_area(T_space, memory_experimental, np.zeros(T_space.size), linewidth=3, fmt='o-', markersize=8)
ax = utils.plot_mean_std_area(T_space, result_all_precisions_mean[M_i, sigmax_i], result_all_precisions_std[M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8)
ax = utils.plot_mean_std_area(T_space, 0.5*result_marginal_fi_mean[..., 0][M_i, sigmax_i], 0.5*result_marginal_fi_std[..., 0][M_i, sigmax_i], ax_handle=ax, linewidth=3, fmt='o-', markersize=8)
ax = utils.plot_mean_std_area(T_space, result_em_fits_mean[..., 0][M_i, sigmax_i], result_em_fits_std[..., 0][M_i, sigmax_i], ax_handle=ax, xlabel='Number of items', ylabel='Kappa/Inverse variance', linewidth=3, fmt='o-', markersize=8)
ax.set_title('M %d, sigmax %.2f' % (M_space[M_i], sigmax_space[sigmax_i]))
plt.legend(['Experimental data', 'Precision of samples', 'Marginal Fisher Information', 'Fitted kappa'])
ax.set_xlim([0.9, 5.1])
ax.set_xticks(range(1, 6))
ax.set_xticklabels(range(1, 6))
if savefigs:
dataio.save_current_figure('memorycurves_M%dsigmax%.2f_{label}_{unique_id}.pdf' % (M_space[M_i], sigmax_space[sigmax_i]))
all_args = data_pbs.loaded_data['args_list']
variables_to_save = ['result_all_precisions_mean', 'result_em_fits_mean', 'result_marginal_inv_fi_mean', 'result_all_precisions_std', 'result_em_fits_std', 'result_marginal_inv_fi_std', 'result_marginal_fi_mean', 'result_marginal_fi_std', 'M_space', 'sigmax_space', 'T_space', 'all_args']
if savedata:
dataio.save_variables(variables_to_save, locals())
plt.show()
return locals()
def fit_transform(self, X, verbose=True, report_interval=1, X_true=None):
"""
Fits the imputer on a dataset with missing data, and returns the
imputations.
Parameters
----------
X : torch.DoubleTensor or torch.cuda.DoubleTensor, shape (n, d)
Contains non-missing and missing data at the indices given by the
"mask" argument. Missing values can be arbitrarily assigned
(e.g. with NaNs).
mask : torch.DoubleTensor or torch.cuda.DoubleTensor, shape (n, d)
mask[i,j] == 1 if X[i,j] is missing, else mask[i,j] == 0.
verbose : bool, default=True
If True, output loss to log during iterations.
report_interval : int, default=1
Interval between loss reports (if verbose).
X_true: torch.DoubleTensor or None, default=None
Ground truth for the missing values. If provided, will output a
validation score during training. For debugging only.
Returns
-------
X_filled: torch.DoubleTensor or torch.cuda.DoubleTensor
Imputed missing data (plus unchanged non-missing data).
"""
X = X.clone()
n, d = X.shape
mask = torch.isnan(X).double()
normalized_tol = self.tol * torch.max(torch.abs(X[~mask.bool()]))
if self.batchsize > n // 2:
e = int(np.log2(n // 2))
self.batchsize = 2**e
if verbose:
logging.info(
f"Batchsize larger that half size = {len(X) // 2}."
f" Setting batchsize to {self.batchsize}.")
order_ = torch.argsort(mask.sum(0))
optimizers = [
self.opt(self.models[i].parameters(),
lr=self.lr,
weight_decay=self.weight_decay) for i in range(d)
]
imps = (self.noise * torch.randn(mask.shape).double() +
nanmean(X, 0))[mask.bool()]
X[mask.bool()] = imps
X_filled = X.clone()
if X_true is not None:
maes = np.zeros(self.max_iter)
rmses = np.zeros(self.max_iter)
for i in range(self.max_iter):
if self.order == 'random':
order_ = np.random.choice(d, d, replace=False)
X_old = X_filled.clone().detach()
loss = 0
for l in range(d):
j = order_[l].item()
n_not_miss = (~mask[:, j].bool()).sum().item()
if n - n_not_miss == 0:
continue # no missing value on that coordinate
for k in range(self.niter):
loss = 0
X_filled = X_filled.detach()
X_filled[mask[:, j].bool(), j] = self.models[j](
X_filled[mask[:,
j].bool(), :][:,
np.r_[0:j,
j + 1:d]]).squeeze()
for _ in range(self.n_pairs):
idx1 = np.random.choice(n,
self.batchsize,
replace=False)
X1 = X_filled[idx1]
if self.unsymmetrize:
n_miss = (~mask[:, j].bool()).sum().item()
idx2 = np.random.choice(
n_miss,
self.batchsize,
replace=self.batchsize > n_miss)
X2 = X_filled[~mask[:, j].bool(), :][idx2]
else:
idx2 = np.random.choice(n,
self.batchsize,
replace=False)
X2 = X_filled[idx2]
loss = loss + self.sk(X1, X2)
optimizers[j].zero_grad()
loss.backward()
optimizers[j].step()
# Impute with last parameters
with torch.no_grad():
X_filled[mask[:, j].bool(), j] = self.models[j](
X_filled[mask[:,
j].bool(), :][:,
np.r_[0:j,
j + 1:d]]).squeeze()
if X_true is not None:
maes[i] = MAE(X_filled, X_true, mask).item()
rmses[i] = RMSE(X_filled, X_true, mask).item()
if verbose and (i % report_interval == 0):
if X_true is not None:
logging.info(
f'Iteration {i}:\t Loss: {loss.item() / self.n_pairs:.4f}\t'
f'Validation MAE: {maes[i]:.4f}\t'
f'RMSE: {rmses[i]: .4f}')
else:
logging.info(
f'Iteration {i}:\t Loss: {loss.item() / self.n_pairs:.4f}'
)
if torch.norm(X_filled - X_old, p=np.inf) < normalized_tol:
break
if i == (self.max_iter - 1) and verbose:
logging.info('Early stopping criterion not reached')
self.is_fitted = True
if X_true is not None:
return X_filled, maes, rmses
else:
return X_filled
