def test():
ret = ''
ret += 'query=' + str(util.get_query()) + '\n'
ret += '\n'
ret += 'q=None\n'
ret += 'query=' + str(util.get_query(q=None)) + '\n'
ret += '\n'
ret += 'a\n'
ret += 'query=' + str(util.get_query('a')) + '\n'
ret += '\n'
ret += '?a=1 , a\n'
ret += 'query=' + str(util.get_query('a', 'a=1')) + '\n'
ret += '\n'
ret += '?a=1&b=2 , b\n'
ret += 'query=' + str(util.get_query('b', 'a=1&b=2')) + '\n'
ret += '\n'
ret += '?a=1&b=2&a=3 , a\n'
ret += 'query=' + str(util.get_query('a', 'a=1&b=2&a=3')) + '\n'
ret += '\n'
ret += '?a=1&a=2+%26&b=3 , a\n'
ret += 'query=' + str(util.get_query('a', 'a=1&a=2+%26&b=3')) + '\n'
ret += '\n'
ret += '?%E3%81%82=%E3%81%84 , %E3%81%82\n'
ret += 'query=' + str(util.get_query('あ', '%E3%81%82=%E3%81%84')) + '\n'
ret += '\n'
return ret
def plot(dest, results_path, counterfactual, likelihood):
if likelihood == 'ipe':
likelihood = 'ipe_' + util.get_query()
# load in the model data, which includes fitted parameters
data = pd\
.read_csv(os.path.join(results_path, 'model_belief_by_trial_fit.csv'))\
.groupby(['fitted', 'counterfactual', 'likelihood'])\
.get_group((True, counterfactual, likelihood))\
.drop_duplicates(['version', 'model', 'pid', 'B'])\
.set_index(['version', 'model', 'pid'])\
.sortlevel()
# double check that there is exactly one parameter for each pid
assert data.index.is_unique
# plotting config stuff
plot_config = util.load_config()["plots"]
all_colors = plot_config["colors"]
colors = {'static': all_colors[1], 'learning': all_colors[0]}
# create the figure and plot the histograms
fig, axes = plt.subplots(2, 3, sharex=True)
for i, version in enumerate(['H', 'G', 'I']):
for j, model in enumerate(['static', 'learning']):
hist(axes[j, i], data.ix[(version, model)]['B'],
plot_config['darkgrey'])
# set titles and axis labels
for i in range(3):
axes[0, i].set_title('Experiment {}'.format(i + 1), y=1.05)
for ax in axes[:, 0]:
ax.set_ylabel("% participants")
for ax in axes[1]:
ax.set_xlabel(r"Best fit learning rate ($\beta$)")
for i, label in enumerate(['Static', 'Learning']):
mid = sum(axes[i, 0].get_ylim()) / 2.0
axes[i, 0].text(
-2.6,
mid,
label,
rotation=90,
fontsize=12, # same as title font size
verticalalignment='center')
# clear top and right axis lines
sns.despine()
# set figure size
fig.set_figwidth(6.5)
fig.set_figheight(3.25)
plt.draw()
plt.tight_layout()
plt.subplots_adjust(left=0.14)
# save
for pth in dest:
util.save(pth, close=False)
def load_virtual(self):
from util import get_query
for tb_name, vtable in self._connobj.vtables():
try:
self._vtables[tb_name] = None
query = get_query(vtable).bind(self._connobj).object
if hasattr(query, 'alias'):
query = query.alias(tb_name)
self._vtables[tb_name] = query
logger.info('Load virtual table success: %s' % tb_name)
except Exception, e:
logger.error('Load virtual table(%s) failed: %s' %
(tb_name, e),
exc_info=True)
def handle_query(request, name, **kwargs):
''' Handles generic view.
Category is where this should be place (per student, per problem, etc.)
Name is specific
'''
global query_object
if query_object is None:
from util import get_query
query_object = get_query(None)
if name[0] == '_':
raise SuspiciousOperation(name+' called')
kwargs.update(request.POST.items())
kwargs.update(request.GET.items())
results = query_object.__getattr__(name)(**kwargs)
if isinstance(results, basestring):
return HttpResponse(results)
else:
return HttpResponse(json.dumps(results))
def handle_query(request, name, **kwargs):
''' Handles generic view.
Category is where this should be place (per student, per problem, etc.)
Name is specific
'''
global query_object
if query_object is None:
from util import get_query
query_object = get_query(None)
if name[0] == '_':
raise SuspiciousOperation(name+' called')
kwargs.update(request.POST.items())
kwargs.update(request.GET.items())
results = query_object.__getattr__(name)(**kwargs)
if isinstance(results, basestring):
return HttpResponse(results)
else:
return HttpResponse(json.dumps(results))
def test():
ret = 'query=' + str(util.get_query()) + '\n'
ret += 'a=' + str(util.get_query('a')) + '\n'
ret += 'b=' + str(util.get_query('b')) + '\n'
return ret
def plot(dest, results_path, counterfactual, likelihood):
if likelihood == 'ipe':
likelihood = 'ipe_' + util.get_query()
# load in the human responses
human = pd\
.read_csv(os.path.join(results_path, 'human_mass_accuracy_by_trial.csv'))\
.groupby('kappa0')\
.get_group('all')\
.groupby(['version', 'num_mass_trials', 'trial'])\
.filter(filter_trials)\
.set_index('version')
# load in the model responses
model = pd\
.read_csv(os.path.join(results_path, 'model_mass_accuracy_by_trial.csv'))\
.groupby(['likelihood', 'counterfactual', 'kappa0'])\
.get_group((likelihood, counterfactual, 'all'))\
.groupby(['version', 'num_mass_trials', 'trial', 'model', 'fitted'])\
.filter(filter_trials)\
.set_index(['version', 'fitted', 'model'])\
.sortlevel()
# load in the log likelihood ratios
llhr = pd\
.read_csv(os.path.join(results_path, 'model_log_lh_ratios.csv'))\
.groupby(['likelihood', 'counterfactual', 'fitted'])\
.get_group((likelihood, counterfactual, True))\
.groupby(['version', 'num_mass_trials'])\
.filter(filter_trials)\
.set_index(['version'])['llhr']
# load in the bayes factors
factors = pd\
.read_csv(os.path.join(results_path, 'bayes_factors.csv'))\
.groupby(['likelihood', 'counterfactual'])\
.get_group((likelihood, counterfactual))\
.groupby(['version', 'num_mass_trials'])\
.filter(filter_trials)\
.set_index(['version'])['logK']
# colors and line styles
plot_config = util.load_config()["plots"]
darkgrey = plot_config["darkgrey"]
lines = {'human': '-', 'static': ':', 'learning': '--'}
colors = {'human': darkgrey, 'static': darkgrey, 'learning': darkgrey}
fig, axes = plt.subplots(2, 2, sharey=True)
# top left subplot: IPE model for experiment 1
plot_all(axes[0, 0], None, model.ix[('H', False)], lines, colors)
axes[0, 0].set_title('(a) IPE observer model')
axes[0, 0].set_xlabel('Trial')
axes[0, 0].set_ylabel('Pr(correct ratio chosen)')
axes[0, 0].set_xticks([1, 5, 10, 15, 20])
axes[0, 0].set_xlim([1, 20])
axes[0, 0].set_ylim(0.48, 1.02)
# top right subplot: experiment 1a
plot_all(axes[0, 1], human.ix['H'], model.ix[('H', True)], lines, colors)
axes[0, 1].set_title('(b) Experiment 1')
axes[0, 1].set_xlabel('Trial')
axes[0, 1].set_xticks([1, 5, 10, 15, 20])
axes[0, 1].set_xlim([1, 20])
add_llhr_and_factor(axes[0, 1], llhr.ix['H'], factors.ix['H'])
# bottom left subplot: experiment 1b
plot_all(axes[1, 0], human.ix['G'], model.ix[('G', True)], lines, colors)
axes[1, 0].set_title('(c) Experiment 2')
axes[1, 0].set_xlabel('Trial')
axes[1, 0].set_ylabel('Pr(correct ratio chosen)')
axes[1, 0].set_xticks([1, 2, 3, 4, 6, 9, 14, 20])
axes[1, 0].set_xlim([1, 20])
add_llhr_and_factor(axes[1, 0], llhr.ix['G'], factors.ix['G'])
# bottom right subplot: experiment 2 (between subjects)
plot_all(axes[1, 1], human.ix['I'], model.ix[('I', True)], lines, colors)
axes[1, 1].set_title('(d) Experiment 3')
axes[1, 1].set_xlabel('Trial')
axes[1, 1].set_xticks([1, 2, 3, 5, 10])
axes[1, 1].set_xlim([1, 10])
add_llhr_and_factor(axes[1, 1], llhr.ix['I'], factors.ix['I'])
# clear top and right axis lines
sns.despine()
# make the legend
make_legend(axes[0, 0], lines, colors)
# set figure size
fig.set_figwidth(6)
fig.set_figheight(4.8)
plt.draw()
plt.tight_layout()
# save
for pth in dest:
util.save(pth, close=False)