def foo(self, actual, prediction):
_, _, gt_data_for_sum = create_target_table(self.sub_gcd_res, actual)
_, _, actual_data_for_sum = create_target_table(self.sub_gcd_res, prediction[:, 1])
all_accuracies = dict([
[rep, accuracy_by_repetition(actual_data_for_sum, gt_data_for_sum,
number_of_repetition=rep)]
for rep in range(1,11)])
print ", ".join([
"acc {}:{}".format(k, v)
for k, v in all_accuracies.iteritems()])
return all_accuracies
# plt.show()
auc_score = roc_auc_score(test_target_gcd, test_prediction[:, 1])
print "auc_score:{0}".format(auc_score)
sub_gcd_res = create_data_for_compare_by_repetition(file_name)
# sub_gcd_res = dict(train_trial=gcd_res['train_trial'][gcd_res['train_mode'] != 1],
# train_block=gcd_res['train_block'][gcd_res['train_mode'] != 1],
# stimulus=gcd_res['stimulus'][gcd_res['train_mode'] != 1])
_, _, gt_data_for_sum = create_target_table(sub_gcd_res,
test_target_gcd)
_, _, actual_data_for_sum = create_target_table(
sub_gcd_res, test_prediction[:, 1])
print "accuracy_by_repetition {0}".format(
accuracy_by_repetition(actual_data_for_sum,
gt_data_for_sum,
number_of_repetition=10))
results.append(
dict(subject_name=subject_name,
test_prediction=test_prediction,
auc_score=auc_score))
break
# In[4]:
import keras
# In[20]:
# In[73]: