def main(domainxml, trainingsetcsv, manifold_value, restrictionstxt):
restrictions = dataset.restrictions_from_text(restrictionstxt)
cols, data = dataset.read(trainingsetcsv.read(), True, restrictions)
expected, actual, expected_hunked, actual_hunked = sampling.cross_validate(data, list(cols), manifold_value)
print("Overall confusion matrix:")
print(sampling.confusion_matrix(expected, actual))
print("\nOverall recall:")
print(sampling.recall(expected, actual, "Obama"))
print("\nOverall precision:")
print(sampling.precision(expected, actual, "Obama"))
print("\nOverall pf:")
print(sampling.pf(expected, actual, "Obama"))
print("\nOverall f-measure:")
print(sampling.f_measure(expected, actual, "Obama"))
print("\nOverall accuracy:")
print(sampling.accuracy(expected, actual))
print("\nAverage accuracy:")
print(sum(sampling.accuracy(e, a) for e, a in zip(expected_hunked, actual_hunked)) / len(expected_hunked))
print("\nOverall error rate:")
print(sampling.error_rate(expected, actual))
print("\nAverage error rate:")
print(sum(sampling.error_rate(e, a) for e, a in zip(expected_hunked, actual_hunked)) / len(expected_hunked))
def test_precision(self):
# The values in the training set
expected = ['a', 'b', 'a', 'b']
# The results from the classifier
actual = ['a', 'b', 'a', 'a']
# As though we're asking the question "does record belong in class a?"
positive = 'a'
result = sampling.precision(expected, actual, positive)
TP = 2
TN = 1
FP = 1
self.assertEqual(result, TP/(TP+FP))