class EpochTrackingBucketIteratorTest(IteratorTest):
def setUp(self):
# The super class creates a self.instances field and populates it with some instances with
# TextFields.
super(EpochTrackingBucketIteratorTest, self).setUp()
self.iterator = EpochTrackingBucketIterator(
sorting_keys=[["text", "num_tokens"]])
self.iterator.index_with(self.vocab)
# We'll add more to create a second dataset.
self.more_instances = [
self.create_instance(["this", "is", "a", "sentence"]),
self.create_instance(
["this", "is", "in", "the", "second", "dataset"]),
self.create_instance(["so", "is", "this", "one"])
]
def test_iterator_tracks_epochs_per_dataset(self):
generated_dataset1 = list(self.iterator(self.instances, num_epochs=2))
generated_dataset2 = list(
self.iterator(self.more_instances, num_epochs=2))
# First dataset has five sentences. See ``IteratorTest.setUp``
assert generated_dataset1[0]["epoch_num"] == [0, 0, 0, 0, 0]
assert generated_dataset1[1]["epoch_num"] == [1, 1, 1, 1, 1]
# Second dataset has three sentences.
assert generated_dataset2[0]["epoch_num"] == [0, 0, 0]
assert generated_dataset2[1]["epoch_num"] == [1, 1, 1]
def setUp(self):
# The super class creates a self.instances field and populates it with some instances with
# TextFields.
super(EpochTrackingBucketIteratorTest, self).setUp()
self.iterator = EpochTrackingBucketIterator(
sorting_keys=[["text", "num_tokens"]])
self.iterator.index_with(self.vocab)
# We'll add more to create a second dataset.
self.more_instances = [
self.create_instance(["this", "is", "a", "sentence"]),
self.create_instance(
["this", "is", "in", "the", "second", "dataset"]),
self.create_instance(["so", "is", "this", "one"])
]
def test_forward_with_epoch_num_changes_cost_weight(self):
# Redefining model. We do not want this to change the state of ``self.model``.
params = Params.from_file(self.param_file)
model = Model.from_params(vocab=self.vocab, params=params[u'model'])
# Initial cost weight, before forward is called.
assert model._checklist_cost_weight == 0.8
iterator = EpochTrackingBucketIterator(sorting_keys=[[u'sentence', u'num_tokens']])
cost_weights = []
for epoch_data in iterator(self.dataset, num_epochs=4):
model.forward(**epoch_data)
cost_weights.append(model._checklist_cost_weight)
# The config file has ``wait_num_epochs`` set to 0, so the model starts decreasing the cost
# weight at epoch 0 itself.
assert_almost_equal(cost_weights, [0.72, 0.648, 0.5832, 0.52488])