| |
| |
|
|
| from data.parser.from_mrp.abstract_parser import AbstractParser |
| import utility.parser_utils as utils |
|
|
|
|
| class SequentialParser(AbstractParser): |
| def __init__(self, args, part: str, fields, filter_pred=None, **kwargs): |
| assert part == "training" or part == "validation" |
| path = args.training_data if part == "training" else args.validation_data |
|
|
| self.data = utils.load_dataset(path) |
| utils.anchor_ids_from_intervals(self.data) |
|
|
| self.node_counter, self.edge_counter, self.no_edge_counter = 0, 0, 0 |
| anchor_count, source_anchor_count, target_anchor_count, n_node_token_pairs = 0, 0, 0, 0 |
|
|
| for sentence_id, sentence in list(self.data.items()): |
| for node in sentence["nodes"]: |
| if "label" not in node: |
| del self.data[sentence_id] |
| break |
|
|
| for node, _ in utils.node_generator(self.data): |
| node["target anchors"] = [] |
| node["source anchors"] = [] |
|
|
| for sentence in self.data.values(): |
| for e in sentence["edges"]: |
| source, target = e["source"], e["target"] |
|
|
| if sentence["nodes"][target]["label"] == "Target": |
| sentence["nodes"][source]["target anchors"] += sentence["nodes"][target]["anchors"] |
| elif sentence["nodes"][target]["label"] == "Source": |
| sentence["nodes"][source]["source anchors"] += sentence["nodes"][target]["anchors"] |
|
|
| for i, node in list(enumerate(sentence["nodes"]))[::-1]: |
| if "label" not in node or node["label"] in ["Source", "Target"]: |
| del sentence["nodes"][i] |
| sentence["edges"] = [] |
|
|
| for node, sentence in utils.node_generator(self.data): |
| self.node_counter += 1 |
|
|
| utils.create_bert_tokens(self.data, args.encoder) |
|
|
| |
| for sentence in self.data.values(): |
| N = len(sentence["nodes"]) |
|
|
| utils.create_edges(sentence) |
| self.no_edge_counter += N * (N - 1) |
|
|
| sentence["anchor edges"] = [N, len(sentence["input"]), []] |
| sentence["source anchor edges"] = [N, len(sentence["input"]), []] |
| sentence["target anchor edges"] = [N, len(sentence["input"]), []] |
|
|
| sentence["anchored labels"] = [len(sentence["input"]), []] |
| for i, node in enumerate(sentence["nodes"]): |
| anchored_labels = [] |
|
|
| for anchor in node["anchors"]: |
| sentence["anchor edges"][-1].append((i, anchor)) |
| anchored_labels.append((anchor, node["label"])) |
|
|
| for anchor in node["source anchors"]: |
| sentence["source anchor edges"][-1].append((i, anchor)) |
| for anchor in node["target anchors"]: |
| sentence["target anchor edges"][-1].append((i, anchor)) |
|
|
| sentence["anchored labels"][1].append(anchored_labels) |
|
|
| anchor_count += len(node["anchors"]) |
| source_anchor_count += len(node["source anchors"]) |
| target_anchor_count += len(node["target anchors"]) |
| n_node_token_pairs += len(sentence["input"]) |
|
|
| sentence["id"] = [sentence["id"]] |
|
|
| self.anchor_freq = anchor_count / n_node_token_pairs |
| self.source_anchor_freq = anchor_count / n_node_token_pairs |
| self.target_anchor_freq = anchor_count / n_node_token_pairs |
| self.input_count = sum(len(sentence["input"]) for sentence in self.data.values()) |
|
|
| super(SequentialParser, self).__init__(fields, self.data, filter_pred) |
|
|
| @staticmethod |
| def node_similarity_key(node): |
| return tuple([node["label"]] + node["anchors"]) |
|
|
