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| #!/usr/bin/env python3 | |
| # coding=utf-8 | |
| 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) | |
| # create edge vectors | |
| 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) | |
| def node_similarity_key(node): | |
| return tuple([node["label"]] + node["anchors"]) | |