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fix RESpanPairClassificationTaskModule #101

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fix RESpanPairClassificationTaskModule #101

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e491c3b
fix end indices and using partition annotations
ArneBinder Jun 24, 2024
a4562a3
adjust model tests
ArneBinder Jun 24, 2024
94116b6
do not adjust candidate_relations in encode_target
ArneBinder Jun 24, 2024
8a25011
simplify and fix decoding
ArneBinder Jun 24, 2024
e72d74e
fix: simplify and fix decoding
ArneBinder Jun 24, 2024
52a5d70
add documentation
ArneBinder Jun 25, 2024
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48 changes: 34 additions & 14 deletions src/pie_modules/taskmodules/re_span_pair_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,13 +192,25 @@ def construct_argument_marker(pos: str, label: Optional[str] = None, role: str =

def inject_markers_into_text(
text: str, positions_and_markers: List[Tuple[int, str]]
) -> Tuple[str, Dict[int, int]]:
) -> Tuple[str, Dict[int, Tuple[int, List[str]]]]:
"""Inject markers into the text at the given positions.

Args:
text: The text to inject the markers into.
positions_and_markers: A list of tuples where each tuple contains the position in the text
where the marker should be injected and the marker text itself.

Returns:
A tuple containing the text with the markers injected and a dictionary mapping the original
positions to the new positions and the markers that were injected at that position.
"""
offset = 0
original2new_pos = dict()
original2new_pos: Dict[int, Tuple[int, List[str]]] = dict()
for original_pos, marker in sorted(positions_and_markers):
text = text[: original_pos + offset] + marker + text[original_pos + offset :]
previous_markers = original2new_pos.get(original_pos, (-1, []))[1]
original2new_pos[original_pos] = (original_pos + offset, previous_markers + [marker])
offset += len(marker)
original2new_pos[original_pos] = original_pos + offset
return text, original2new_pos


Expand Down Expand Up @@ -505,21 +517,23 @@ def inject_markers_for_labeled_spans(

if isinstance(document, TextDocumentWithLabeledPartitions):
# create "dummy" markers for the partitions so that entries for these positions are created
# in original2new_pos
# in original_pos2new_pos_and_markers
for labeled_partition in document.labeled_partitions:
positions_and_markers.append((labeled_partition.start, ""))
positions_and_markers.append((labeled_partition.end, ""))

# inject markers into the text
marked_text, original2new_pos = inject_markers_into_text(
marked_text, original_pos2new_pos_and_markers = inject_markers_into_text(
document.text, positions_and_markers
)

# construct new spans
old2new_spans = dict()
for labeled_span in document.labeled_spans:
start = original2new_pos[labeled_span.start]
end = original2new_pos[labeled_span.end]
start_before_markers, markers = original_pos2new_pos_and_markers[labeled_span.start]
# we use just the span *without* the markers as new span
start = start_before_markers + sum(len(marker) for marker in markers)
end = original_pos2new_pos_and_markers[labeled_span.end][0]
new_span = LabeledSpan(start=start, end=end, label=labeled_span.label)
old2new_spans[labeled_span] = new_span

Expand All @@ -546,9 +560,13 @@ def inject_markers_for_labeled_spans(
new_document.binary_relations.extend(old2new_relations.values())
if isinstance(document, TextDocumentWithLabeledPartitions):
for labeled_partition in document.labeled_partitions:
new_start = original2new_pos[labeled_partition.start]
new_end = original2new_pos[labeled_partition.end]
new_labeled_partitions = labeled_partition.copy(start=new_start, end=new_end)
# we use the span *including* the markers as new span
start, _ = original_pos2new_pos_and_markers[labeled_partition.start]
end_before_markers, markers = original_pos2new_pos_and_markers[
labeled_partition.end
]
end = end_before_markers + sum(len(marker) for marker in markers)
new_labeled_partitions = labeled_partition.copy(start=start, end=end)
new_document.labeled_partitions.append(new_labeled_partitions)

new2old_spans = {new_span: old_span for old_span, new_span in old2new_spans.items()}
Expand Down Expand Up @@ -657,7 +675,7 @@ def encode_target(
get_relation_argument_spans_and_roles(relation)
].append(relation)
label_indices = [] # list of label indices
candidate_relations = []
# candidate_relations = []
for candidate_relation in task_encoding.metadata["candidate_relations"]:
candidate_roles_and_args = get_relation_argument_spans_and_roles(candidate_relation)
gold_relations = gold_roles_and_args2relation.get(candidate_roles_and_args, [])
Expand All @@ -678,9 +696,9 @@ def encode_target(
label_idx = PAD_VALUES["labels"]

label_indices.append(label_idx)
candidate_relations.append(candidate_relation)
# candidate_relations.append(candidate_relation)

task_encoding.metadata["candidate_relations"] = candidate_relations
# task_encoding.metadata["candidate_relations"] = candidate_relations
target: TargetEncodingType = {"labels": to_tensor("labels", label_indices)}

self._maybe_log_example(task_encoding=task_encoding, target=target)
Expand Down Expand Up @@ -711,7 +729,9 @@ def _maybe_log_example(
):
logger.info(f"relation {i}: {label}")
for j, arg_idx in enumerate(tuple_indices):
arg_tokens = tokens[span_start_indices[arg_idx] : span_end_indices[arg_idx]]
arg_tokens = tokens[
span_start_indices[arg_idx] : span_end_indices[arg_idx] + 1
]
logger.info(f"\targ {j}: {' '.join([str(x) for x in arg_tokens])}")

self._logged_examples_counter += 1
Expand Down
81 changes: 38 additions & 43 deletions tests/models/test_span_tuple_classification.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,7 @@ def batch():
]
),
"span_start_indices": tensor([[1, 9, 0, 0], [4, 12, 18, 0], [4, 12, 17, 21]]),
"span_end_indices": tensor([[7, 12, 0, 0], [10, 15, 21, 0], [10, 15, 20, 24]]),
"span_end_indices": tensor([[6, 11, 0, 0], [9, 14, 20, 0], [9, 14, 19, 23]]),
"tuple_indices": tensor(
[[[0, 1], [-1, -1], [-1, -1]], [[0, 1], [0, 2], [2, 1]], [[0, 1], [2, 3], [3, 2]]]
),
Expand Down Expand Up @@ -351,46 +351,41 @@ def test_forward_logits(batch, model):
tensor(
[
[
-0.23075447976589203,
0.08129829168319702,
-0.26441076397895813,
0.3208361268043518,
-0.3551301658153534,
0.09493370354175568,
-0.15801358222961426,
0.5679908990859985,
],
[
-0.2247302085161209,
0.21453489363193512,
-0.20609508454799652,
0.2984844148159027,
-0.266460657119751,
0.16119083762168884,
-0.10706772655248642,
0.5230874419212341,
],
[
-0.0552724152803421,
0.18319237232208252,
-0.14115819334983826,
0.23137536644935608,
-0.11953088641166687,
0.1623934805393219,
-0.04825110733509064,
0.43645235896110535,
],
[-0.2047966569662094, 0.17388933897018433, -0.06319254636764526, 0.4306640625],
[
-0.2897184491157532,
0.17462071776390076,
-0.12004873156547546,
0.1817789375782013,
-0.3208402395248413,
0.09282125532627106,
-0.05495951324701309,
0.4880615472793579,
],
[
-0.3101494312286377,
0.18245069682598114,
-0.13525372743606567,
0.28625163435935974,
-0.4020463228225708,
0.2283128798007965,
0.013205204159021378,
0.3972089886665344,
],
[
-0.33728304505348206,
0.22038179636001587,
-0.0482308566570282,
0.25237396359443665,
],
[
-0.3835912048816681,
0.20549766719341278,
0.15333643555641174,
0.23370930552482605,
-0.2575981616973877,
0.0700659453868866,
-0.010283984243869781,
0.4580671489238739,
],
]
),
Expand All @@ -402,7 +397,7 @@ def test_step(batch, model, config):
loss = model._step("train", batch)
assert loss is not None
if config == {}:
torch.testing.assert_close(loss, torch.tensor(1.3911350965499878))
torch.testing.assert_close(loss, torch.tensor(1.3872407674789429))
else:
raise ValueError(f"Unknown config: {config}")

Expand All @@ -413,7 +408,7 @@ def test_training_step_and_on_epoch_end(batch, model, config):
loss = model.training_step(batch, batch_idx=0)
assert loss is not None
if config == {}:
torch.testing.assert_close(loss, torch.tensor(1.3911350965499878))
torch.testing.assert_close(loss, torch.tensor(1.3872407674789429))
else:
raise ValueError(f"Unknown config: {config}")

Expand All @@ -427,7 +422,7 @@ def test_validation_step_and_on_epoch_end(batch, model, config):
assert loss is not None
metric_values = {k: v.item() for k, v in metric.compute().items()}
if config == {}:
torch.testing.assert_close(loss, torch.tensor(1.3911350965499878))
torch.testing.assert_close(loss, torch.tensor(1.3872407674789429))
assert metric_values == {
"macro/f1": 0.14814814925193787,
"micro/f1": 0.2857142984867096,
Expand All @@ -449,7 +444,7 @@ def test_test_step_and_on_epoch_end(batch, model, config):
assert loss is not None
metric_values = {k: v.item() for k, v in metric.compute().items()}
if config == {}:
torch.testing.assert_close(loss, torch.tensor(1.3911350965499878))
torch.testing.assert_close(loss, torch.tensor(1.3872407674789429))
assert metric_values == {
"macro/f1": 0.14814814925193787,
"micro/f1": 0.2857142984867096,
Expand Down Expand Up @@ -483,21 +478,21 @@ def test_predict_and_predict_step(model, batch, config, test_step):
tensor(
[
[
[0.1973, 0.2695, 0.1907, 0.3425],
[0.1586, 0.2488, 0.1932, 0.3993],
[-1.0000, -1.0000, -1.0000, -1.0000],
[-1.0000, -1.0000, -1.0000, -1.0000],
],
[
[0.1902, 0.2951, 0.1938, 0.3209],
[0.2213, 0.2809, 0.2031, 0.2947],
[0.1859, 0.2958, 0.2203, 0.2979],
[0.1692, 0.2596, 0.1985, 0.3727],
[0.1944, 0.2578, 0.2088, 0.3390],
[0.1818, 0.2655, 0.2095, 0.3432],
],
[
[0.1772, 0.2900, 0.2111, 0.3217],
[0.1699, 0.2968, 0.2269, 0.3064],
[0.1571, 0.2831, 0.2687, 0.2912],
[0.1650, 0.2495, 0.2152, 0.3704],
[0.1511, 0.2839, 0.2289, 0.3361],
[0.1750, 0.2429, 0.2241, 0.3580],
],
],
]
),
)
else:
Expand Down
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