def plot(results_path, fig_path):
data = pd.read_csv(
results_path.joinpath("trial_accuracy_means.csv"))\
.groupby('model').get_group('exp')\
.sort('trial')
trials = data['trial']
acc = data['median'] * 100
fig, ax = plt.subplots()
ax.plot(trials, acc, 'k.')
ax.set_xlim(1, 200)
ax.set_ylim(70, 100)
ax.set_xlabel("Trial", fontsize=14)
ax.set_ylabel("Percent correct", fontsize=14)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
fig.set_figheight(3.5)
fig.set_figwidth(4.5)
plt.draw()
plt.tight_layout()
pths = [fig_path.joinpath("trial_accuracy.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
data = pd.read_csv(
results_path.joinpath("trial_time_means.csv"))\
.groupby('model').get_group('exp')\
.sort_values(by='trial')
trials = data['trial']
times = data['median']
fig, ax = plt.subplots()
ax.plot(trials, times, 'k.')
ax.set_xlim(1, 200)
ax.set_xlabel("Trial", fontsize=14)
ax.set_ylabel("Response time", fontsize=14)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
fig.set_figheight(3.5)
fig.set_figwidth(4.5)
plt.draw()
plt.tight_layout()
pths = [fig_path.joinpath("trial_time.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
order = ['exp', 'oc', 'th', 'hc', 'bq', 'bqp']
titles = {
'exp': "Human",
'oc': "Oracle",
'th': "Threshold",
'hc': "HC",
'bq': "BQ (equal prior)",
'bqp': "BQ (unequal prior)"
}
pth = results_path.joinpath("all_response_times.pkl")
with open(pth, "r") as fh:
times = pickle.load(fh)
fig, axes = plt.subplots(1, len(order), sharey=True)
for i, key in enumerate(order):
ax = axes[i]
bins = 200
if key == 'exp':
hist, edges = np.histogram(
times[key] / 1000., bins=bins,
range=(0, 20))
else:
hist, edges = np.histogram(
times[key], bins=bins,
range=(0, bins))
edges = edges[:101]
hist = hist[:100]
hist = hist * 100 / float(len(times[key]))
width = edges[1] - edges[0]
ax.bar(edges[:-1], hist, width=width, color='k')
ax.set_xlim(0, edges[-1])
ax.set_title(titles[key], fontsize=14)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
if key == 'exp':
ax.set_xlabel("RT (seconds)", fontsize=14)
else:
ax.set_xlabel("Number of actions", fontsize=14)
axes[0].set_ylabel("Percent", fontsize=14)
fig.set_figheight(2.5)
fig.set_figwidth(16)
plt.draw()
plt.tight_layout()
pths = [fig_path.joinpath("response_time_histograms.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
order = ['hc', 'bq', 'bqp']
titles = {
'exp': "Human",
'hc': "HC",
'bq': "BQ (equal prior)",
'bqp': "BQ (unequal prior)"
}
results = pd.read_csv(
results_path.joinpath("accuracy_means.csv"))\
.set_index(['stimulus', 'theta', 'flipped', 'model'])['median']\
.unstack(['model', 'flipped']) * 100
fig, axes = plt.subplots(1, len(order), sharey=True, sharex=True)
for i, key in enumerate(order):
ax = axes[i]
for flipped in ['same', 'flipped']:
ax.plot(
results[(key, flipped)],
results[('exp', flipped)],
'.', alpha=0.8, label=flipped)
ax.set_xlabel("Model accuracy", fontsize=14)
ax.set_title(titles[key], fontsize=14)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
axes[0].set_ylabel("Human accuracy", fontsize=14)
axes[0].set_xlim(-5, 105)
axes[0].set_ylim(45, 105)
axes[0].legend(loc=0, numpoints=1)
fig.set_figheight(3)
fig.set_figwidth(8)
plt.draw()
plt.tight_layout()
pths = [fig_path.joinpath("accuracy_scatters.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot_key(key, results_path, fig_path):
fig, axes = plt.subplots(4, 5, sharey=True, sharex=True)
means = pd.read_csv(
results_path.joinpath("theta_time_stimulus.csv"))\
.set_index(['stimulus', 'flipped', 'model'])\
.groupby(level='model').get_group(key)
for i, (stim, sdf) in enumerate(means.groupby(level='stimulus')):
ax = axes.flat[i]
for flipped, stats in sdf.groupby(level='flipped'):
lower = stats['median'] - stats['lower']
upper = stats['upper'] - stats['median']
ax.errorbar(
stats['modtheta'], stats['median'],
yerr=[lower, upper],
label=flipped, lw=3)
ax.set_xlabel("Rotation")
ax.set_xticks([0, 60, 120, 180])
ax.set_xlim(-10, 190)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
ax.set_title("Stim %s" % stim)
fig.set_figheight(8)
fig.set_figwidth(10)
plt.draw()
plt.tight_layout()
pths = [fig_path.joinpath("response_time_stimuli_%s.%s" % (key, ext))
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
order = ['exp', 'oc', 'th', 'hc', 'bq', 'bqp']
titles = {
'exp': "Human",
'oc': "Oracle",
'th': "Threshold",
'hc': "HC",
'bq': "BQ (equal prior)",
'bqp': "BQ (unequal prior)"
}
colors = {
'same': [ 0.16339869, 0.4449827 , 0.69750096],
'flipped': [ 0.72848904, 0.1550173 , 0.19738562]
}
time_results = pd.read_csv(
results_path.joinpath("theta_time.csv"))
acc_results = pd.read_csv(
results_path.joinpath("theta_accuracy.csv"))
fig, axes = plt.subplots(2, len(order), sharex=True)
for i, key in enumerate(order):
ax = axes[0, i]
df = time_results.groupby('model').get_group(key)
for flipped, stats in df.groupby('flipped'):
if key == 'exp':
median = stats['median'] / 1000.
lower = (stats['median'] - stats['lower']) / 1000.
upper = (stats['upper'] - stats['median']) / 1000.
else:
median = stats['median']
lower = stats['median'] - stats['lower']
upper = stats['upper'] - stats['median']
ax.errorbar(
stats['modtheta'], median,
yerr=[lower, upper], lw=3,
color=colors[flipped],
ecolor=colors[flipped])
ax.set_xticks(np.arange(0, 200, 30))
ax.set_xlim(-10, 190)
ax.set_title(titles[key], fontsize=14)
if key == 'exp':
ax.set_ylabel("Response time", fontsize=14)
else:
ax.set_ylabel("# actions", fontsize=14)
util.sync_ylims(axes[0, order.index('bq')], axes[0, order.index('bqp')])
for i, key in enumerate(order):
ax = axes[1, i]
df = acc_results.groupby('model').get_group(key)
for flipped, stats in df.groupby('flipped'):
lower = stats['median'] - stats['lower']
upper = stats['upper'] - stats['median']
ax.errorbar(
stats['modtheta'], stats['median'],
yerr=[lower, upper], lw=3,
color=colors[flipped],
ecolor=colors[flipped])
ax.set_xlim(-10, 190)
ax.set_ylim(25, 105)
ax.set_xticks(np.arange(0, 200, 30))
ax.set_xlabel("Rotation", fontsize=14)
ax.set_ylabel("Accuracy", fontsize=14)
for ax in axes.flat:
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
p0 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors['same'],
ec=colors['same'])
p1 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors['flipped'],
ec=colors['flipped'])
leg = axes[1, 1].legend(
[p0, p1], ["\"same\" pairs", "\"flipped\" pairs"],
numpoints=1, fontsize=12,
loc='lower center',
title='Stimuli')
frame = leg.get_frame()
frame.set_edgecolor('#FFFFFF')
util.sync_ylabel_coords(axes.flat, -0.175)
fig.set_figheight(4)
fig.set_figwidth(18)
plt.draw()
plt.tight_layout()
plt.subplots_adjust(wspace=0.4)
pths = [fig_path.joinpath("response_time_accuracy.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
order = ['exp', 'th', 'bqp']
titles = {
'exp': "Human",
'oc': "Oracle",
'th': "Threshold",
'hc': "HC",
'bq': "BQ (equal prior)",
'bqp': "BQ (unequal prior)"
}
colors = {
'same': 'r',
'flipped': 'b'
}
means = pd.read_csv(
results_path.joinpath("theta_time_stimulus.csv"))\
.set_index(['stimulus', 'flipped', 'model'])\
.groupby(level='stimulus').get_group(2)
fig, axes = plt.subplots(1, len(order), sharex=True)
for i, key in enumerate(order):
ax = axes[i]
df = means.groupby(level='model').get_group(key)
for flipped, stats in df.groupby(level='flipped'):
if key == 'exp':
median = stats['median'] / 1000.
lower = (stats['median'] - stats['lower']) / 1000.
upper = (stats['upper'] - stats['median']) / 1000.
else:
median = stats['median']
lower = stats['median'] - stats['lower']
upper = stats['upper'] - stats['median']
ax.errorbar(
stats['modtheta'], median,
yerr=[lower, upper], lw=3,
color=colors[flipped],
ecolor=colors[flipped])
ax.set_xticks(np.arange(0, 200, 30))
ax.set_xlim(-10, 190)
ax.set_xlabel("Rotation")
ax.set_title(titles[key], fontsize=14)
if key == 'exp':
ax.set_ylabel("Response time", fontsize=14)
else:
ax.set_ylabel("Number of actions", fontsize=14)
# util.sync_ylims(axes[order.index('bq')], axes[order.index('bqp')])
for ax in axes.flat:
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
ax.set_axis_bgcolor('0.95')
p0 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors['same'],
ec=colors['same'])
p1 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors['flipped'],
ec=colors['flipped'])
leg = axes[0].legend(
[p0, p1], ["same", "flipped"],
numpoints=1, fontsize=12,
loc='lower right')
frame = leg.get_frame()
frame.set_edgecolor('#FFFFFF')
util.sync_ylabel_coords(axes.flat, -0.175)
fig.set_figheight(3)
fig.set_figwidth(9)
plt.draw()
plt.tight_layout()
plt.subplots_adjust(wspace=0.4)
pths = [fig_path.joinpath("response_time_stimulus.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
keys = ['exp', 'oc', 'th', 'hc', 'bq', 'bqp']
corrs = pd.read_csv(results_path.joinpath("theta_accuracy_corrs.csv"))\
.set_index(['model', 'flipped'])\
.unstack('flipped')\
.reindex(keys)
fig, ax = plt.subplots()
width = 1
offset = width * 5 / 2.
colors = {
'same': '#ff5555',
'flipped': '#5555ff'
}
titles = {
'exp': "Human",
'oc': "Oracle",
'th': "Threshold",
'hc': "HC",
'bq': "BQ\n(equal)",
'bqp': "BQ\n(unequal)"
}
order = ['same', 'flipped']
for i, flipped in enumerate(order):
y = corrs.xs(flipped, axis=1, level='flipped')
median = y['median']
lerr = median - y['lower']
uerr = y['upper'] - median
ax.bar(
i * width + np.arange(len(median)) * offset,
-median,
yerr=[-uerr, -lerr],
color=colors[flipped],
ecolor='k',
width=width,
edgecolor='none',
capsize=0)
ax.set_ylim(0, 1)
ax.set_xlim(-width / 2., len(median) * offset)
ax.set_xticks(np.arange(len(median)) * offset + width)
ax.set_xticklabels([titles[key] for key in keys], fontsize=10)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
ax.set_ylabel(r"Spearman correlation ($r_s$)", fontsize=14)
p0 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors['same'],
ec=colors['same'])
p1 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors['flipped'],
ec=colors['flipped'])
leg = ax.legend(
[p0, p1], ["\"same\" pairs", "\"flipped\" pairs"],
numpoints=1, fontsize=12,
loc='upper right',
bbox_to_anchor=(1, 1.05))
frame = leg.get_frame()
frame.set_facecolor('0.9')
frame.set_edgecolor('#FFFFFF')
fig.set_figwidth(5)
fig.set_figheight(3)
plt.draw()
plt.tight_layout()
pths = [fig_path.joinpath("theta_accuracy_corrs.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
order = ['oc', 'th', 'hc', 'bq', 'bqp']
titles = {
'oc': "Oracle",
'th': "Threshold",
'hc': "HC",
'bq': "BQ (equal prior)",
'bqp': "BQ (unequal prior)"
}
results = pd.read_csv(
results_path.joinpath("response_time_means.csv"))\
.set_index(['stimulus', 'theta', 'flipped', 'model'])['median']\
.groupby(level='model')\
.apply(util.zscore)\
.unstack('model')
corrs = pd.read_csv(
results_path.joinpath("response_time_corrs.csv"))\
.set_index(['model', 'flipped'])\
.stack()\
.unstack(['model', 'flipped'])\
.xs('all', level='flipped', axis=1)
fig, axes = plt.subplots(1, len(order), sharey=True, sharex=True)
for i, key in enumerate(order):
ax = axes[i]
xmean = results[key]
ymean = results['exp']
corr = util.report_pearson.format(**dict(corrs[key]))
ax.plot([-4, 4], [-4, 4], ls='--', color='#666666')
ax.plot(xmean, ymean, '.', alpha=0.9, color='k')
ax.set_xticks([])
ax.set_yticks([])
ax.set_title(titles[key], fontsize=14)
ax.set_xlabel(corr, fontsize=14)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
ax.set_axis_bgcolor('0.95')
util.sync_xlims(axes[order.index('bq')], axes[order.index('bqp')])
axes[0].set_xlim(-4, 4)
axes[0].set_ylim(-4, 4)
axes[0].set_ylabel("Human", fontsize=14)
fig.set_figheight(2.5)
fig.set_figwidth(16)
plt.draw()
plt.tight_layout()
plt.subplots_adjust(wspace=0.1, bottom=0.2, top=0.9)
pths = [fig_path.joinpath("response_time_scatters.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
def plot(results_path, fig_path):
keys = ['exp', 'oc', 'th', 'hc', 'bq', 'bqp']
human = pd.read_csv(results_path.joinpath("human_corrs.csv"))\
.set_index('measure')
human['flipped'] = 'all'
time = pd.read_csv(results_path.joinpath("response_time_corrs.csv"))
time = time.append(human.xs('time'))\
.set_index(['model', 'flipped'])\
.unstack('flipped')\
.reindex(keys)
acc = pd.read_csv(results_path.joinpath("accuracy_corrs.csv"))
acc = acc.append(human.xs('accuracy'))\
.set_index(['model', 'flipped'])\
.unstack('flipped')\
.reindex(keys)
fig, ax = plt.subplots()
width = 1
offset = width * 5 / 2.
titles = {
'exp': "Human",
'oc': "Oracle",
'th': "Threshold",
'hc': "HC",
'bq': "BQ\n(equal)",
'bqp': "BQ\n(unequal)"
}
colors = ['#55cc77', '#cc55aa']
y = time.xs('all', axis=1, level='flipped')
median = y['median']
lerr = median - y['lower']
uerr = y['upper'] - median
ax.bar(
np.arange(len(median)) * offset,
median,
yerr=[lerr, uerr],
color=colors[0],
ecolor='k',
width=width,
edgecolor='none',
capsize=0)
y = acc.xs('all', axis=1, level='flipped')
median = y['median']
lerr = median - y['lower']
uerr = y['upper'] - median
ax.bar(
width + np.arange(len(median)) * offset,
median,
yerr=[lerr, uerr],
color=colors[1],
ecolor='k',
width=width,
edgecolor='none',
capsize=0)
ax.set_ylim(0, 1)
ax.set_xlim(-width / 2., len(median) * offset)
ax.set_xticks(np.arange(len(median)) * offset + width)
ax.set_xticklabels([titles[key] for key in keys], fontsize=10)
util.clear_right(ax)
util.clear_top(ax)
util.outward_ticks(ax)
ax.set_ylabel(r"Pearson correlation ($r$)", fontsize=14)
p0 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors[0],
ec=colors[0])
p1 = plt.Rectangle(
(0, 0), 1, 1,
fc=colors[1],
ec=colors[1])
leg = ax.legend(
[p0, p1], ["response time", "accuracy"],
numpoints=1, fontsize=12,
loc='upper right',
bbox_to_anchor=(1, 1.05))
frame = leg.get_frame()
frame.set_facecolor('0.9')
frame.set_edgecolor('#FFFFFF')
fig.set_figwidth(5)
fig.set_figheight(3)
plt.draw()
plt.tight_layout()
pths = [fig_path.joinpath("human_model_corrs.%s" % ext)
for ext in ('png', 'pdf')]
for pth in pths:
util.save(pth, close=False)
return pths
