/
plots_fitexperiment_papertheo.py
515 lines (425 loc) · 18.7 KB
/
plots_fitexperiment_papertheo.py
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#!/usr/bin/env python
# encoding: utf-8
import numpy as np
import matplotlib.pyplot as plt
import progress
import utils
import scipy.stats as spst
import pycircstat
import warnings
# plt.rcParams['font.size'] = 24
class PlotsFitExperimentAllTPaperTheo(object):
"""
This class does all plots as in our PLOSCB2015 paper, for new
FitExperimentAllT instances.
"""
def __init__(self, fit_experiment_allt,
do_distrib_errors_fig5=True,
do_memcurves_fig6=True,
do_mixtcurves_fig13=True,
do_distrib_errors_data_fig2=True,
):
self.fit_exp = fit_experiment_allt
self.experiment_id = self.fit_exp.experiment_id
self.result_em_fits_stats = None
self.do_distrib_errors_fig5 = do_distrib_errors_fig5
self.do_memcurves_fig6 = do_memcurves_fig6
self.do_mixtcurves_fig13 = do_mixtcurves_fig13
self.do_distrib_errors_data_fig2 = do_distrib_errors_data_fig2
print "Doing Paper plots for %s. \nFig5 %d, Fig6 %d, Fig13 %d" % (
self.experiment_id,
self.do_distrib_errors_fig5,
self.do_memcurves_fig6,
self.do_mixtcurves_fig13
)
def do_plots(self):
'''
Do all plots for that FitExperimentAllT.
These correspond to a particular experiment_id only, not multiple.
'''
if self.do_distrib_errors_data_fig2:
self.plots_distrib_errors_data_fig2()
if self.do_distrib_errors_fig5:
self.plots_distrib_errors_fig5()
if self.do_memcurves_fig6 or self.do_mixtcurves_fig13:
self.plots_memmixtcurves_fig6fig13()
def plots_distrib_errors_data_fig2(self, size=6, nice_xticks=True):
'''
HUMAN DATA for Fig5
Same as plots_distrib_errors_fig5, but for the Experimental data
Series of plots reproducing Fig 2 - Distribution of errors in human subjects
'''
f, axes = plt.subplots(nrows=2,
ncols=self.fit_exp.T_space.size,
figsize=(2*size, size)
)
for t_i, T in enumerate(self.fit_exp.T_space):
print "DATA T %d" % T
self.fit_exp.setup_experimental_stimuli_T(T)
self.fit_exp.restore_responses('human')
self.fit_exp.sampler.plot_histogram_errors(bins=41, ax_handle=axes[0, t_i], norm='density')
axes[0, t_i].set_title('')
axes[0, t_i].set_ylim((0, 2))
if nice_xticks:
axes[0, t_i].set_xticks(
(-np.pi, -np.pi/2, 0, np.pi/2., np.pi))
axes[0, t_i].set_xticklabels(
(r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
fontsize=15)
if T > 1:
self.fit_exp.sampler.plot_histogram_bias_nontarget(bins=41, ax_handle=axes[1, t_i], show_parameters=False)
axes[1, t_i].set_title('')
axes[1, t_i].set_ylim((0, 0.3))
if nice_xticks:
axes[1, t_i].set_xticks(
(-np.pi, -np.pi/2, 0, np.pi/2., np.pi))
axes[1, t_i].set_xticklabels(
(r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
fontsize=15)
else:
axes[1, t_i].axis('off')
f.suptitle('Fig2 - Human distribution errors', y=0.95)
return axes
def plots_distrib_errors_fig5(self, size=6, show_fits=False,
nice_xticks=True):
'''
Series of plots reproducing Fig 5 - Distribution of errors of the
model
Arranged as in the paper.
'''
f, axes = plt.subplots(nrows=2,
ncols=self.fit_exp.T_space.size,
figsize=(2*size, size)
)
for t_i, T in enumerate(self.fit_exp.T_space):
print "MODEL T %d" % T
self.fit_exp.setup_experimental_stimuli_T(T)
if 'samples' in self.fit_exp.get_names_stored_responses():
self.fit_exp.restore_responses('samples')
else:
self.fit_exp.sampler.force_sampling_round()
self.fit_exp.store_responses('samples')
self.fit_exp.sampler.plot_histogram_errors(bins=41, ax_handle=axes[0, t_i], norm='density')
axes[0, t_i].set_title('')
axes[0, t_i].set_ylim((0, 2))
if nice_xticks:
axes[0, t_i].set_xticks(
(-np.pi, -np.pi/2, 0, np.pi/2., np.pi))
axes[0, t_i].set_xticklabels(
(r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
fontsize=15)
if T > 1:
self.fit_exp.sampler.plot_histogram_bias_nontarget(bins=41, ax_handle=axes[1, t_i], show_parameters=False)
axes[1, t_i].set_title('')
axes[1, t_i].set_ylim((0, 0.3))
if nice_xticks:
axes[1, t_i].set_xticks(
(-np.pi, -np.pi/2, 0, np.pi/2., np.pi))
axes[1, t_i].set_xticklabels(
(r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
fontsize=15)
else:
axes[1, t_i].axis('off')
f.suptitle('Fig5 - Model distribution errors', y=0.95)
return axes
def plots_KS_comparison_fig2fig5(self,
bins=41,
show_pval=True,
size=6,
nice_xticks=True,
):
'''
Will plot the ECDF of data/samples and then do Kolmogorov-Smirnov /Kuiper 2-samples tests on them.
'''
f, axes = plt.subplots(nrows=2,
ncols=self.fit_exp.T_space.size,
figsize=(size*2, size)
)
result_KS = dict(human=dict(),
model=dict(),
ks_pval=dict(),
kuiper_pval=dict()
)
warnings.simplefilter("ignore")
for t_i, T in enumerate(self.fit_exp.T_space):
result_KS['ks_pval'][T] = dict()
result_KS['kuiper_pval'][T] = dict()
self.fit_exp.setup_experimental_stimuli_T(T)
# Human histograms and CDF
self.fit_exp.restore_responses('human')
result_KS['human'][T] = self.fit_exp.sampler.compute_errors_alltargets_histograms(bins=bins)
# Samples histograms and CDF
if 'samples' in self.fit_exp.get_names_stored_responses():
self.fit_exp.restore_responses('samples')
else:
self.fit_exp.sampler.force_sampling_round()
self.fit_exp.store_responses('samples')
result_KS['model'][T] = self.fit_exp.sampler.compute_errors_alltargets_histograms(bins=bins)
# Compute K-S 2-samples tests stats
for condition in ['targets', 'nontargets']:
if condition in result_KS['human'][T]:
ks_out = spst.ks_2samp(
result_KS['human'][T][condition]['samples'],
result_KS['model'][T][condition]['samples']
)
result_KS['ks_pval'][T][condition] = \
ks_out.pvalue
result_KS['kuiper_pval'][T][condition] = \
pycircstat.tests.kuiper(
result_KS['human'][T][condition]['samples'],
result_KS['model'][T][condition]['samples'])[0][0]
### Plot everything
axes[0, t_i].plot(result_KS['human'][T]['targets']['x'],
result_KS['human'][T]['targets']['ecdf'],
label='data'
)
axes[0, t_i].plot(result_KS['model'][T]['targets']['x'],
result_KS['model'][T]['targets']['ecdf'],
label='model'
)
axes[0, t_i].set_title('')
axes[0, t_i].set_xlim((-np.pi, np.pi))
# axes[0, t_i].set_ylim((0, 2))
if nice_xticks:
axes[0, t_i].set_xticks(
(-np.pi, -np.pi/2, 0, np.pi/2., np.pi))
axes[0, t_i].set_xticklabels(
(r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
fontsize=15)
if show_pval:
axes[0, t_i].text(
0.02, 0.99,
"KS p: %.2f" % result_KS['ks_pval'][T]['targets'],
transform=axes[0, t_i].transAxes,
horizontalalignment='left',
verticalalignment='top'
)
axes[0, t_i].text(
0.02, 0.9,
"Kuiper p: %.2f" % result_KS['kuiper_pval'][T]['targets'],
transform=axes[0, t_i].transAxes,
horizontalalignment='left',
verticalalignment='top'
)
if T > 1:
axes[1, t_i].plot(result_KS['human'][T]['nontargets']['x'],
result_KS['human'][T]['nontargets']['ecdf'],
label='data'
)
axes[1, t_i].plot(result_KS['model'][T]['nontargets']['x'],
result_KS['model'][T]['nontargets']['ecdf'],
label='model'
)
axes[1, t_i].set_title('')
axes[1, t_i].set_xlim((-np.pi, np.pi))
if nice_xticks:
axes[1, t_i].set_xticks(
(-np.pi, -np.pi/2, 0, np.pi/2., np.pi))
axes[1, t_i].set_xticklabels(
(r'$-\pi$', r'$-\frac{\pi}{2}$', r'$0$', r'$\frac{\pi}{2}$', r'$\pi$'),
fontsize=15)
if show_pval:
axes[1, t_i].text(
0.02, 0.99,
"KS p: %.2f" % result_KS['ks_pval'][T]['nontargets'],
transform=axes[1, t_i].transAxes,
horizontalalignment='left',
verticalalignment='top'
)
axes[1, t_i].text(
0.02, 0.9,
"Kuiper p: %.2f" % result_KS['kuiper_pval'][T]['nontargets'],
transform=axes[1, t_i].transAxes,
horizontalalignment='left',
verticalalignment='top'
)
else:
axes[1, t_i].axis('off')
axes[1, 1].legend(loc='center',
bbox_to_anchor=(0.5, 0.5),
bbox_transform=axes[1, 0].transAxes
)
f.suptitle('ECDF between human and model')
return axes, result_KS
def plots_memmixtcurves_fig6fig13(self,
num_repetitions=1,
use_cache=True,
layout='vertical',
size=6
):
'''
Plots the memory fidelity for all T and the mixture proportions for all T
'''
if self.result_em_fits_stats is None or not use_cache:
search_progress = progress.Progress(self.fit_exp.T_space.size*num_repetitions)
# kappa, mixt_target, mixt_nontarget, mixt_random, ll
result_em_fits = np.nan*np.ones((self.fit_exp.T_space.size, 5, num_repetitions))
for T_i, T in enumerate(self.fit_exp.T_space):
self.fit_exp.setup_experimental_stimuli_T(T)
for repet_i in xrange(num_repetitions):
print "%.2f%%, %s left - %s" % (search_progress.percentage(), search_progress.time_remaining_str(), search_progress.eta_str())
print "Fit for T=%d, %d/%d" % (T, repet_i+1, num_repetitions)
if 'samples' in self.fit_exp.get_names_stored_responses() and repet_i < 1:
self.fit_exp.restore_responses('samples')
else:
self.fit_exp.sampler.force_sampling_round()
self.fit_exp.store_responses('samples')
# Fit mixture model
curr_params_fit = self.fit_exp.sampler.fit_mixture_model(use_all_targets=False)
result_em_fits[T_i, :, repet_i] = [curr_params_fit[key]
for key in ('kappa', 'mixt_target',
'mixt_nontargets_sum', 'mixt_random',
'train_LL')]
search_progress.increment()
# Get stats of EM Fits
self.result_em_fits_stats = dict(
mean=utils.nanmean(result_em_fits, axis=-1),
std=utils.nanstd(result_em_fits, axis=-1)
)
# Do the plots
if self.do_memcurves_fig6 and self.do_mixtcurves_fig13:
if layout == 'vertical':
f, axes = plt.subplots(nrows=2, figsize=(size, size*2))
else:
f, axes = plt.subplots(ncols=2, figsize=(size*2, size))
else:
f, ax = plt.subplots(figsize=(size, size))
axes = [ax]
ax_i = 0
if self.do_memcurves_fig6:
self.__plot_memcurves(self.result_em_fits_stats,
suptitle_text='Memory fidelity',
ax=axes[ax_i]
)
ax_i += 1
if self.do_mixtcurves_fig13:
self.__plot_mixtcurves(self.result_em_fits_stats,
suptitle_text='Mixture proportions',
ax=axes[ax_i]
)
ax_i += 1
return axes
# Memory curve kappa
def __plot_memcurves(self, model_em_fits, suptitle_text=None, ax=None):
'''
Nice plot for the memory fidelity, as in Fig6 of the paper theo
Changes to using the subject fits if FitExperimentAllTSubject used.
'''
T_space = self.fit_exp.T_space
data_em_fits = self.fit_exp.get_em_fits_arrays()
if ax is None:
_, ax = plt.subplots()
else:
ax.hold(False)
ax = utils.plot_mean_std_area(
T_space,
data_em_fits['mean'][0],
data_em_fits['std'][0],
linewidth=3, fmt='o-', markersize=8,
label='Data',
ax_handle=ax
)
ax.hold(True)
ax = utils.plot_mean_std_area(
T_space,
model_em_fits['mean'][..., 0],
model_em_fits['std'][..., 0],
xlabel='Number of items',
ylabel="Memory fidelity $[rad^{-2}]$",
linewidth=3,
fmt='o-', markersize=8,
label='Model',
ax_handle=ax
)
ax.legend(loc='upper right',
bbox_to_anchor=(1., 1.)
)
ax.set_xlim([0.9, T_space.max()+0.1])
ax.set_xticks(range(1, T_space.max()+1))
ax.set_xticklabels(range(1, T_space.max()+1))
if suptitle_text:
ax.set_title(suptitle_text)
# ax.get_figure().suptitle(suptitle_text)
ax.hold(False)
ax.get_figure().canvas.draw()
return ax
def __plot_mixtcurves(self, model_em_fits, suptitle_text=None, ax=None):
'''
Similar kind of plot, but showing the mixture proportions, as in Figure13
'''
T_space = self.fit_exp.T_space
data_em_fits = self.fit_exp.get_em_fits_arrays()
if ax is None:
_, ax = plt.subplots()
else:
ax.hold(False)
model_em_fits['mean'][np.isnan(model_em_fits['mean'])] = 0.0
model_em_fits['std'][np.isnan(model_em_fits['std'])] = 0.0
# Show model fits
utils.plot_mean_std_area(
T_space,
model_em_fits['mean'][..., 1],
model_em_fits['std'][..., 1],
xlabel='Number of items',
ylabel="Mixture proportions",
ax_handle=ax, linewidth=3, fmt='o-', markersize=5,
label='Target',
)
ax.hold(True)
utils.plot_mean_std_area(
T_space,
model_em_fits['mean'][..., 2],
model_em_fits['std'][..., 2],
xlabel='Number of items',
ylabel="Mixture probabilities",
ax_handle=ax, linewidth=3, fmt='o-', markersize=5,
label='Nontarget'
)
utils.plot_mean_std_area(
T_space,
model_em_fits['mean'][..., 3],
model_em_fits['std'][..., 3],
xlabel='Number of items',
ylabel="Mixture probabilities",
ax_handle=ax, linewidth=3, fmt='o-', markersize=5,
label='Random'
)
# Now data
utils.plot_mean_std_area(
T_space,
data_em_fits['mean'][1],
data_em_fits['std'][1],
xlabel='Number of items',
ylabel="Mixture probabilities",
ax_handle=ax, linewidth=2, fmt='o--', markersize=5,
label='Data target'
)
utils.plot_mean_std_area(
T_space,
data_em_fits['mean'][2],
data_em_fits['std'][2],
xlabel='Number of items',
ylabel="Mixture probabilities",
ax_handle=ax, linewidth=2, fmt='o--', markersize=5, label='Data nontarget'
)
utils.plot_mean_std_area(
T_space,
data_em_fits['mean'][3],
data_em_fits['std'][3],
xlabel='Number of items',
ylabel="Mixture probabilities",
ax_handle=ax, linewidth=2, fmt='o--', markersize=5, label='Data random'
)
ax.legend(loc='upper left',
bbox_to_anchor=(1., 1.)
)
ax.set_xlim([0.9, T_space.max() + 0.1])
ax.set_ylim([0.0, 1.1])
ax.set_xticks(range(1, T_space.max() + 1))
ax.set_xticklabels(range(1, T_space.max() + 1))
if suptitle_text:
ax.set_title(suptitle_text)
# ax.get_figure().suptitle(suptitle_text)
ax.get_figure().canvas.draw()
ax.hold(False)
return ax