def test_get_best_da_hyp(self):
# Test case when only one dai should be included in the hyp.
dacn = DialogueActConfusionNetwork()
dacn.add(0.2, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.1, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(use_log=False)
self.assertAlmostEqual(best_hyp.prob, 0.8 * 0.7 * 0.9)
self.assertEqual(len(best_hyp.da), 1)
# Test case when 2 dais should be included in the hyp.
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(use_log=False)
self.assertAlmostEqual(best_hyp.prob, 0.9 * 0.7 * 0.9)
self.assertEqual(len(best_hyp.da), 2)
# Test the case with logarithms.
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(use_log=True)
self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.7 * 0.9))
self.assertEqual(len(best_hyp.da), 2)
# Test the case with manual thresholds.
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(
use_log=True, threshold=0.1, thresholds={
DialogueActItem(dai='inform(food=chinese)'): 0.5,
DialogueActItem(dai='inform(food=czech)'): 0.9,
DialogueActItem(dai='inform(food=russian)'): 0.5
})
# Test food=czech should NOT be included.
self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.3 * 0.9))
self.assertEqual(len(best_hyp.da), 1)
self.assertTrue(not DialogueActItem(dai='inform(food=czech)') in best_hyp.da)
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(
use_log=True, threshold=0.1, thresholds={
DialogueActItem(dai='inform(food=chinese)'): 0.5,
DialogueActItem(dai='inform(food=czech)'): 0.5,
DialogueActItem(dai='inform(food=russian)'): 0.5
})
# Test food=czech should be included.
self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.7 * 0.9))
self.assertEqual(len(best_hyp.da), 2)
self.assertTrue(DialogueActItem(dai='inform(food=czech)') in best_hyp.da)
def test_get_best_da_hyp(self):
# Test case when only one dai should be included in the hyp.
dacn = DialogueActConfusionNetwork()
dacn.add(0.2, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.1, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(use_log=False)
self.assertAlmostEqual(best_hyp.prob, 0.8 * 0.7 * 0.9)
self.assertEqual(len(best_hyp.da), 1)
# Test case when 2 dais should be included in the hyp.
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(use_log=False)
self.assertAlmostEqual(best_hyp.prob, 0.9 * 0.7 * 0.9)
self.assertEqual(len(best_hyp.da), 2)
# Test the case with logarithms.
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(use_log=True)
self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.7 * 0.9))
self.assertEqual(len(best_hyp.da), 2)
# Test the case with manual thresholds.
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(
use_log=True,
threshold=0.1,
thresholds={
DialogueActItem(dai='inform(food=chinese)'): 0.5,
DialogueActItem(dai='inform(food=czech)'): 0.9,
DialogueActItem(dai='inform(food=russian)'): 0.5
})
# Test food=czech should NOT be included.
self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.3 * 0.9))
self.assertEqual(len(best_hyp.da), 1)
self.assertTrue(not DialogueActItem(
dai='inform(food=czech)') in best_hyp.da)
dacn = DialogueActConfusionNetwork()
dacn.add(0.1, DialogueActItem(dai='inform(food=chinese)'))
dacn.add(0.7, DialogueActItem(dai='inform(food=czech)'))
dacn.add(0.9, DialogueActItem(dai='inform(food=russian)'))
best_hyp = dacn.get_best_da_hyp(
use_log=True,
threshold=0.1,
thresholds={
DialogueActItem(dai='inform(food=chinese)'): 0.5,
DialogueActItem(dai='inform(food=czech)'): 0.5,
DialogueActItem(dai='inform(food=russian)'): 0.5
})
# Test food=czech should be included.
self.assertAlmostEqual(best_hyp.prob, math.log(0.9 * 0.7 * 0.9))
self.assertEqual(len(best_hyp.da), 2)
self.assertTrue(
DialogueActItem(dai='inform(food=czech)') in best_hyp.da)
