def _unpack_model_batch_prediction(self,
batch,
coerce_tree=False) -> np.ndarray:
"""
Interpret prediction result per batch
coerce_tree = True if you want to ensure that the output forms a tree
"""
out_dict = self.model(**batch)
pred_matrix = out_dict["pred_matrix"]
batch_interpretation = []
for es in range(len(pred_matrix)):
essay_pred = tonp(pred_matrix[es])
# decoding using simple argmax
essay_pred = np.argmax(essay_pred, axis=-1)
dist_interpretation = []
for i in range(len(essay_pred)):
dist_interpretation.append(essay_pred[i] - i)
# check if the output is a tree
rep = TreeBuilder(dist_interpretation)
if (not rep.is_tree()) and (coerce_tree == True):
# run MINIMUM spanning tree
attn_matrix = tonp(pred_matrix[es])
attn_matrix = np.array(attn_matrix)
rank_order = get_rank_order(attn_matrix)
dist_interpretation = run_MST(
rank_order, rank_order, verdict="min"
) # --> use rank as the weight, "minimum" spanning tree, lower_rank number in rank is better
# add the decoding result to the batch result
batch_interpretation.append(dist_interpretation)
return batch_interpretation
def _unpack_gold_batch_prediction(self, batch: np.ndarray) -> List:
"""
Only use predictions without padding
Args:
batch (torch.Tensor): prediction in batch
Returns:
List
"""
output_linking = []
output_node_labelling = []
batch_rel_dists = tonp(batch["rel_dists"])
batch_component_labels = tonp(batch["component_labels"])
seq_len = batch["seq_len"]
for b in range(len(batch_rel_dists)):
rel_dists_gold = batch_rel_dists[b][:seq_len[b]].tolist()
rel_dists_gold = [self.dist_idx_to_dist(x) for x in rel_dists_gold]
component_labels_gold = batch_component_labels[
b][:seq_len[b]].tolist()
component_labels_gold = [
self.component_idx_to_label(x) for x in component_labels_gold
]
output_linking.append(rel_dists_gold)
output_node_labelling.append(component_labels_gold)
return output_linking, output_node_labelling
def predict(self, ds: Iterable[Instance], coerce_tree=False) -> np.ndarray:
"""
Generate prediction result
coerce_tree = True if we want to make sure that the prediction forms a tree
"""
pred_generator = self.iterator(ds, num_epochs=1, shuffle=False)
self.model.eval()
pred_generator_tqdm = tqdm(
pred_generator, total=self.iterator.get_num_batches(ds)
) # what if the the valid/test data contain label that does not exist in the training data --> workaround for the vocab
preds = []
golds = []
with torch.no_grad():
for batch in pred_generator_tqdm:
batch = nn_util.move_to_device(batch, self.cuda_device)
preds.extend(
self._unpack_model_batch_prediction(
batch, coerce_tree=coerce_tree))
golds.extend(
self._unpack_gold_batch_prediction(
tonp(batch["rel_dists"]), batch["seq_len"]))
return preds, golds
def _unpack_model_batch_prediction(self,
batch,
coerce_tree=False) -> np.ndarray:
"""
Interpret prediction result per batch
coerce_tree = True if we want to make sure that the predictions form a tree (using MST (min or max) algorithm)
"""
out_dict = self.model(**batch)
pred_linking_softmax = tonp(out_dict["pred_linking_softmax"])
pred_node_labelling_softmax = tonp(
out_dict["pred_node_labelling_softmax"])
linking_preds = []
node_labelling_preds = []
for es in range(len(pred_linking_softmax)):
essay_linking = []
essay_labelling = []
max_seq_len = batch["seq_len"][es]
# simple decoding using argmax
for s in range(
max_seq_len
): # iterate each sentence in the essay, s is the index of the current sentence
# perform constrained argmax for linking
curr_link_softmax = pred_linking_softmax[es][s]
ranked_pred = [
i for i in reversed(
sorted(enumerate(curr_link_softmax),
key=lambda x: x[1]))
]
for i in range(len(ranked_pred)):
tmp_dist = self.dist_idx_to_dist(ranked_pred[i][0])
if 0 <= tmp_dist + s <= max_seq_len - 1:
pred_dist = tmp_dist
break
# argmax for labelling
curr_label_softmax = pred_node_labelling_softmax[es][s]
pred_idx = np.argmax(curr_label_softmax)
pred_label = self.component_idx_to_label(pred_idx)
# essay-level result
essay_linking.append(pred_dist)
essay_labelling.append(pred_label)
# check if the output is tree
rep = TreeBuilder(essay_linking)
if (not rep.is_tree()) and (coerce_tree == True):
attn_matrix = [
] # element [i,j] denotes the probability of sentence i connects to sentence j (j as the target)
for s in range(
max_seq_len
): # iterate each sentence in the essay, s is the index of the current sentence
curr_pred = pred_linking_softmax[es][s]
# get the prediction to each possible target sentence in the text
row_pred = [0] * max_seq_len
for i in range(len(curr_pred)):
temp_dist = self.dist_idx_to_dist(i)
value = curr_pred[i]
if 0 <= temp_dist + s <= max_seq_len - 1:
row_pred[temp_dist + s] = value
attn_matrix.append(row_pred)
# run MAXIMUM spanning tree
attn_matrix = np.array(attn_matrix)
rank_order = get_rank_order(attn_matrix)
essay_linking = run_MST(
rank_order, attn_matrix, verdict="max"
) # --> use the softmax probability as the weight, we run the maximum spanning tree here because higher probability means better
# batch-level result
linking_preds.append(essay_linking)
node_labelling_preds.append(essay_labelling)
return linking_preds, node_labelling_preds
def _unpack_model_batch_prediction(self,
batch,
coerce_tree=False) -> np.ndarray:
"""
Interpret prediction result per batch
"""
out_dict = self.model(**batch)
pred_softmax = tonp(out_dict["pred_softmax"])
# print("seq len", batch["seq_len"])
# print(pred_softmax.shape)
batch_interpretation = []
for es in range(len(pred_softmax)):
essay_interpretation = []
max_seq_len = batch["seq_len"][es]
# simple decoding using argmax
for s in range(
max_seq_len
): # iterate each sentence in the essay, s is the index of the current sentence
curr_pred = pred_softmax[es][s]
# perform constrained argmax
ranked_pred = [
i for i in reversed(
sorted(enumerate(curr_pred), key=lambda x: x[1]))
]
# print(ranked_pred)
for i in range(len(ranked_pred)):
tmp_dist = self.dist_idx_to_dist(ranked_pred[i][0])
# print(tmp_dist, tmp_dist+s)
# input()
if 0 <= tmp_dist + s <= max_seq_len - 1:
pred_dist = tmp_dist
break
essay_interpretation.append(pred_dist)
# check if the output is tree
rep = TreeBuilder(essay_interpretation)
if (not rep.is_tree()) and (coerce_tree == True):
attn_matrix = [
] # element [i,j] denotes the probability of sentence i connects to sentence j (j as the target)
for s in range(
max_seq_len
): # iterate each sentence in the essay, s is the index of the current sentence
curr_pred = pred_softmax[es][s]
# get the prediction to each possible target sentence in the text
row_pred = [0] * max_seq_len
for i in range(len(curr_pred)):
temp_dist = self.dist_idx_to_dist(i)
value = curr_pred[i]
if 0 <= temp_dist + s <= max_seq_len - 1:
row_pred[temp_dist + s] = value
attn_matrix.append(row_pred)
# run MAXIMUM spanning tree
attn_matrix = np.array(attn_matrix)
rank_order = get_rank_order(attn_matrix)
essay_interpretation = run_MST(
rank_order, attn_matrix, verdict="max"
) # --> use the softmax probability as the weight, we run the maximum spanning tree here because higher probability means better
batch_interpretation.append(essay_interpretation)
return batch_interpretation