def calc_easy_bts_binary(responses, meta_true):
"""Calculate easiest binary version of bts using own answers and predicted percentage of ppl endorsing true
Args:
responses: list where elt is 0 if subject said false and 1 if subject said true.
meta_true: list where elt is subject's prediction of fraction of ppl endorsing true.
Returns:
dict where each field (bts, surprise, accuracy) points to an np.array with an elt for each subject.
"""
assert len(responses) == len(meta_true)
do_assert = True
xbar_true = jmutils.mean(responses)
xbar_true = jmutils.bound(xbar_true, 0.01, 0.99)
xbar = [1 - xbar_true, xbar_true]
meta_true_trimmed = [jmutils.bound(mt, 0.01, 0.99) for mt in meta_true]
meta_false = [1 - et for et in meta_true_trimmed]
log_ybar = [jmutils.log_geo_mean(meta_false), jmutils.log_geo_mean(meta_true_trimmed)]
#if (xbar_true != jmutils.mean(responses)) or (len(responses) == 1):
# do_assert = False
easy_bts = calc_generic_bts(xbar, log_ybar, responses, zip(meta_false, meta_true_trimmed))
return easy_bts
def calc_log_ybar(predictions):
"""Calculate log(ybar) given meta predictions.
Args:
predictions: list where each sublist gives the predictions for a subject of the fraction of ppl endorsing each constructed answer.
Returns: list where each element is log geometric mean of predictions about each answer.
"""
num_answers = len(predictions[0])
log_ybar = [0] * num_answers
for k in range(num_answers):
ans_predictions = [s[k] for s in predictions]
if ans_predictions.count(0) > 0:
print "got a zero"
log_ybar[k] = jmutils.log_geo_mean(ans_predictions) #log geo mean is mean of logs.
return log_ybar
