def get_results(ag, label):
accuracy, false_positives, false_negatives, c_value= find_best(ag, c_values,\
thresholds)
print ""
#print stuff here
print "Permutation: " + label
print "Accuracy: " + str(accuracy)
print "False positives: " + str(false_positives)
print "False negatives: " + str(false_negatives)
print "c_value: " + str(c_value)
print ""
def get_results(sa, label):
ag= create_strict_article_group_from_sa(sa, 1000, 20)
accuracy, false_positives, false_negatives, c_value= find_best(ag, c_values,\
thresholds)
print ""
#print stuff here
print "Permutation: " + label
print "Accuracy: " + str(accuracy)
print "False positives: " + str(false_positives)
print "False negatives: " + str(false_negatives)
print "c_value: " + str(c_value)
print ""
def get_results(ag, label):
if os.path.exists("positives.ex"):
os.remove("positives.ex")
os.remove("negatives.ex")
train_sets, validation_sets, test_set = subsets(ag.svm_ready_examples, 5)
for threshold in thresholds:
accuracy, true_plus, true_minus, false_positives, false_negatives, c_value = find_best(
c_values, threshold, train_sets, validation_sets, test_set
)
print str(threshold) + "\t" + str(false_positives / (true_minus + false_negatives)) + "\t" + str(
true_plus / (true_plus + false_positives)
)