def compute_optimal_rcscale(cls, M, R, population_code_type='conjunctive'):
'''
Compute the optimal rcscale, depending on the type of code we use.
'''
if population_code_type == 'conjunctive':
# We use the optimum heuristic for the rc_scale: try to cover the space fully, assuming uniform coverage with squares of size 2*(2*utils.kappa_to_stddev(kappa)). We assume that 2*stddev gives a good approximation to the appropriate coverage required.
return utils.stddev_to_kappa(2. * np.pi / int(M**(1. / R)))
elif population_code_type == 'feature':
return utils.stddev_to_kappa(2. * np.pi / int(M / R))
def create_full_features(cls,
M,
R=2,
scale=0.3,
ratio=40.,
autoset_parameters=False,
response_maxout=False):
'''
Create a RandomFactorialNetwork instance, using a pure conjunctive code
'''
print "create feature network, R=%d, M=%d, autoset: %d" % (
R, M, autoset_parameters)
rn = HighDimensionNetwork(M, R=R, response_maxout=response_maxout)
if autoset_parameters:
# Use optimal values for the parameters. Be careful, this assumes M/2 and coverage of full 2 pi space
# Assume one direction should cover width = pi, the other should cover M/2 * width/2. = 2pi
# width = utils.stddev_to_kappa(stddev)
scale = utils.stddev_to_kappa(np.pi)
scale2 = cls.compute_optimal_rcscale(
M, R, population_code_type='feature')
ratio = scale2 / scale
else:
if ratio < 0.0:
# Setting ratio < 0 cause some mid-automatic parameter setting.
# Assume that only one scale is really desired, and the other automatically set.
scale_fixed = utils.stddev_to_kappa(np.pi)
ratio = np.max((scale / scale_fixed, scale_fixed / scale))
print "Semi auto ratio: %f %f %f" % (scale, scale_fixed, ratio)
M_sub = M / rn.R
# Assign centers
rn.assign_prefered_stimuli(tiling_type='features', reset=True)
# Now assign scales
resetted = False
for r in xrange(rn.R):
rn.assign_aligned_eigenvectors(
scale=scale,
ratio=ratio,
scaled_dimension=r,
specific_neurons=np.arange(r * M_sub, (r + 1) * M_sub),
reset=not resetted)
resetted = True
rn.population_code_type = 'feature'
return rn
def create_mixed(cls, M, R=2, ratio_feature_conjunctive=0.5, conjunctive_parameters=None, feature_parameters=None, autoset_parameters=False, response_maxout=False):
'''
Create a RandomFactorialNetwork instance, using a pure conjunctive code
'''
print "Create mixed network, R=%d autoset: %d" % (R, autoset_parameters)
conj_scale = 1.0
feat_scale = 0.3
feat_ratio = 40.0
if conjunctive_parameters is not None:
# Heavily refactored, but keeps compatibility...
conj_scale = conjunctive_parameters['scale']
if feature_parameters is not None:
feat_scale = feature_parameters['scale']
feat_ratio = feature_parameters['ratio']
# nb_feature_centers = feature_parameters.get('nb_feature_centers', 1)
rn = HighDimensionNetwork(M, R=R, response_maxout=response_maxout)
rn.conj_subpop_size = int(M*ratio_feature_conjunctive)
rn.feat_subpop_size = M - rn.conj_subpop_size
if autoset_parameters:
# Use optimal values for the parameters. Be careful, this assumes M/2 and coverage of full 2 pi space
# Assume one direction should cover width = pi, the other should cover M/2 * width/2. = 2pi
# width = utils.stddev_to_kappa(stddev)
if rn.conj_subpop_size > 0:
conj_scale = cls.compute_optimal_rcscale(rn.conj_subpop_size, R, population_code_type='conjunctive')
if rn.feat_subpop_size > 0:
feat_scale = utils.stddev_to_kappa(np.pi)
feat_ratio = cls.compute_optimal_rcscale(rn.feat_subpop_size, R, population_code_type='feature')/feat_scale
print "Population sizes: ratio: %.1f conj: %d, feat: %d, autoset: %d" % (ratio_feature_conjunctive, rn.conj_subpop_size, rn.feat_subpop_size, autoset_parameters)
# Create the conjunctive subpopulation
rn.assign_prefered_stimuli(tiling_type='conjunctive', reset=True, specific_neurons=np.arange(rn.conj_subpop_size))
rn.assign_aligned_eigenvectors(scale=conj_scale, ratio=1.0, specific_neurons=np.arange(rn.conj_subpop_size), reset=True)
# Create the feature subpopulation
# Assign centers
rn.assign_prefered_stimuli(tiling_type='features', specific_neurons=np.arange(rn.conj_subpop_size, M))
# Now assign scales
feat_sub_M = rn.feat_subpop_size/rn.R
for r in xrange(rn.R):
rn.assign_aligned_eigenvectors(scale=feat_scale, ratio=feat_ratio, scaled_dimension=r, specific_neurons=np.arange(rn.conj_subpop_size + r*feat_sub_M, rn.conj_subpop_size + (r+1)*feat_sub_M))
rn.population_code_type = 'mixed'
rn.ratio_conj = ratio_feature_conjunctive
return rn
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 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 = 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_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_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 receptivesize_effect_plots(variables_launcher_running, plotting_parameters):
'''
Do some plots (possibly live) with outputs from launcher_do_receptivesize_effect
'''
### Load the experimental data for the plots
# data_gorgo11 = load_experimental_data.load_data_gorgo11(fit_mixture_model=True)
# gorgo11_T_space = data_gorgo11['data_to_fit']['n_items']
# gorgo11_emfits_meanstd = data_gorgo11['em_fits_nitems_arrays']
# gorgo11_emfits_meanstd['mean'][2, 0] = 0.0
# gorgo11_emfits_meanstd['std'][2, 0] = 0.0
# data_bays09 = load_experimental_data.load_data_bays09(fit_mixture_model=True)
# bays09_T_space = data_bays09['data_to_fit']['n_items']
# bays09_emfits_meanstd = data_bays09['em_fits_nitems_arrays']
# Compute the "optimal" rcscale
if variables_launcher_running['all_parameters']['code_type'] == 'conj':
optimal_scale = utils.stddev_to_kappa(2.*np.pi/int(variables_launcher_running['all_parameters']['M']**0.5))
optimal_scale_corrected = utils.stddev_to_kappa(np.pi/(int(variables_launcher_running['all_parameters']['M']**0.5)))
elif variables_launcher_running['all_parameters']['code_type'] == 'feat':
optimal_scale = utils.stddev_to_kappa(2.*np.pi/int(variables_launcher_running['all_parameters']['M']/2.))
optimal_scale_corrected = utils.stddev_to_kappa(np.pi/int(variables_launcher_running['all_parameters']['M']/2.))
else:
optimal_scale = 0.0
### Now do the plots
current_axes = plotting_parameters['axes']
dataio = variables_launcher_running['dataio']
rcscale_space = variables_launcher_running['rcscale_space']
result_precision_stats = utils.nanstats(variables_launcher_running['result_all_precisions'], axis=-1)
result_em_fits_stats = utils.nanstats(variables_launcher_running['result_em_fits'], axis=-1)
result_marginal_fi_stats = utils.nanstats(variables_launcher_running['result_marginal_inv_fi'][:, 2], axis=-1)
plt.ion()
# Precision wrt rcscale_space
def plot_precision_rcscale(ax=None):
if ax is not None:
plt.figure(ax.get_figure().number)
ax.hold(False)
# Curve of precision evolution.
ax = utils.plot_mean_std_area(rcscale_space, result_precision_stats['mean'], result_precision_stats['std'], linewidth=3, fmt='o-', markersize=8, label='Precision', ax_handle=ax)
ax.hold(True)
ax.axvline(x=optimal_scale, color='r', linewidth=3)
ax.axvline(x=optimal_scale_corrected, color='k', linewidth=3)
ax.legend()
ax.set_title("Precision {code_type} {M} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']))
ax.set_xlim(rcscale_space.min(), rcscale_space.max())
ax.set_ylim(bottom=0.0)
ax.get_figure().canvas.draw()
dataio.save_current_figure('precision_rcscale_{code_type}_M{M}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']))
return ax
# Precision wrt rcscale_space
def plot_fisherinfo_rcscale(ax=None):
if ax is not None:
plt.figure(ax.get_figure().number)
ax.hold(False)
# Curve of precision evolution.
ax = utils.plot_mean_std_area(rcscale_space, result_marginal_fi_stats['mean'], result_marginal_fi_stats['std'], linewidth=3, fmt='o-', markersize=8, label='Fisher info', ax_handle=ax)
ax.hold(True)
ax.axvline(x=optimal_scale, color='r', linewidth=3)
ax.axvline(x=optimal_scale_corrected, color='k', linewidth=3)
ax.legend()
ax.set_title("FI {code_type} {M} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']))
ax.set_xlim(rcscale_space.min(), rcscale_space.max())
ax.set_ylim(bottom=0.0)
ax.get_figure().canvas.draw()
dataio.save_current_figure('fi_rcscale_{code_type}_M{M}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']))
return ax
# Memory curve kappa
def plot_kappa_rcscale(ax=None):
if ax is not None:
plt.figure(ax.get_figure().number)
ax.hold(False)
ax = utils.plot_mean_std_area(rcscale_space, result_em_fits_stats['mean'][..., 0], result_em_fits_stats['std'][..., 0], linewidth=3, fmt='o-', markersize=8, label='Memory error $[rad^{-2}]$', ax_handle=ax)
ax.hold(True)
ax.axvline(x=optimal_scale, color='r', linewidth=3)
ax.axvline(x=optimal_scale_corrected, color='k', linewidth=3)
ax.legend()
ax.set_xlim(rcscale_space.min(), rcscale_space.max())
ax.set_ylim(bottom=0.0)
ax.get_figure().canvas.draw()
ax.set_title("kappa {code_type} {M} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']))
dataio.save_current_figure('kappa_rcscale_{code_type}_M{M}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']))
return ax
# Plot EM Mixtures proportions
def plot_mixtures_rcscale(ax=None):
if ax is None:
_, ax = plt.subplots()
if ax is not None:
plt.figure(ax.get_figure().number)
ax.hold(False)
result_em_fits_stats['mean'][np.isnan(result_em_fits_stats['mean'])] = 0.0
result_em_fits_stats['std'][np.isnan(result_em_fits_stats['std'])] = 0.0
utils.plot_mean_std_area(rcscale_space, result_em_fits_stats['mean'][..., 1], result_em_fits_stats['std'][..., 1], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Target')
ax.hold(True)
utils.plot_mean_std_area(rcscale_space, result_em_fits_stats['mean'][..., 2], result_em_fits_stats['std'][..., 2], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Nontarget')
utils.plot_mean_std_area(rcscale_space, result_em_fits_stats['mean'][..., 3], result_em_fits_stats['std'][..., 3], xlabel='Number of items', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='o-', markersize=5, label='Random')
ax.axvline(x=optimal_scale, color='r', linewidth=3)
ax.axvline(x=optimal_scale_corrected, color='k', linewidth=3)
ax.set_xlim(rcscale_space.min(), rcscale_space.max())
ax.set_ylim(bottom=0.0, top=1.0)
ax.legend(prop={'size':15})
ax.set_title("mixts {code_type} {M} {sigmax:.3f} {sigmay:.2f}".format(**variables_launcher_running['all_parameters']))
ax.get_figure().canvas.draw()
dataio.save_current_figure('em_mixts_rcscale_{code_type}_M{M}_sigmax{sigmax}_sigmay{sigmay}_{{label}}_{{unique_id}}.pdf'.format(**variables_launcher_running['all_parameters']))
return ax
# Do all plots for all datasets
current_axes['precision_rcscale'] = plot_precision_rcscale(ax=current_axes.get('precision_rcscale', None))
current_axes['fisherinfo_rcscale'] = plot_fisherinfo_rcscale(ax=current_axes.get('fisherinfo_rcscale', None))
current_axes['kappa_rcscale'] = plot_kappa_rcscale(ax=current_axes.get('kappa_rcscale', None))
current_axes['mixtures_rcscale'] = plot_mixtures_rcscale(ax=current_axes.get('mixtures_rcscale', None))
def plots_misbinding_logposterior(data_pbs, generator_module=None):
'''
Reload 3D volume runs from PBS and plot them
'''
#### SETUP
#
savedata = False
savefigs = True
plot_logpost = False
plot_error = False
plot_mixtmodel = True
plot_hist_responses_fisherinfo = True
compute_plot_bootstrap = False
compute_fisher_info_perratioconj = True
# mixturemodel_to_use = 'original'
mixturemodel_to_use = 'allitems'
# mixturemodel_to_use = 'allitems_kappafi'
caching_fisherinfo_filename = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'cache_fisherinfo.pickle')
#
#### /SETUP
print "Order parameters: ", generator_module.dict_parameters_range.keys()
result_all_log_posterior = np.squeeze(data_pbs.dict_arrays['result_all_log_posterior']['results'])
result_all_thetas = np.squeeze(data_pbs.dict_arrays['result_all_thetas']['results'])
ratio_space = data_pbs.loaded_data['parameters_uniques']['ratio_conj']
print ratio_space
print result_all_log_posterior.shape
N = result_all_thetas.shape[-1]
result_prob_wrong = np.zeros((ratio_space.size, N))
result_em_fits = np.empty((ratio_space.size, 6))*np.nan
all_args = data_pbs.loaded_data['args_list']
fixed_means = [-np.pi*0.6, np.pi*0.6]
all_angles = np.linspace(-np.pi, np.pi, result_all_log_posterior.shape[-1])
dataio = DataIO(output_folder=generator_module.pbs_submission_infos['simul_out_dir'] + '/outputs/', label='global_' + dataset_infos['save_output_filename'])
plt.rcParams['font.size'] = 18
if plot_hist_responses_fisherinfo:
# From cache
if caching_fisherinfo_filename is not None:
if os.path.exists(caching_fisherinfo_filename):
# Got file, open it and try to use its contents
try:
with open(caching_fisherinfo_filename, 'r') as file_in:
# Load and assign values
cached_data = pickle.load(file_in)
result_fisherinfo_ratio = cached_data['result_fisherinfo_ratio']
compute_fisher_info_perratioconj = False
except IOError:
print "Error while loading ", caching_fisherinfo_filename, "falling back to computing the Fisher Info"
if compute_fisher_info_perratioconj:
# We did not save the Fisher info, but need it if we want to fit the mixture model with fixed kappa. So recompute them using the args_dicts
result_fisherinfo_ratio = np.empty(ratio_space.shape)
# Invert the all_args_i -> ratio_conj direction
parameters_indirections = data_pbs.loaded_data['parameters_dataset_index']
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# Get index of first dataset with the current ratio_conj (no need for the others, I think)
arg_index = parameters_indirections[(ratio_conj,)][0]
# Now using this dataset, reconstruct a RandomFactorialNetwork and compute the fisher info
curr_args = all_args[arg_index]
curr_args['stimuli_generation'] = lambda T: np.linspace(-np.pi*0.6, np.pi*0.6, T)
(random_network, data_gen, stat_meas, sampler) = launchers.init_everything(curr_args)
# Theo Fisher info
result_fisherinfo_ratio[ratio_conj_i] = sampler.estimate_fisher_info_theocov()
del curr_args['stimuli_generation']
# Save everything to a file, for faster later plotting
if caching_fisherinfo_filename is not None:
try:
with open(caching_fisherinfo_filename, 'w') as filecache_out:
data_cache = dict(result_fisherinfo_ratio=result_fisherinfo_ratio)
pickle.dump(data_cache, filecache_out, protocol=2)
except IOError:
print "Error writing out to caching file ", caching_fisherinfo_filename
# Now plots. Do histograms of responses (around -pi/6 and pi/6), add Von Mises derived from Theo FI on top, and vertical lines for the correct target/nontarget angles.
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# Histogram
ax = utils.hist_angular_data(result_all_thetas[ratio_conj_i], bins=100, title='ratio %.2f, fi %.0f' % (ratio_conj, result_fisherinfo_ratio[ratio_conj_i]))
bar_heights, _, _ = utils.histogram_binspace(result_all_thetas[ratio_conj_i], bins=100, norm='density')
# Add Fisher info prediction on top
x = np.linspace(-np.pi, np.pi, 1000)
if result_fisherinfo_ratio[ratio_conj_i] < 700:
# Von Mises PDF
utils.plot_vonmises_pdf(x, utils.stddev_to_kappa(1./result_fisherinfo_ratio[ratio_conj_i]**0.5), mu=fixed_means[-1], ax_handle=ax, linewidth=3, color='r', scale=np.max(bar_heights), fmt='-')
else:
# Switch to Gaussian instead
utils.plot_normal_pdf(x, mu=fixed_means[-1], std=1./result_fisherinfo_ratio[ratio_conj_i]**0.5, ax_handle=ax, linewidth=3, color='r', scale=np.max(bar_heights), fmt='-')
# ax.set_xticks([])
# ax.set_yticks([])
# Add vertical line to correct target/nontarget
ax.axvline(x=fixed_means[0], color='g', linewidth=2)
ax.axvline(x=fixed_means[1], color='r', linewidth=2)
ax.get_figure().canvas.draw()
if savefigs:
# plt.tight_layout()
dataio.save_current_figure('results_misbinding_histresponses_vonmisespdf_ratioconj%.2f{label}_{unique_id}.pdf' % (ratio_conj))
if plot_logpost:
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
# ax = utils.plot_mean_std_area(all_angles, nanmean(result_all_log_posterior[ratio_conj_i], axis=0), nanstd(result_all_log_posterior[ratio_conj_i], axis=0))
# ax.set_xlim((-np.pi, np.pi))
# ax.set_xticks((-np.pi, -np.pi / 2, 0, np.pi / 2., np.pi))
# ax.set_xticklabels((r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'))
# ax.set_yticks(())
# ax.get_figure().canvas.draw()
# if savefigs:
# dataio.save_current_figure('results_misbinding_logpost_ratioconj%.2f_{label}_global_{unique_id}.pdf' % ratio_conj)
# Compute the probability of answering wrongly (from fitting mixture distrib onto posterior)
for n in xrange(result_all_log_posterior.shape[1]):
result_prob_wrong[ratio_conj_i, n], _, _ = utils.fit_gaussian_mixture_fixedmeans(all_angles, np.exp(result_all_log_posterior[ratio_conj_i, n]), fixed_means=fixed_means, normalise=True, return_fitted_data=False, should_plot=False)
# ax = utils.plot_mean_std_area(ratio_space, nanmean(result_prob_wrong, axis=-1), nanstd(result_prob_wrong, axis=-1))
plt.figure()
plt.plot(ratio_space, utils.nanmean(result_prob_wrong, axis=-1))
# ax.get_figure().canvas.draw()
if savefigs:
dataio.save_current_figure('results_misbinding_probwrongpost_allratioconj_{label}_global_{unique_id}.pdf')
if plot_error:
## Compute Standard deviation/precision from samples and plot it as a function of ratio_conj
stats = utils.compute_mean_std_circular_data(utils.wrap_angles(result_all_thetas - fixed_means[1]).T)
f = plt.figure()
plt.plot(ratio_space, stats['std'])
plt.ylabel('Standard deviation [rad]')
if savefigs:
dataio.save_current_figure('results_misbinding_stddev_allratioconj_{label}_global_{unique_id}.pdf')
f = plt.figure()
plt.plot(ratio_space, utils.compute_angle_precision_from_std(stats['std'], square_precision=False), linewidth=2)
plt.ylabel('Precision [$1/rad$]')
plt.xlabel('Proportion of conjunctive units')
plt.grid()
if savefigs:
dataio.save_current_figure('results_misbinding_precision_allratioconj_{label}_global_{unique_id}.pdf')
## Compute the probability of misbinding
# 1) Just count samples < 0 / samples tot
# 2) Fit a mixture model, average over mixture probabilities
prob_smaller0 = np.sum(result_all_thetas <= 1, axis=1)/float(result_all_thetas.shape[1])
em_centers = np.zeros((ratio_space.size, 2))
em_covs = np.zeros((ratio_space.size, 2))
em_pk = np.zeros((ratio_space.size, 2))
em_ll = np.zeros(ratio_space.size)
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
cen_lst, cov_lst, em_pk[ratio_conj_i], em_ll[ratio_conj_i] = pygmm.em(result_all_thetas[ratio_conj_i, np.newaxis].T, K = 2, max_iter = 400, init_kw={'cluster_init':'fixed', 'fixed_means': fixed_means})
em_centers[ratio_conj_i] = np.array(cen_lst).flatten()
em_covs[ratio_conj_i] = np.array(cov_lst).flatten()
# print em_centers
# print em_covs
# print em_pk
f = plt.figure()
plt.plot(ratio_space, prob_smaller0)
plt.ylabel('Misbound proportion')
if savefigs:
dataio.save_current_figure('results_misbinding_countsmaller0_allratioconj_{label}_global_{unique_id}.pdf')
f = plt.figure()
plt.plot(ratio_space, np.max(em_pk, axis=-1), 'g', linewidth=2)
plt.ylabel('Mixture proportion, correct')
plt.xlabel('Proportion of conjunctive units')
plt.grid()
if savefigs:
dataio.save_current_figure('results_misbinding_emmixture_allratioconj_{label}_global_{unique_id}.pdf')
# Put everything on one figure
f = plt.figure(figsize=(10, 6))
norm_for_plot = lambda x: (x - np.min(x))/np.max((x - np.min(x)))
plt.plot(ratio_space, norm_for_plot(stats['std']), ratio_space, norm_for_plot(utils.compute_angle_precision_from_std(stats['std'], square_precision=False)), ratio_space, norm_for_plot(prob_smaller0), ratio_space, norm_for_plot(em_pk[:, 1]), ratio_space, norm_for_plot(em_pk[:, 0]))
plt.legend(('Std dev', 'Precision', 'Prob smaller 1', 'Mixture proportion correct', 'Mixture proportion misbinding'))
# plt.plot(ratio_space, norm_for_plot(compute_angle_precision_from_std(stats['std'], square_precision=False)), ratio_space, norm_for_plot(em_pk[:, 1]), linewidth=2)
# plt.legend(('Precision', 'Mixture proportion correct'), loc='best')
plt.grid()
if savefigs:
dataio.save_current_figure('results_misbinding_allmetrics_allratioconj_{label}_global_{unique_id}.pdf')
if plot_mixtmodel:
# Fit Paul's model
target_angle = np.ones(N)*fixed_means[1]
nontarget_angles = np.ones((N, 1))*fixed_means[0]
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
print "Ratio: ", ratio_conj
responses = result_all_thetas[ratio_conj_i]
if mixturemodel_to_use == 'allitems_kappafi':
curr_params_fit = em_circularmixture_allitems_kappafi.fit(responses, target_angle, nontarget_angles, kappa=result_fisherinfo_ratio[ratio_conj_i])
elif mixturemodel_to_use == 'allitems':
curr_params_fit = em_circularmixture_allitems_uniquekappa.fit(responses, target_angle, nontarget_angles)
else:
curr_params_fit = em_circularmixture.fit(responses, target_angle, nontarget_angles)
result_em_fits[ratio_conj_i] = [curr_params_fit['kappa'], curr_params_fit['mixt_target']] + utils.arrnum_to_list(curr_params_fit['mixt_nontargets']) + [curr_params_fit[key] for key in ('mixt_random', 'train_LL', 'bic')]
print curr_params_fit
if False:
f, ax = plt.subplots()
ax2 = ax.twinx()
# left axis, kappa
ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 0], 0*result_em_fits[:, 0], xlabel='Proportion of conjunctive units', ylabel="Inverse variance $[rad^{-2}]$", ax_handle=ax, linewidth=3, fmt='o-', markersize=8, label='Fitted kappa', color='k')
# Right axis, mixture probabilities
utils.plot_mean_std_area(ratio_space, result_em_fits[:, 1], 0*result_em_fits[:, 1], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Target')
utils.plot_mean_std_area(ratio_space, result_em_fits[:, 2], 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Nontarget')
utils.plot_mean_std_area(ratio_space, result_em_fits[:, 3], 0*result_em_fits[:, 3], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax2, linewidth=3, fmt='o-', markersize=8, label='Random')
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, fontsize=12, loc='right')
# ax.set_xlim([0.9, 5.1])
# ax.set_xticks(range(1, 6))
# ax.set_xticklabels(range(1, 6))
plt.grid()
f.canvas.draw()
if True:
# Mixture probabilities
ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 1], 0*result_em_fits[:, 1], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", linewidth=3, fmt='-', markersize=8, label='Target')
utils.plot_mean_std_area(ratio_space, result_em_fits[:, 2], 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='-', markersize=8, label='Nontarget')
utils.plot_mean_std_area(ratio_space, result_em_fits[:, 3], 0*result_em_fits[:, 3], xlabel='Proportion of conjunctive units', ylabel="Mixture probabilities", ax_handle=ax, linewidth=3, fmt='-', markersize=8, label='Random')
ax.legend(loc='right')
# ax.set_xlim([0.9, 5.1])
# ax.set_xticks(range(1, 6))
# ax.set_xticklabels(range(1, 6))
plt.grid()
if savefigs:
dataio.save_current_figure('results_misbinding_emmixture_allratioconj_{label}_global_{unique_id}.pdf')
if True:
# Kappa
# ax = utils.plot_mean_std_area(ratio_space, result_em_fits[:, 0], 0*result_em_fits[:, 0], xlabel='Proportion of conjunctive units', ylabel="$\kappa [rad^{-2}]$", linewidth=3, fmt='-', markersize=8, label='Kappa')
ax = utils.plot_mean_std_area(ratio_space, utils.kappa_to_stddev(result_em_fits[:, 0]), 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Standard deviation [rad]", linewidth=3, fmt='-', markersize=8, label='Mixture model $\kappa$')
# Add Fisher Info theo
ax = utils.plot_mean_std_area(ratio_space, utils.kappa_to_stddev(result_fisherinfo_ratio), 0*result_em_fits[:, 2], xlabel='Proportion of conjunctive units', ylabel="Standard deviation [rad]", linewidth=3, fmt='-', markersize=8, label='Fisher Information', ax_handle=ax)
ax.legend(loc='best')
# ax.set_xlim([0.9, 5.1])
# ax.set_xticks(range(1, 6))
# ax.set_xticklabels(range(1, 6))
plt.grid()
if savefigs:
dataio.save_current_figure('results_misbinding_kappa_allratioconj_{label}_global_{unique_id}.pdf')
if compute_plot_bootstrap:
## Compute the bootstrap pvalue for each ratio
# use the bootstrap CDF from mixed runs, not the exact current ones, not sure if good idea.
bootstrap_to_load = 1
if bootstrap_to_load == 1:
cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap_mixed_from_bootstrapnontargets.pickle')
bootstrap_ecdf_sum_label = 'bootstrap_ecdf_allitems_sum_sigmax_T'
bootstrap_ecdf_all_label = 'bootstrap_ecdf_allitems_all_sigmax_T'
elif bootstrap_to_load == 2:
cache_bootstrap_fn = os.path.join(generator_module.pbs_submission_infos['simul_out_dir'], 'outputs', 'cache_bootstrap_misbinding_mixed.pickle')
bootstrap_ecdf_sum_label = 'bootstrap_ecdf_allitems_sum_ratioconj'
bootstrap_ecdf_all_label = 'bootstrap_ecdf_allitems_all_ratioconj'
try:
with open(cache_bootstrap_fn, 'r') as file_in:
# Load and assign values
cached_data = pickle.load(file_in)
assert bootstrap_ecdf_sum_label in cached_data
assert bootstrap_ecdf_all_label in cached_data
should_fit_bootstrap = False
except IOError:
print "Error while loading ", cache_bootstrap_fn
# Select the ECDF to use
if bootstrap_to_load == 1:
sigmax_i = 3 # corresponds to sigmax = 2, input here.
T_i = 1 # two possible targets here.
bootstrap_ecdf_sum_used = cached_data[bootstrap_ecdf_sum_label][sigmax_i][T_i]['ecdf']
bootstrap_ecdf_all_used = cached_data[bootstrap_ecdf_all_label][sigmax_i][T_i]['ecdf']
elif bootstrap_to_load == 2:
ratio_conj_i = 4
bootstrap_ecdf_sum_used = cached_data[bootstrap_ecdf_sum_label][ratio_conj_i]['ecdf']
bootstrap_ecdf_all_used = cached_data[bootstrap_ecdf_all_label][ratio_conj_i]['ecdf']
result_pvalue_bootstrap_sum = np.empty(ratio_space.size)*np.nan
result_pvalue_bootstrap_all = np.empty((ratio_space.size, nontarget_angles.shape[-1]))*np.nan
for ratio_conj_i, ratio_conj in enumerate(ratio_space):
print "Ratio: ", ratio_conj
responses = result_all_thetas[ratio_conj_i]
bootstrap_allitems_nontargets_allitems_uniquekappa = em_circularmixture_allitems_uniquekappa.bootstrap_nontarget_stat(responses, target_angle, nontarget_angles,
sumnontargets_bootstrap_ecdf=bootstrap_ecdf_sum_used,
allnontargets_bootstrap_ecdf=bootstrap_ecdf_all_used)
result_pvalue_bootstrap_sum[ratio_conj_i] = bootstrap_allitems_nontargets_allitems_uniquekappa['p_value']
result_pvalue_bootstrap_all[ratio_conj_i] = bootstrap_allitems_nontargets_allitems_uniquekappa['allnontarget_p_value']
## Plots
# f, ax = plt.subplots()
# ax.plot(ratio_space, result_pvalue_bootstrap_all, linewidth=2)
# if savefigs:
# dataio.save_current_figure("pvalue_bootstrap_all_ratioconj_{label}_{unique_id}.pdf")
f, ax = plt.subplots()
ax.plot(ratio_space, result_pvalue_bootstrap_sum, linewidth=2)
plt.grid()
if savefigs:
dataio.save_current_figure("pvalue_bootstrap_sum_ratioconj_{label}_{unique_id}.pdf")
# plt.figure()
# plt.plot(ratio_MMlower, results_filtered_smoothed/np.max(results_filtered_smoothed, axis=0), linewidth=2)
# plt.plot(ratio_MMlower[np.argmax(results_filtered_smoothed, axis=0)], np.ones(results_filtered_smoothed.shape[-1]), 'ro', markersize=10)
# plt.grid()
# plt.ylim((0., 1.1))
# plt.subplots_adjust(right=0.8)
# plt.legend(['%d item' % i + 's'*(i>1) for i in xrange(1, T+1)], loc='center right', bbox_to_anchor=(1.3, 0.5))
# plt.xticks(np.linspace(0, 1.0, 5))
variables_to_save = ['target_angle', 'nontarget_angles']
if savedata:
dataio.save_variables_default(locals(), variables_to_save)
dataio.make_link_output_to_dropbox(dropbox_current_experiment_folder='misbindings')
plt.show()
return locals()
def check_precision_sensitivity_determ():
''' Let's construct a situation where we have one Von Mises component and one random component. See how the random component affects the basic precision estimator we use elsewhere.
'''
N = 1000
kappa_space = np.array([3., 10., 20.])
# kappa_space = np.array([3.])
nb_repeats = 20
ratio_to_kappa = False
savefigs = True
precision_nb_samples = 101
N_rnd_space = np.linspace(0, N/2, precision_nb_samples).astype(int)
precision_all = np.zeros((N_rnd_space.size, nb_repeats))
kappa_estimated_all = np.zeros((N_rnd_space.size, nb_repeats))
precision_squared_all = np.zeros((N_rnd_space.size, nb_repeats))
kappa_mixtmodel_all = np.zeros((N_rnd_space.size, nb_repeats))
mixtmodel_all = np.zeros((N_rnd_space.size, nb_repeats, 2))
dataio = DataIO.DataIO()
target_samples = np.zeros(N)
for kappa in kappa_space:
true_kappa = kappa*np.ones(N_rnd_space.size)
# First sample all as von mises
samples_all = spst.vonmises.rvs(kappa, size=(N_rnd_space.size, nb_repeats, N))
for repeat in progress.ProgressDisplay(xrange(nb_repeats)):
for i, N_rnd in enumerate(N_rnd_space):
samples = samples_all[i, repeat]
# Then set K of them to random [-np.pi, np.pi] values.
samples[np.random.randint(N, size=N_rnd)] = utils.sample_angle(N_rnd)
# Estimate precision from those samples.
precision_all[i, repeat] = utils.compute_precision_samples(samples, square_precision=False, remove_chance_level=False)
precision_squared_all[i, repeat] = utils.compute_precision_samples(samples, square_precision=True)
# convert circular std dev back to kappa
kappa_estimated_all[i, repeat] = utils.stddev_to_kappa(1./precision_all[i, repeat])
# Fit mixture model
params_fit = em_circularmixture.fit(samples, target_samples)
kappa_mixtmodel_all[i, repeat] = params_fit['kappa']
mixtmodel_all[i, repeat] = params_fit['mixt_target'], params_fit['mixt_random']
print "%d/%d N_rnd: %d, Kappa: %.3f, precision: %.3f, kappa_tilde: %.3f, precision^2: %.3f, kappa_mixtmod: %.3f" % (repeat, nb_repeats, N_rnd, kappa, precision_all[i, repeat], kappa_estimated_all[i, repeat], precision_squared_all[i, repeat], kappa_mixtmodel_all[i, repeat])
if ratio_to_kappa:
precision_all /= kappa
precision_squared_all /= kappa
kappa_estimated_all /= kappa
true_kappa /= kappa
f, ax = plt.subplots()
ax.plot(N_rnd_space/float(N), true_kappa, 'k-', linewidth=3, label='Kappa_true')
utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(precision_all, axis=-1), np.std(precision_all, axis=-1), ax_handle=ax, label='precision')
utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(precision_squared_all, axis=-1), np.std(precision_squared_all, axis=-1), ax_handle=ax, label='precision^2')
utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(kappa_estimated_all, axis=-1), np.std(kappa_estimated_all, axis=-1), ax_handle=ax, label='kappa_tilde')
utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(kappa_mixtmodel_all, axis=-1), np.std(kappa_mixtmodel_all, axis=-1), ax_handle=ax, label='kappa mixt model')
ax.legend()
ax.set_title('Effect of random samples on precision. kappa: %.2f. ratiokappa %s' % (kappa, ratio_to_kappa))
ax.set_xlabel('Proportion random samples. N tot %d' % N)
ax.set_ylabel('Kappa/precision (not same units)')
f.canvas.draw()
if savefigs:
dataio.save_current_figure("precision_sensitivity_kappa%dN%d_{unique_id}.pdf" % (kappa, N))
# Do another plot, with kappa and mixt_target/mixt_random. Use left/right axis separately
f, ax = plt.subplots()
ax2 = ax.twinx()
# left axis, kappa
ax.plot(N_rnd_space/float(N), true_kappa, 'k-', linewidth=3, label='kappa true')
utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(kappa_mixtmodel_all, axis=-1), np.std(kappa_mixtmodel_all, axis=-1), ax_handle=ax, label='kappa')
# Right axis, mixture probabilities
utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(mixtmodel_all[..., 0], axis=-1), np.std(mixtmodel_all[..., 0], axis=-1), ax_handle=ax2, label='mixt target', color='r')
utils.plot_mean_std_area(N_rnd_space/float(N), np.mean(mixtmodel_all[..., 1], axis=-1), np.std(mixtmodel_all[..., 1], axis=-1), ax_handle=ax2, label='mixt random', color='g')
ax.set_title('Mixture model parameters evolution. kappa: %.2f, ratiokappa %s' % (kappa, ratio_to_kappa))
ax.set_xlabel('Proportion random samples. N tot %d' % N)
ax.set_ylabel('Kappa')
ax2.set_ylabel('Mixture proportions')
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2)
if savefigs:
dataio.save_current_figure("precision_sensitivity_mixtmodel_kappa%dN%d_{unique_id}.pdf" % (kappa, N))
return locals()
def plots_ratioMscaling(data_pbs, generator_module=None):
'''
Reload and plot precision/fits of a Mixed code.
'''
#### SETUP
#
savefigs = True
savedata = True
plots_pcolor_all = True
plots_effect_M_target_kappa = False
plots_kappa_fi_comparison = False
plots_multiple_fisherinfo = False
specific_plot_effect_R = False
convert_M_realsizes = True
plots_pcolor_realsizes_Msubs = True
plots_pcolor_realsizes_Mtot = 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))
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)
all_args = data_pbs.loaded_data['args_list']
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 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), 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='nearest', log_scale=False)
if savefigs:
dataio.save_current_figure('pcolortrueM%s_%s_R%d_log_{label}_{unique_id}.pdf' % (points_label, title, R))
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'])
# # show precision
# plot_interp(result_parameters_flat_subM_converted, result_all_precisions_mean_flat, currR_indices, title='precision')
# # show kappa
# plot_interp(result_parameters_flat_subM_converted, result_em_fits_kappa_flat, currR_indices, title='kappa')
# plot_interp(result_parameters_flat_subM_converted, result_em_fits_kappa_valid_flat, currR_indices, title='kappavalid')
# # show probability on target
# plot_interp(result_parameters_flat_subM_converted, result_em_fits_target_flat, currR_indices, title='target')
# # show fisher info
# plot_interp(result_parameters_flat_subM_converted, result_fisherinfor_mean_flat, currR_indices, title='fisherinfo')
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:
# Choose a M, find which ratio gives best fit to a given kappa
M_target = 356
M_target_i = np.argmin(np.abs(M_space - M_target))
utils.pcolor_2d_data(result_em_fits_kappa_valid[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))
if savefigs:
dataio.save_current_figure('specific_pcolor_kappa_M%d_log_{label}_{unique_id}.pdf' % (M_target))
# target_kappa = np.ma.mean(result_em_fits_kappa_valid[M_target_i])
# target_kappa = 5*1e3
target_kappa = 1.2e3
dist_target_kappa = np.abs(result_em_fits_kappa_valid[M_target_i] - target_kappa)
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('specific_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()
