def main():
parser = argparse.ArgumentParser(description="Anavec test and evaluation script", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--referencefile', type=str, help="Reference list (particular format)", action='store',required=True)
setup_argparser(parser) #for anavec
args = parser.parse_args()
references = []
alltestwords = []
allmask = []
correct = 0
with open(args.referencefile, 'r',encoding='utf-8') as f:
windowmask = [InputTokenState.CORRECT]*2 + [InputTokenState.CORRECTABLE] + [InputTokenState.CORRECT, InputTokenState.CORRECT | InputTokenState.EOL ]
for line in f:
fields = line.strip().split('#')
error = fields[0]
correction = fields[1]
context = fields[2].split(' ')
windowwords = context[:2] + [error] + context[2:]
alltestwords += windowwords
allmask += windowmask
references.append( (windowwords, windowmask, correction) )
l = len(references)
print("Read " + str(l) + " reference instances",file=sys.stderr)
assert len(alltestwords) == len(allmask)
corrector = Corrector(**vars(args))
observations = []
goals = []
for result, (testwords, mask, reference) in zip(corrector.correct(alltestwords, allmask), references):
output = list(sorted(result['candidatetree'][2][1], key=lambda x: -1 * x.score))
if output:
if reference == output[0].text:
match = "BEST"
elif reference in [c.text for c in output]:
match = "YES"
else:
match = "NO"
print(match + "\t[" + reference + "]\t" + testwords[2] + "\t-->\t" + "\t".join([ c.text + "["+str(c.score)+"]" for c in output]))
output = output[0].text
else:
output = ""
print("NO\t[" + reference + "]\t" + testwords[2] + "\t-->\tNO-SUGGESTIONS! [0.0]")
observations.append(output)
goals.append(reference)
evaluation = ClassEvaluation(goals, observations)
print("# Precision = ", evaluation.precision())
print("# Recall = ", evaluation.recall())
def test001(self):
e = ClassEvaluation(self.goals, self.observations)
print
print e
print e.confusionmatrix()
def test001(self):
e = ClassEvaluation(self.goals, self.observations)
print()
print(e)
print(e.confusionmatrix())
def test001(self):
"""Class evaluation test -- (See also http://en.wikipedia.org/wiki/Confusion_matrix , using same data)"""
e = ClassEvaluation(self.goals, self.observations)
print
print(e)
print(e.confusionmatrix())
self.assertEqual(e.tp['cat'], 5)
self.assertEqual(e.fp['cat'], 2)
self.assertEqual(e.tn['cat'], 17)
self.assertEqual(e.fn['cat'], 3)
self.assertEqual(e.tp['rabbit'], 11)
self.assertEqual(e.fp['rabbit'], 1)
self.assertEqual(e.tn['rabbit'], 13)
self.assertEqual(e.fn['rabbit'], 2)
self.assertEqual(e.tp['dog'], 3)
self.assertEqual(e.fp['dog'], 5)
self.assertEqual(e.tn['dog'], 16)
self.assertEqual(e.fn['dog'], 3)
self.assertEqual(round(e.precision('cat'), 6), 0.714286)
self.assertEqual(round(e.precision('rabbit'), 6), 0.916667)
self.assertEqual(round(e.precision('dog'), 6), 0.375000)
self.assertEqual(round(e.recall('cat'), 6), 0.625000)
self.assertEqual(round(e.recall('rabbit'), 6), 0.846154)
self.assertEqual(round(e.recall('dog'), 6), 0.500000)
self.assertEqual(round(e.fscore('cat'), 6), 0.666667)
self.assertEqual(round(e.fscore('rabbit'), 6), 0.880000)
self.assertEqual(round(e.fscore('dog'), 6), 0.428571)
self.assertEqual(round(e.accuracy(), 6), 0.703704)
def test001(self):
"""Class evaluation test -- (See also http://en.wikipedia.org/wiki/Confusion_matrix , using same data)"""
e = ClassEvaluation(self.goals, self.observations)
print
print(e)
print(e.confusionmatrix())
self.assertEqual(e.tp['cat'], 5)
self.assertEqual(e.fp['cat'], 2)
self.assertEqual(e.tn['cat'], 17)
self.assertEqual(e.fn['cat'], 3)
self.assertEqual(e.tp['rabbit'], 11)
self.assertEqual(e.fp['rabbit'], 1)
self.assertEqual(e.tn['rabbit'], 13)
self.assertEqual(e.fn['rabbit'], 2)
self.assertEqual(e.tp['dog'], 3)
self.assertEqual(e.fp['dog'], 5)
self.assertEqual(e.tn['dog'], 16)
self.assertEqual(e.fn['dog'], 3)
self.assertEqual( round(e.precision('cat'),6), 0.714286)
self.assertEqual( round(e.precision('rabbit'),6), 0.916667)
self.assertEqual( round(e.precision('dog'),6), 0.375000)
self.assertEqual( round(e.recall('cat'),6), 0.625000)
self.assertEqual( round(e.recall('rabbit'),6), 0.846154)
self.assertEqual( round(e.recall('dog'),6),0.500000)
self.assertEqual( round(e.fscore('cat'),6), 0.666667)
self.assertEqual( round(e.fscore('rabbit'),6), 0.880000)
self.assertEqual( round(e.fscore('dog'),6),0.428571)
self.assertEqual( round(e.accuracy(),6), 0.703704)
