def test_evaluate(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics,
self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics,
self.vocabulary)
word_id = numpy_optimizer.get_word_id('d')
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 1 if orig_class_id != 1 else 0
orig_ll = numpy_optimizer.log_likelihood()
self.assertTrue(
numpy.isclose(orig_ll, theano_optimizer.log_likelihood()))
ll_diff = numpy_optimizer._evaluate(word_id, new_class_id)
self.assertTrue(
numpy.isclose(ll_diff,
theano_optimizer._evaluate(word_id, new_class_id)))
numpy_optimizer._move(word_id, new_class_id)
new_ll = numpy_optimizer.log_likelihood()
self.assertFalse(numpy.isclose(orig_ll, new_ll))
self.assertTrue(numpy.isclose(orig_ll + ll_diff, new_ll))
theano_optimizer._move(word_id, new_class_id)
self.assertTrue(
numpy.isclose(new_ll, theano_optimizer.log_likelihood()))
def test_move_and_back(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics, self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_counts = numpy.copy(numpy_optimizer._class_counts)
orig_cc_counts = numpy.copy(numpy_optimizer._cc_counts)
orig_cw_counts = numpy.copy(numpy_optimizer._cw_counts)
orig_wc_counts = numpy.copy(numpy_optimizer._wc_counts)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
numpy_optimizer._move(word_id, new_class_id)
theano_optimizer._move(word_id, new_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertEqual(numpy.count_nonzero(numpy_optimizer._class_counts != orig_class_counts), 2)
self.assertEqual(numpy.sum(numpy_optimizer._class_counts), numpy.sum(orig_class_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cc_counts != orig_cc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cc_counts), numpy.sum(orig_cc_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cw_counts != orig_cw_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cw_counts), numpy.sum(orig_cw_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._wc_counts != orig_wc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._wc_counts), numpy.sum(orig_wc_counts))
numpy_optimizer._move(word_id, orig_class_id)
theano_optimizer._move(word_id, orig_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertTrue(numpy.array_equal(numpy_optimizer._class_counts, orig_class_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cc_counts, orig_cc_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cw_counts, orig_cw_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._wc_counts, orig_wc_counts))
def test_move_and_back(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics, self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_counts = numpy.copy(numpy_optimizer._class_counts)
orig_cc_counts = numpy.copy(numpy_optimizer._cc_counts)
orig_cw_counts = numpy.copy(numpy_optimizer._cw_counts)
orig_wc_counts = numpy.copy(numpy_optimizer._wc_counts)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
numpy_optimizer._move(word_id, new_class_id)
theano_optimizer._move(word_id, new_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertEqual(numpy.count_nonzero(numpy_optimizer._class_counts != orig_class_counts), 2)
self.assertEqual(numpy.sum(numpy_optimizer._class_counts), numpy.sum(orig_class_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cc_counts != orig_cc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cc_counts), numpy.sum(orig_cc_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._cw_counts != orig_cw_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._cw_counts), numpy.sum(orig_cw_counts))
self.assertGreater(numpy.count_nonzero(numpy_optimizer._wc_counts != orig_wc_counts), 0)
self.assertEqual(numpy.sum(numpy_optimizer._wc_counts), numpy.sum(orig_wc_counts))
numpy_optimizer._move(word_id, orig_class_id)
theano_optimizer._move(word_id, orig_class_id)
self.assert_optimizers_equal(numpy_optimizer, theano_optimizer)
self.assertTrue(numpy.array_equal(numpy_optimizer._class_counts, orig_class_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cc_counts, orig_cc_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._cw_counts, orig_cw_counts))
self.assertTrue(numpy.array_equal(numpy_optimizer._wc_counts, orig_wc_counts))
def test_move_and_recompute(self):
optimizer1 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = optimizer1.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
optimizer1._word_to_class[word_id] = new_class_id
counts = optimizer1._compute_class_statistics(
optimizer1._word_counts, optimizer1._ww_counts,
optimizer1._word_to_class)
class_counts = numpy.zeros(optimizer1.num_classes, 'int32')
cc_counts = numpy.zeros(
(optimizer1.num_classes, optimizer1.num_classes), dtype='int32')
cw_counts = numpy.zeros(
(optimizer1.num_classes, optimizer1.vocabulary_size),
dtype='int32')
wc_counts = numpy.zeros(
(optimizer1.vocabulary_size, optimizer1.num_classes),
dtype='int32')
for wid, cid in enumerate(optimizer1._word_to_class):
class_counts[cid] += optimizer1._word_counts[wid]
for left_wid, right_wid in zip(*optimizer1._ww_counts.nonzero()):
count = optimizer1._ww_counts[left_wid, right_wid]
left_cid = optimizer1._word_to_class[left_wid]
right_cid = optimizer1._word_to_class[right_wid]
cc_counts[left_cid, right_cid] += count
cw_counts[left_cid, right_wid] += count
wc_counts[left_wid, right_cid] += count
self.assertTrue(numpy.array_equal(class_counts, counts[0]))
self.assertTrue(numpy.array_equal(cc_counts, counts[1]))
self.assertTrue(numpy.array_equal(cw_counts, counts[2]))
self.assertTrue(numpy.array_equal(wc_counts, counts[3]))
optimizer1._class_counts = counts[0]
optimizer1._cc_counts = counts[1]
optimizer1._cw_counts = counts[2]
optimizer1._wc_counts = counts[3]
optimizer2 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer2.get_word_class(word_id)
optimizer2._move(word_id, new_class_id)
self.assertEqual(
numpy.count_nonzero(
optimizer1._class_counts != optimizer2._class_counts), 0)
self.assertEqual(
numpy.count_nonzero(
optimizer1._cc_counts != optimizer2._cc_counts), 0)
self.assertEqual(
numpy.count_nonzero(
optimizer1._cw_counts != optimizer2._cw_counts), 0)
self.assertEqual(
numpy.count_nonzero(
optimizer1._wc_counts != optimizer2._wc_counts), 0)
optimizer3 = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer3.get_word_class(word_id)
optimizer3._move(word_id, new_class_id)
self.assert_optimizers_equal(optimizer2, optimizer3)
def test_move_and_recompute(self):
optimizer1 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
word_id = self.vocabulary.word_to_id['d']
orig_class_id = optimizer1.get_word_class(word_id)
new_class_id = 3 if orig_class_id != 3 else 4
optimizer1._word_to_class[word_id] = new_class_id
counts = optimizer1._compute_class_statistics(optimizer1._word_counts,
optimizer1._ww_counts,
optimizer1._word_to_class)
class_counts = numpy.zeros(optimizer1.num_classes, 'int32')
cc_counts = numpy.zeros((optimizer1.num_classes, optimizer1.num_classes), dtype='int32')
cw_counts = numpy.zeros((optimizer1.num_classes, optimizer1.vocabulary_size), dtype='int32')
wc_counts = numpy.zeros((optimizer1.vocabulary_size, optimizer1.num_classes), dtype='int32')
for wid, cid in enumerate(optimizer1._word_to_class):
class_counts[cid] += optimizer1._word_counts[wid]
for left_wid, right_wid in zip(*optimizer1._ww_counts.nonzero()):
count = optimizer1._ww_counts[left_wid, right_wid]
left_cid = optimizer1._word_to_class[left_wid]
right_cid = optimizer1._word_to_class[right_wid]
cc_counts[left_cid,right_cid] += count
cw_counts[left_cid,right_wid] += count
wc_counts[left_wid,right_cid] += count
self.assertTrue(numpy.array_equal(class_counts, counts[0]))
self.assertTrue(numpy.array_equal(cc_counts, counts[1]))
self.assertTrue(numpy.array_equal(cw_counts, counts[2]))
self.assertTrue(numpy.array_equal(wc_counts, counts[3]))
optimizer1._class_counts = counts[0]
optimizer1._cc_counts = counts[1]
optimizer1._cw_counts = counts[2]
optimizer1._wc_counts = counts[3]
optimizer2 = NumpyBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer2.get_word_class(word_id)
optimizer2._move(word_id, new_class_id)
self.assertEqual(numpy.count_nonzero(optimizer1._class_counts != optimizer2._class_counts), 0)
self.assertEqual(numpy.count_nonzero(optimizer1._cc_counts != optimizer2._cc_counts), 0)
self.assertEqual(numpy.count_nonzero(optimizer1._cw_counts != optimizer2._cw_counts), 0)
self.assertEqual(numpy.count_nonzero(optimizer1._wc_counts != optimizer2._wc_counts), 0)
optimizer3 = TheanoBigramOptimizer(self.statistics, self.vocabulary)
orig_class_id = optimizer3.get_word_class(word_id)
optimizer3._move(word_id, new_class_id)
self.assert_optimizers_equal(optimizer2, optimizer3)
def test_evaluate(self):
numpy_optimizer = NumpyBigramOptimizer(self.statistics, self.vocabulary)
theano_optimizer = TheanoBigramOptimizer(self.statistics, self.vocabulary)
word_id = numpy_optimizer.get_word_id('d')
orig_class_id = numpy_optimizer.get_word_class(word_id)
new_class_id = 1 if orig_class_id != 1 else 0
orig_ll = numpy_optimizer.log_likelihood()
self.assertTrue(numpy.isclose(orig_ll, theano_optimizer.log_likelihood()))
ll_diff = numpy_optimizer._evaluate(word_id, new_class_id)
self.assertTrue(numpy.isclose(ll_diff, theano_optimizer._evaluate(word_id, new_class_id)))
numpy_optimizer._move(word_id, new_class_id)
new_ll = numpy_optimizer.log_likelihood()
self.assertFalse(numpy.isclose(orig_ll, new_ll))
self.assertTrue(numpy.isclose(orig_ll + ll_diff, new_ll))
theano_optimizer._move(word_id, new_class_id)
self.assertTrue(numpy.isclose(new_ll, theano_optimizer.log_likelihood()))
