def test_eval(self):
entries = [Environment(
{"text_query": "ab test", "ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParserWithoutVariadicArgs())
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParserWithoutVariadicArgs())(
"y = x + 1"
)
transform = AddQueryForTreeGenDecoder(aencoder, 3,)
query = transform(
reference=[Token(None, "ab", "ab"), Token(None, "test", "test")],
action_sequence=action_sequence,
train=False
)
assert np.array_equal(
[
[-1, -1, -1], [2, -1, -1], [3, 2, -1], [4, 3, 2],
[3, 2, -1], [5, 3, 2], [5, 3, 2], [4, 5, 3],
[5, 3, 2], [6, 5, 3]
],
query.numpy()
)
def test_simple_case(self):
entries = [Environment(
{"ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParser())
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParser())(
ground_truth="y = x + 1"
)
transform = EncodeActionSequence(aencoder)
ground_truth = transform(
action_sequence=action_sequence,
reference=[Token(None, "foo", "foo"), Token(None, "bar", "bar")],
)
assert np.array_equal(
[
[3, -1, -1], [4, -1, -1], [-1, 1, -1], [1, -1, -1],
[5, -1, -1], [-1, 2, -1], [1, -1, -1], [4, -1, -1],
[-1, 3, -1], [1, -1, -1], [6, -1, -1], [-1, 4, -1],
[1, -1, -1]
],
ground_truth.numpy()
)
def test_eval(self):
entries = [Environment(
{"text_query": "ab test", "ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParserWithoutVariadicArgs())
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParserWithoutVariadicArgs())(
"y = x + 1"
)
transform = AddActionSequenceAsTree(aencoder,)
matrix, depth = transform(
reference=[Token(None, "ab", "ab"), Token(None, "test", "test")],
action_sequence=action_sequence,
train=False
)
assert np.array_equal(
[0, 1, 2, 3, 2, 3, 3, 4, 3, 4],
depth.numpy()
)
assert np.array_equal(
[[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
matrix.numpy()
)
def test_n_dependent(self):
entries = [Environment(
{"text_query": "ab test", "ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParserWithoutVariadicArgs())
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParserWithoutVariadicArgs())(
"y = x + 1"
)
transform = AddPreviousActionRules(aencoder, 2, n_dependent=3)
prev_rule_action = transform(
reference=[Token(None, "ab", "ab"), Token(None, "test", "test")],
action_sequence=action_sequence,
train=False,
)
assert np.array_equal(
[
# str -> "y"
[[-1, -1, -1], [-1, 3, -1], [-1, -1, -1]],
# Number -> number
[[8, -1, -1], [9, -1, -1], [-1, -1, -1]],
[[-1, -1, -1], [-1, 4, -1], [-1, -1, -1]],
],
prev_rule_action.numpy()
)
def test_eval(self):
entries = [Environment(
{"text_query": "foo bar", "ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParser())
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParser())(
"y = x + 1"
)
transform = AddActions(aencoder)
action_tensor = transform(
reference=[Token(None, "foo", "foo"), Token(None, "bar", "bar")],
action_sequence=action_sequence,
train=False
)
assert np.array_equal(
[
[2, 2, 0], [4, 3, 1], [6, 4, 2], [6, 4, 2], [5, 3, 1],
[6, 5, 5], [6, 5, 5], [5, 5, 5], [6, 4, 8], [6, 4, 8],
[5, 5, 5], [9, 6, 11], [9, 6, 11], [-1, -1, -1]
],
action_tensor.numpy()
)
def test_eval(self):
entries = [Environment(
{"ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParser())
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParser())(
"y = x + 1"
)
transform = AddPreviousActions(aencoder)
prev_action_tensor = transform(
reference=[Token(None, "foo", "foo"), Token(None, "bar", "bar")],
action_sequence=action_sequence,
train=False
)
assert np.array_equal(
[
[2, -1, -1], [3, -1, -1], [4, -1, -1], [-1, 1, -1],
[1, -1, -1], [5, -1, -1], [-1, 2, -1], [1, -1, -1],
[4, -1, -1], [-1, 3, -1], [1, -1, -1], [6, -1, -1],
[-1, 4, -1], [1, -1, -1]
],
prev_action_tensor.numpy()
)
def prepare_encoder(self, dataset, parser):
words = get_words(dataset, tokenize)
chars = get_characters(dataset, tokenize)
samples = get_samples(dataset, parser)
qencoder = LabelEncoder(words, 2)
cencoder = LabelEncoder(chars, 0)
aencoder = ActionSequenceEncoder(samples, 2)
return qencoder, cencoder, aencoder
def test_impossible_case(self):
entries = [Environment(
{"ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParser())
d.tokens = [("", "y"), ("", "1")]
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParser())(
ground_truth="y = x + 1"
)
transform = EncodeActionSequence(aencoder)
with pytest.raises(RuntimeError):
transform(
reference=[Token(None, "foo", "foo"), Token(None, "bar", "bar")],
action_sequence=action_sequence,
)
def test_eval(self):
entries = [Environment(
{"text_query": "ab test", "ground_truth": "y = x + 1"},
set(["ground_truth"])
)]
dataset = ListDataset(entries)
d = get_samples(dataset, MockParserWithoutVariadicArgs())
aencoder = ActionSequenceEncoder(d, 0)
action_sequence = GroundTruthToActionSequence(MockParserWithoutVariadicArgs())(
"y = x + 1"
)
transform = AddPreviousActionRules(aencoder, 2)
prev_rule_action = transform(
reference=[Token(None, "ab", "ab"), Token(None, "test", "test")],
action_sequence=action_sequence,
train=False
)
assert np.array_equal(
[
# None -> Root
[[1, -1, -1], [2, -1, -1], [-1, -1, -1]],
# Assign -> Name, expr
[[3, -1, -1], [4, -1, -1], [5, -1, -1]],
# Name -> str
[[4, -1, -1], [6, -1, -1], [-1, -1, -1]],
# str -> "x"
[[-1, -1, -1], [-1, 1, -1], [-1, -1, -1]],
# Op -> str, expr, expr
[[7, -1, -1], [6, -1, -1], [5, -1, -1]],
# str -> "+"
[[-1, -1, -1], [-1, 2, -1], [-1, -1, -1]],
# Name -> str
[[4, -1, -1], [6, -1, -1], [-1, -1, -1]],
# str -> "y"
[[-1, -1, -1], [-1, 3, -1], [-1, -1, -1]],
# Number -> number
[[8, -1, -1], [9, -1, -1], [-1, -1, -1]],
[[-1, -1, -1], [-1, 4, -1], [-1, -1, -1]],
],
prev_rule_action.numpy()
)
