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| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # Rhizome | |
| # Version beta 0.0, August 2023 | |
| # Property of IBM Research, Accelerated Discovery | |
| # | |
| """ | |
| PLEASE NOTE THIS IMPLEMENTATION INCLUDES THE ORIGINAL SOURCE CODE (AND SOME ADAPTATIONS) | |
| OF THE MHG IMPLEMENTATION OF HIROSHI KAJINO AT IBM TRL ALREADY PUBLICLY AVAILABLE. | |
| THIS MIGHT INFLUENCE THE DECISION OF THE FINAL LICENSE SO CAREFUL CHECK NEEDS BE DONE. | |
| """ | |
| """ Title """ | |
| __author__ = "Hiroshi Kajino <[email protected]>" | |
| __copyright__ = "(c) Copyright IBM Corp. 2018" | |
| __version__ = "0.1" | |
| __date__ = "Jun 4 2018" | |
| from ..hypergraph import Hypergraph | |
| from copy import deepcopy | |
| from typing import List | |
| import numpy as np | |
| def common_node_list(hg1: Hypergraph, hg2: Hypergraph) -> List[str]: | |
| """ return a list of common nodes | |
| Parameters | |
| ---------- | |
| hg1, hg2 : Hypergraph | |
| Returns | |
| ------- | |
| list of str | |
| list of common nodes | |
| """ | |
| if hg1 is None or hg2 is None: | |
| return [], False | |
| else: | |
| node_set = hg1.nodes.intersection(hg2.nodes) | |
| node_dict = {} | |
| if 'order4hrg' in hg1.node_attr(list(hg1.nodes)[0]): | |
| for each_node in node_set: | |
| node_dict[each_node] = hg1.node_attr(each_node)['order4hrg'] | |
| else: | |
| for each_node in node_set: | |
| node_dict[each_node] = hg1.node_attr(each_node)['symbol'].__hash__() | |
| node_list = [] | |
| for each_key, _ in sorted(node_dict.items(), key=lambda x:x[1]): | |
| node_list.append(each_key) | |
| edge_name = hg1.has_edge(node_list, ignore_order=True) | |
| if edge_name: | |
| if not hg1.edge_attr(edge_name).get('terminal', True): | |
| node_list = hg1.nodes_in_edge(edge_name) | |
| return node_list, True | |
| else: | |
| return node_list, False | |
| def _node_match(node1, node2): | |
| # if the nodes are hyperedges, `atom_attr` determines the match | |
| if node1['bipartite'] == 'edge' and node2['bipartite'] == 'edge': | |
| return node1["attr_dict"]['symbol'] == node2["attr_dict"]['symbol'] | |
| elif node1['bipartite'] == 'node' and node2['bipartite'] == 'node': | |
| # bond_symbol | |
| return node1['attr_dict']['symbol'] == node2['attr_dict']['symbol'] | |
| else: | |
| return False | |
| def _easy_node_match(node1, node2): | |
| # if the nodes are hyperedges, `atom_attr` determines the match | |
| if node1['bipartite'] == 'edge' and node2['bipartite'] == 'edge': | |
| return node1["attr_dict"].get('symbol', None) == node2["attr_dict"].get('symbol', None) | |
| elif node1['bipartite'] == 'node' and node2['bipartite'] == 'node': | |
| # bond_symbol | |
| return node1['attr_dict'].get('ext_id', -1) == node2['attr_dict'].get('ext_id', -1)\ | |
| and node1['attr_dict']['symbol'] == node2['attr_dict']['symbol'] | |
| else: | |
| return False | |
| def _node_match_prod_rule(node1, node2, ignore_order=False): | |
| # if the nodes are hyperedges, `atom_attr` determines the match | |
| if node1['bipartite'] == 'edge' and node2['bipartite'] == 'edge': | |
| return node1["attr_dict"]['symbol'] == node2["attr_dict"]['symbol'] | |
| elif node1['bipartite'] == 'node' and node2['bipartite'] == 'node': | |
| # ext_id, order4hrg, bond_symbol | |
| if ignore_order: | |
| return node1['attr_dict']['symbol'] == node2['attr_dict']['symbol'] | |
| else: | |
| return node1['attr_dict']['symbol'] == node2['attr_dict']['symbol']\ | |
| and node1['attr_dict'].get('ext_id', -1) == node2['attr_dict'].get('ext_id', -1) | |
| else: | |
| return False | |
| def _edge_match(edge1, edge2, ignore_order=False): | |
| #return True | |
| if ignore_order: | |
| return True | |
| else: | |
| return edge1["order"] == edge2["order"] | |
| def masked_softmax(logit, mask): | |
| ''' compute a probability distribution from logit | |
| Parameters | |
| ---------- | |
| logit : array-like, length D | |
| each element indicates how each dimension is likely to be chosen | |
| (the larger, the more likely) | |
| mask : array-like, length D | |
| each element is either 0 or 1. | |
| if 0, the dimension is ignored | |
| when computing the probability distribution. | |
| Returns | |
| ------- | |
| prob_dist : array, length D | |
| probability distribution computed from logit. | |
| if `mask[d] = 0`, `prob_dist[d] = 0`. | |
| ''' | |
| if logit.shape != mask.shape: | |
| raise ValueError('logit and mask must have the same shape') | |
| c = np.max(logit) | |
| exp_logit = np.exp(logit - c) * mask | |
| sum_exp_logit = exp_logit @ mask | |
| return exp_logit / sum_exp_logit | |