Case Study of Generated Logic Rules #7

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1. immortalCO commented on Aug 9, 2021

   

   immortalCO

   on Aug 9, 2021

   Collaborator

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   The relation names of FB15k-237 are very long. So we have renamed them in the paper by understanding their meaning.

   The following code can be used to print rules from trained models, with various constraints:

   import gc
   import copy
   gc.enable()
   
   import os
   import sys
   from sys import *
   #from random import *
   from collections import defaultdict
   import torch
   import torch.nn as nn
   from torch import *
   from torch.nn import *
   from torch.optim import *
   from random import shuffle
   from random import randint
   import time
   import datetime
   import json
   import torch.nn.functional as F
   from model import *
   
   relation2id = dict()
   id2relation = dict()
   
   with open(f'dataset/FB15k-237/relations.dict') as fin:
   	relation2id = dict()
   	for line in fin:
   		rid, relation = line.strip().split('\t')
   		relation2id[relation] = int(rid)
   
   		rel = ""
   		cnt = 0
   		for c in reversed(relation):
   			if c == '/':
   				cnt += 1
   				if cnt == 2 and len(rel) >= 30:
   					break
   				if cnt >= 3:
   					break
   			rel = c + rel
   
   		id2relation[int(rid)] = rel
   
   R = len(relation2id)
   
   mov = R
   inv = [0] * 2 * R
   
   for i in range(R):
   	inv[i + mov] = i
   	inv[i] = i + mov
   	id2relation[i + mov] = "!" + id2relation[i]
   
   a = torch.load(sys.argv[1])
   r = int(a['r'])
   
   def has_revlink(rule):
   	for i in range(len(rule) - 1):
   		if rule[i] == inv[rule[i + 1]]:
   			return True
   	return False
   
   
   def contains_r(rule):
   	r = int(a['r'])
   	ret = r in set(map(int, rule))
   	return ret
   
   def prt(p, n=10):
   	for _i in range(n):
   		i = p[_i]
   		r = id2relation[int(a['r'])]
   		path = map(lambda x : id2relation[int(x)], a['rules'][i])
   		val = a['predictor']['rule_weight_raw'][i]
   
   
   		print(f"&$\\gets$&$",end='')
   		Ltr = "XUVW"
   		for i, r in enumerate(path):
   			r = r.replace('_', '\\_')
   			if r[0] != '!':
   				print(f"{Ltr[i]}\\relarr{{{r}}}",end="")
   			else:
   				print(f"{Ltr[i]}\\relarrl{{{r[1:]}}}",end="")
   
   		print("Y$\\\\")
   
   
   
   weight = a['predictor']['rule_weight_raw']
   
   
   print("Relation:", id2relation[int(a['r'])])
   
   print("general:")
   p = sorted(range(len(a['rules'])), 
   	key=lambda i : (weight[i]),
   	reverse=True)
   prt(p)
   
   print("no self:")
   p = sorted(range(len(a['rules'])), 
   	key=lambda i : (not contains_r(a['rules'][i]), not has_revlink(a['rules'][i]), weight[i]),
   	reverse=True)
   prt(p,n=40)
   
   print("revlink:")
   p = sorted(range(len(a['rules'])), 
   	key=lambda i : (has_revlink(a['rules'][i]), weight[i]),
   	reverse=True)
   prt(p,n=40)

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2. LuMflowers commented on Aug 12, 2021

   

   LuMflowers

   on Aug 12, 2021

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   When I run the above code, I encounter an error " a = torch.load(sys.argv[1]) IndexError: list index out of range". How to solve this problem?

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3. navdeepkjohal commented on May 8, 2022

   

   navdeepkjohal

   on May 8, 2022

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   Hello,

   sys.argv[1] is the path to the model file, that is learnt during the train time. set it to ''./workspace/model_0.pth' to see the rules for relation 0 (r=0) and so on for the other relations. Also, the rules generated by this code are in the form of pdf code. Give the output of this file as the input to latex file. I hope this helps.