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tester.py
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import torch
from sematch.semantic.graph import DBpediaDataTransform, Taxonomy
from sematch.semantic.similarity import ConceptSimilarity
concept = ConceptSimilarity(Taxonomy(DBpediaDataTransform()), 'models/dbpedia_type_ic.txt')
from functools import reduce
import numpy as np
import time
from tqdm import tqdm
from utils import *
from models import *
from sklearn.utils import shuffle
import pickle
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class Tester:
def __init__(self, dataset, args, model_path, valid_or_test):
self.args = args
self.hierarchy = args.hierarchy
self.name = args.dataset
self.setting = args.setting
self.device = args.device
self.model_name = args.model
if self.model_name == 'TransE':
self.model = TransE(dataset.num_ent(), dataset.num_rel(), args.dim, self.device)
if self.model_name == 'TransH':
self.model = TransH(dataset.num_ent(), dataset.num_rel(), args.dim, self.device)
if self.model_name == 'TransR':
self.model = TransR(dataset.num_ent(), dataset.num_rel(), args.dim, self.device)
if self.model_name == 'TransD':
self.model = TransD(dataset.num_ent(), dataset.num_rel(), args.dim, self.device)
if self.model_name == 'DistMult':
self.model = DistMult(dataset.num_ent(), dataset.num_rel(), args.dim, self.device)
if self.model_name == 'ComplEx':
self.model = ComplEx(dataset.num_ent(), dataset.num_rel(), args.dim, self.device)
if self.model_name == 'SimplE':
self.model = SimplE(dataset.num_ent(), dataset.num_rel(), args.dim, self.device, dataset, args)
if self.model_name == 'SimplE2':
self.model = SimplE2(dataset.num_ent(), dataset.num_rel(), args.dim, self.device)
if self.model_name == 'ConvE':
self.model = ConvE(dataset.num_ent(), dataset.num_rel(), args, self.device)
if self.model_name == 'ConvKB2D':
self.model = ConvKB2D(dataset.num_ent(), dataset.num_rel(), args, self.device)
if self.model_name == 'RGCN':
self.model = RGCN(dataset.num_ent(), dataset.num_rel(), args, self.device)
if self.model_name == 'CompGCN':
self.model = CompGCN_DistMult(dataset.num_ent(), dataset.num_rel(), args, self.device)
self.model.load_state_dict(torch.load(model_path))
self.model.eval()
self.dataset = dataset
self.valid_or_test = valid_or_test
self.batch_size = args.batch_size
self.neg_ratio = args.neg_ratio
self.neg_sampler = args.neg_sampler
self.metric = args.metrics
self.sem = args.sem
if self.model_name != 'ConvE':
with open('datasets/' + self.dataset.name + "/tail2triples.pkl", 'rb') as f:
self.all_possible_ts = pickle.load(f)
with open('datasets/' + self.dataset.name + "/head2triples.pkl", 'rb') as f:
self.all_possible_hs = pickle.load(f)
else:
with open('datasets/' + self.dataset.name + "/tail2triples_inv.pkl", 'rb') as f:
self.all_possible_ts = pickle.load(f)
def get_observed_h(self, h, r, t):
return(list(set(self.all_possible_hs[t.item()][r.item()]) - set([h.item()])))
def get_observed_t(self, h, r, t):
try:
return(list(set(self.all_possible_ts[h.item()][r.item()]) - set([t.item()])))
except KeyError:
return None
def predictions(self, h, r, t, all_entities):
heads = h.reshape(-1, 1).repeat(1, all_entities.size()[1]).to(self.device)
rels = r.reshape(-1, 1).repeat(1, all_entities.size()[1]).to(self.device)
tails = t.reshape(-1, 1).repeat(1, all_entities.size()[1]).to(self.device)
triplets = torch.stack((heads, rels, all_entities), dim=2).reshape(-1, 3).to(self.device)
tails_predictions = self.model.forward((triplets[:,0]),(triplets[:,1]),(triplets[:,2])).reshape(1, -1)
triplets = torch.stack((all_entities, rels, tails), dim=2).reshape(-1, 3).to(self.device)
heads_predictions = self.model.forward((triplets[:,0]),(triplets[:,1]),(triplets[:,2])).reshape(1, -1)
return heads_predictions.squeeze(), tails_predictions.squeeze()
def calc_valid_mrr(self):
schema_OWA = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
sem_t_triples_OWA, sem_h_triples_OWA = 0, 0 # for keeping track of the # of triples for which Sem@K_h/t can be calculated
schema_CWA = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
schema_CWA_wp = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
sem_t_triples_CWA, sem_h_triples_CWA= 0, 0 # for keeping track of the # of triples for which Sem@K_h/t can be calculated
ext_CWA = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
sem_t_triples_ext, sem_h_triples_ext= 0, 0 # for keeping track of the # of triples for which Sem@K_h/t can be calculated
filt_hit1_h, filt_hit1_t, filt_hit3_h, filt_hit3_t, filt_hit5_h, filt_hit5_t, filt_hit10_h, filt_hit10_t = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
cpt_h_na, cpt_t_na, cpt_h_good, cpt_t_good, h1s_h, h1s_t = 0,0,0,0,0,0
h1_h, h1_t, h1_sem1_h, h1_sem1_t = 0, 0, 0, 0
filt_mrr_h, filt_mrr_t = [], []
zero_tensor = torch.tensor([0], device=self.device)
one_tensor = torch.tensor([1], device=self.device)
X_valid = torch.from_numpy((self.dataset.data[self.valid_or_test]))
num_ent = self.dataset.num_ent()
all_entities = torch.arange(end=num_ent, device=self.device).unsqueeze(0)
start = time.time()
if self.model_name =='ConvE':
half_idx = int(X_valid.shape[0]/2)
X_valid_tails = X_valid[:half_idx]
X_valid_tails_inv = X_valid[half_idx:]
for triple in tqdm(X_valid_tails):
h,r,t = triple[0], triple[1], triple[2]
rm_idx_t = self.get_observed_t(h,r,t)
tails_predictions = self.model.get_score(h.to(self.device),r.to(self.device),t.to(self.device)).squeeze()
tails_predictions[[rm_idx_t]] = - np.inf
indices_tail = tails_predictions.argsort(descending=True)
filt_rank_t = (indices_tail == t).nonzero(as_tuple=True)[0].item() + 1
filt_mrr_t.append(1.0/filt_rank_t)
if self.metric == 'sem' or self.metric == 'all':
if self.sem == 'schema' or self.sem == 'both':
if r.item() in self.dataset.r2id2range2id.keys() and self.dataset.r2id2range2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2range2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
sem_t_triples_CWA+=1
try:
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
except:
continue
elif self.setting=='OWA':
try:
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
except:
continue
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA_wp['sem1_t'], schema_CWA_wp['sem3_t'], schema_CWA_wp['sem5_t'], schema_CWA_wp['sem10_t'] = schema_CWA_wp['sem1_t']+s1, \
schema_CWA_wp['sem3_t']+s3, schema_CWA_wp['sem5_t']+s5, schema_CWA_wp['sem10_t']+s10
sem_t_triples_CWA+=1
if self.sem == 'extensional' or self.sem == 'both':
if r.item() in self.dataset.r2ts.keys():
if len(self.dataset.r2ts[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_tail[:10], r.item(), side='tail', k=10)
ext_CWA['sem1_t'], ext_CWA['sem3_t'], ext_CWA['sem5_t'], ext_CWA['sem10_t'] = ext_CWA['sem1_t']+s1, \
ext_CWA['sem3_t']+s3, ext_CWA['sem5_t']+s5, ext_CWA['sem10_t']+s10
sem_t_triples_ext+=1
if self.metric == 'ranks' or self.metric == 'all':
filt_hit10_t += torch.where(indices_tail[:10] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_t += torch.where(indices_tail[:5] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_t += torch.where(indices_tail[:3] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_t += torch.where(indices_tail[:1] == t.item(), one_tensor, zero_tensor).sum().item()
# batch of reversed triples
for triple in tqdm(X_valid_tails_inv):
h,r,t = triple[0], triple[1], triple[2]
rm_idx_t = self.get_observed_t(h,r,t)
tails_predictions = self.model.get_score(h.to(self.device),r.to(self.device),t.to(self.device)).squeeze()
tails_predictions[[rm_idx_t]] = - np.inf
indices_tail = tails_predictions.argsort(descending=True)
filt_rank_h = (indices_tail == t).nonzero(as_tuple=True)[0].item() + 1
filt_mrr_h.append(1.0/filt_rank_h)
if self.metric == 'sem' or self.metric == 'all':
if self.sem == 'schema' or self.sem == 'both':
if r.item() in self.dataset.r2id2range2id.keys() and self.dataset.r2id2range2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2range2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
sem_h_triples_CWA+=1
try:
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
except:
continue
elif self.setting=='OWA':
try:
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
except:
continue
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA_wp['sem1_h'], schema_CWA_wp['sem3_h'], schema_CWA_wp['sem5_h'], schema_CWA_wp['sem10_h'] = schema_CWA_wp['sem1_h']+s1, \
schema_CWA_wp['sem3_h']+s3, schema_CWA_wp['sem5_h']+s5, schema_CWA_wp['sem10_h']+s10
sem_h_triples_CWA+=1
if self.sem == 'extensional' or self.sem == 'both':
if r.item() in self.dataset.r2ts.keys():
if len(self.dataset.r2ts[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_tail[:10], r.item(), side='tail', k=10)
ext_CWA['sem1_h'], ext_CWA['sem3_h'], ext_CWA['sem5_h'], ext_CWA['sem10_h'] = ext_CWA['sem1_h']+s1, \
ext_CWA['sem3_h']+s3, ext_CWA['sem5_h']+s5, ext_CWA['sem10_h']+s10
sem_h_triples_ext+=1
if self.metric == 'ranks' or self.metric == 'all':
filt_hit10_h += torch.where(indices_tail[:10] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_h += torch.where(indices_tail[:5] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_h += torch.where(indices_tail[:3] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_h += torch.where(indices_tail[:1] == t.item(), one_tensor, zero_tensor).sum().item()
else:
X_valid = shuffle(X_valid, random_state=7)
if X_valid.shape[0] > 10000 :
X_valid = X_valid[:5000]
for triple in tqdm(X_valid):
h,r,t = triple[0], triple[1], triple[2]
rm_idx_t = self.get_observed_t(h,r,t)
rm_idx_h = self.get_observed_h(h,r,t)
heads_predictions, tails_predictions = self.predictions(h,r,t,all_entities)
# Filtered Scenario
heads_predictions[[rm_idx_h]], tails_predictions[[rm_idx_t]] = -np.inf, -np.inf
indices_tail, indices_head = tails_predictions.argsort(descending=True), heads_predictions.argsort(descending=True)
filt_rank_h = (indices_head == h).nonzero(as_tuple=True)[0].item() + 1
filt_rank_t = (indices_tail == t).nonzero(as_tuple=True)[0].item() + 1
if indices_head[0].item() == h.item() :
h1_h +=1
if indices_tail[0].item() == t.item() :
h1_t +=1
# Filtered MR and MRR
filt_mrr_h.append(1.0/filt_rank_h)
filt_mrr_t.append(1.0/filt_rank_t)
if self.metric == 'sem' or self.metric == 'all':
if self.sem == 'schema' or self.sem == 'both':
if r.item() in self.dataset.r2id2dom2id.keys() and self.dataset.r2id2dom2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2dom2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_head[:100], r.item(), side='head', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
sem_h_triples_CWA+=1
s1, s3, s5, s10 = self.sem_at_k(indices_head[:1000], r.item(), side='head', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
elif self.setting=='OWA':
try:
s1, s3, s5, s10 = self.sem_at_k(indices_head[:100], r.item(), side='head', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
except:
continue
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_head[:100], r.item(), side='head', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_head[:100], r.item(), side='head', k=10, setting='CWA')
schema_CWA_wp['sem1_h'], schema_CWA_wp['sem3_h'], schema_CWA_wp['sem5_h'], schema_CWA_wp['sem10_h'] = schema_CWA_wp['sem1_h']+s1, \
schema_CWA_wp['sem3_h']+s3, schema_CWA_wp['sem5_h']+s5, schema_CWA_wp['sem10_h']+s10
sem_h_triples_CWA+=1
if r.item() in self.dataset.r2id2range2id.keys() and self.dataset.r2id2range2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2range2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
sem_t_triples_CWA+=1
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
elif self.setting=='OWA':
try:
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
except:
continue
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA_wp['sem1_t'], schema_CWA_wp['sem3_t'], schema_CWA_wp['sem5_t'], schema_CWA_wp['sem10_t'] = schema_CWA_wp['sem1_t']+s1, \
schema_CWA_wp['sem3_t']+s3, schema_CWA_wp['sem5_t']+s5, schema_CWA_wp['sem10_t']+s10
sem_t_triples_CWA+=1
if self.sem == 'extensional' or self.sem == 'both':
if r.item() in self.dataset.r2hs.keys():
if len(self.dataset.r2hs[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_head[:10], r.item(), side='head', k=10)
ext_CWA['sem1_h'], ext_CWA['sem3_h'], ext_CWA['sem5_h'], ext_CWA['sem10_h'] = ext_CWA['sem1_h']+s1, \
ext_CWA['sem3_h']+s3, ext_CWA['sem5_h']+s5, ext_CWA['sem10_h']+s10
sem_h_triples_ext+=1
if r.item() in self.dataset.r2ts.keys():
if len(self.dataset.r2ts[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_tail[:10], r.item(), side='tail', k=10)
ext_CWA['sem1_t'], ext_CWA['sem3_t'], ext_CWA['sem5_t'], ext_CWA['sem10_t'] = ext_CWA['sem1_t']+s1, \
ext_CWA['sem3_t']+s3, ext_CWA['sem5_t']+s5, ext_CWA['sem10_t']+s10
sem_t_triples_ext+=1
if self.metric == 'ranks' or self.metric == 'all':
filt_hit10_t += torch.where(indices_tail[:10] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_t += torch.where(indices_tail[:5] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_t += torch.where(indices_tail[:3] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_t += torch.where(indices_tail[:1] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit10_h += torch.where(indices_head[:10] == h.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_h += torch.where(indices_head[:5] == h.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_h += torch.where(indices_head[:3] == h.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_h += torch.where(indices_head[:1] == h.item(), one_tensor, zero_tensor).sum().item()
print(time.time() - start)
filt_mrr_t = np.mean(filt_mrr_t)
filt_mrr_h = np.mean(filt_mrr_h)
filt_mrr = (filt_mrr_h + filt_mrr_t)/2
if self.model_name =='ConvE':
filtered_hits_at_10 = (filt_hit10_h + filt_hit10_t)/(2*X_valid_tails.shape[0])*100
filtered_hits_at_5 = (filt_hit5_h + filt_hit5_t)/(2*X_valid_tails.shape[0])*100
filtered_hits_at_3 = (filt_hit3_h + filt_hit3_t)/(2*X_valid_tails.shape[0])*100
filtered_hits_at_1 = (filt_hit1_h + filt_hit1_t)/(2*X_valid_tails.shape[0])*100
filt_h10_t, filt_h5_t, filt_h3_t, filt_h1_t = filt_hit10_t/X_valid_tails.shape[0]*100, filt_hit5_t/X_valid_tails.shape[0]*100, filt_hit3_t/X_valid_tails.shape[0]*100, filt_hit1_t/X_valid_tails.shape[0]*100
filt_h10_h, filt_h5_h, filt_h3_h, filt_h1_h = filt_hit10_h/X_valid_tails.shape[0]*100, filt_hit5_h/X_valid_tails.shape[0]*100, filt_hit3_h/X_valid_tails.shape[0]*100, filt_hit1_h/X_valid_tails.shape[0]*100
else:
filtered_hits_at_10 = (filt_hit10_h + filt_hit10_t)/(2*X_valid.shape[0])*100
filtered_hits_at_5 = (filt_hit5_h + filt_hit5_t)/(2*X_valid.shape[0])*100
filtered_hits_at_3 = (filt_hit3_h + filt_hit3_t)/(2*X_valid.shape[0])*100
filtered_hits_at_1 = (filt_hit1_h + filt_hit1_t)/(2*X_valid.shape[0])*100
filt_h10_t, filt_h5_t, filt_h3_t, filt_h1_t = filt_hit10_t/X_valid.shape[0]*100, filt_hit5_t/X_valid.shape[0]*100, filt_hit3_t/X_valid.shape[0]*100, filt_hit1_t/X_valid.shape[0]*100
filt_h10_h, filt_h5_h, filt_h3_h, filt_h1_h = filt_hit10_h/X_valid.shape[0]*100, filt_hit5_h/X_valid.shape[0]*100, filt_hit3_h/X_valid.shape[0]*100, filt_hit1_h/X_valid.shape[0]*100
logger.info('{} MRR: {}'.format('Filtered', filt_mrr))
logger.info('{} Hits@{}: {}'.format('Filtered', 1, filtered_hits_at_1))
logger.info('{} Hits@{}: {}'.format('Filtered', 3, filtered_hits_at_3))
logger.info('{} Hits@{}: {}'.format('Filtered', 10, filtered_hits_at_10))
if self.sem == 'schema' or self.sem == 'both':
if self.setting == 'both' or self.setting == 'CWA':
schema_CWA['sem1'] = (((schema_CWA['sem1_h']/sem_h_triples_CWA) + (schema_CWA['sem1_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem3'] = (((schema_CWA['sem3_h']/sem_h_triples_CWA) + (schema_CWA['sem3_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem5'] = (((schema_CWA['sem5_h']/sem_h_triples_CWA) + (schema_CWA['sem5_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem10'] = (((schema_CWA['sem10_h']/sem_h_triples_CWA) + (schema_CWA['sem10_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem1_h'], schema_CWA['sem1_t'] = (schema_CWA['sem1_h']/sem_h_triples_CWA)*100, (schema_CWA['sem1_t']/sem_t_triples_CWA)*100
schema_CWA['sem3_h'], schema_CWA['sem3_t'] = (schema_CWA['sem3_h']/sem_h_triples_CWA)*100, (schema_CWA['sem3_t']/sem_t_triples_CWA)*100
schema_CWA['sem5_h'], schema_CWA['sem5_t'] = (schema_CWA['sem5_h']/sem_h_triples_CWA)*100, (schema_CWA['sem5_t']/sem_t_triples_CWA)*100
schema_CWA['sem10_h'], schema_CWA['sem10_t'] = (schema_CWA['sem10_h']/sem_h_triples_CWA)*100, (schema_CWA['sem10_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem1'] = (((schema_CWA_wp['sem1_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem1_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem3'] = (((schema_CWA_wp['sem3_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem3_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem5'] = (((schema_CWA_wp['sem5_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem5_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem10'] = (((schema_CWA_wp['sem10_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem10_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem1_h'], schema_CWA_wp['sem1_t'] = (schema_CWA_wp['sem1_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem1_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem3_h'], schema_CWA_wp['sem3_t'] = (schema_CWA_wp['sem3_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem3_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem5_h'], schema_CWA_wp['sem5_t'] = (schema_CWA_wp['sem5_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem5_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem10_h'], schema_CWA_wp['sem10_t'] = (schema_CWA_wp['sem10_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem10_t']/sem_t_triples_CWA)*100
for k in [1, 3, 5, 10]:
logger.info('[Schema|CWA] Sem@{}: {}'.format(k, (eval("schema_CWA['sem"+str(k)+"']"))))
logger.info('[Schema|CWA|Wu-Palmer] Sem@{}: {}'.format(k, (eval("schema_CWA_wp['sem"+str(k)+"']"))))
if self.setting == 'both' or self.setting == 'OWA':
schema_OWA['sem1'] = (((schema_OWA['sem1_h']/sem_h_triples_OWA) + (schema_OWA['sem1_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem3'] = (((schema_OWA['sem3_h']/sem_h_triples_OWA) + (schema_OWA['sem3_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem5'] = (((schema_OWA['sem5_h']/sem_h_triples_OWA) + (schema_OWA['sem5_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem10'] = (((schema_OWA['sem10_h']/sem_h_triples_OWA) + (schema_OWA['sem10_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem1_h'], schema_OWA['sem1_t'] = (schema_OWA['sem1_h']/sem_h_triples_OWA)*100, (schema_OWA['sem1_t']/sem_t_triples_OWA)*100
schema_OWA['sem3_h'], schema_OWA['sem3_t'] = (schema_OWA['sem3_h']/sem_h_triples_OWA)*100, (schema_OWA['sem3_t']/sem_t_triples_OWA)*100
schema_OWA['sem5_h'], schema_OWA['sem5_t'] = (schema_OWA['sem5_h']/sem_h_triples_OWA)*100, (schema_OWA['sem5_t']/sem_t_triples_OWA)*100
schema_OWA['sem10_h'], schema_OWA['sem10_t'] = (schema_OWA['sem10_h']/sem_h_triples_OWA)*100, (schema_OWA['sem10_t']/sem_t_triples_OWA)*100
for k in [1, 3, 5, 10]:
logger.info('[Schema|OWA] Sem@{}: {}'.format(k, (eval("schema_OWA['sem"+str(k)+"']"))))
if self.sem == 'extensional' or self.sem == 'both':
ext_CWA['sem1'] = (((ext_CWA['sem1_h']/sem_h_triples_ext) + (ext_CWA['sem1_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem3'] = (((ext_CWA['sem3_h']/sem_h_triples_ext) + (ext_CWA['sem3_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem5'] = (((ext_CWA['sem5_h']/sem_h_triples_ext) + (ext_CWA['sem5_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem10'] = (((ext_CWA['sem10_h']/sem_h_triples_ext) + (ext_CWA['sem10_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem1_h'], ext_CWA['sem1_t'] = (ext_CWA['sem1_h']/sem_h_triples_ext)*100, (ext_CWA['sem1_t']/sem_t_triples_ext)*100
ext_CWA['sem3_h'], ext_CWA['sem3_t'] = (ext_CWA['sem3_h']/sem_h_triples_ext)*100, (ext_CWA['sem3_t']/sem_t_triples_ext)*100
ext_CWA['sem5_h'], ext_CWA['sem5_t'] = (ext_CWA['sem5_h']/sem_h_triples_ext)*100, (ext_CWA['sem5_t']/sem_t_triples_ext)*100
ext_CWA['sem10_h'], ext_CWA['sem10_t'] = (ext_CWA['sem10_h']/sem_h_triples_ext)*100, (ext_CWA['sem10_t']/sem_t_triples_ext)*100
for k in [1, 3, 5, 10]:
logger.info('[Extensional] Sem@{}: {}'.format(k, (eval("ext_CWA['sem"+str(k)+"']"))))
if self.metric == 'sem' or self.metric == 'all':
return filt_mrr, filtered_hits_at_1, filtered_hits_at_3, filtered_hits_at_5, filtered_hits_at_10, filt_mrr_h, filt_mrr_t, \
filt_h1_h, filt_h1_t, filt_h3_h, filt_h3_t, filt_h5_h, filt_h5_t, filt_h10_h, filt_h10_t, schema_CWA, schema_OWA, ext_CWA, schema_CWA_wp
else:
return filt_mrr, filtered_hits_at_1, filtered_hits_at_3, filtered_hits_at_5, filtered_hits_at_10,\
filt_mrr_h, filt_mrr_t, filt_h1_h, filt_h1_t, filt_h3_h, filt_h3_t, filt_h5_h, filt_h5_t, filt_h10_h, filt_h10_t
def test(self):
schema_OWA = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
sem_t_triples_OWA, sem_h_triples_OWA = 0, 0 # for keeping track of the # of triples for which Sem@K_h/t can be calculated
schema_CWA = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
schema_CWA_wp = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
sem_t_triples_CWA, sem_h_triples_CWA= 0, 0 # for keeping track of the # of triples for which Sem@K_h/t can be calculated
ext_CWA = {'sem1_h': 0.0, 'sem3_h': 0.0, 'sem5_h': 0.0, 'sem10_h': 0.0, 'sem1_t': 0.0, 'sem3_t': 0.0, 'sem5_t': 0.0, 'sem10_t': 0.0, \
'sem1': 0.0, 'sem3': 0.0, 'sem5': 0.0, 'sem10': 0.0}
sem_t_triples_ext, sem_h_triples_ext= 0, 0 # for keeping track of the # of triples for which Sem@K_h/t can be calculated
filt_hit1_h, filt_hit1_t, filt_hit3_h, filt_hit3_t, filt_hit5_h, filt_hit5_t, filt_hit10_h, filt_hit10_t = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
filt_mrr_h, filt_mrr_t = [], []
X_valid_or_test = torch.from_numpy((self.dataset.data[self.valid_or_test]))
zero_tensor = torch.tensor([0], device=self.device)
one_tensor = torch.tensor([1], device=self.device)
num_ent = self.dataset.num_ent()
all_entities = torch.arange(end=num_ent, device=self.device).unsqueeze(0)
start = time.time()
if self.model_name =='ConvE':
half_idx = int(X_valid_or_test.shape[0]/2)
X_valid_or_test_tails = X_valid_or_test[:half_idx]
X_valid_or_test_inv = X_valid_or_test[half_idx:]
for triple in tqdm(X_valid_or_test_tails):
h,r,t = triple[0], triple[1], triple[2]
rm_idx_t = self.get_observed_t(h,r,t)
tails_predictions = self.model.get_score(h.to(self.device),r.to(self.device),t.to(self.device)).squeeze()
tails_predictions[[rm_idx_t]] = - np.inf
indices_tail = tails_predictions.argsort(descending=True)
filt_rank_t = (indices_tail == t).nonzero(as_tuple=True)[0].item() + 1
filt_mrr_t.append(1.0/filt_rank_t)
if self.metric == 'sem' or self.metric == 'all':
if self.sem == 'schema' or self.sem == 'both':
if r.item() in self.dataset.r2id2range2id.keys() and self.dataset.r2id2range2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2range2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
sem_t_triples_CWA+=1
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
elif self.setting=='OWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA_wp['sem1_t'], schema_CWA_wp['sem3_t'], schema_CWA_wp['sem5_t'], schema_CWA_wp['sem10_t'] = schema_CWA_wp['sem1_t']+s1, \
schema_CWA_wp['sem3_t']+s3, schema_CWA_wp['sem5_t']+s5, schema_CWA_wp['sem10_t']+s10
sem_t_triples_CWA+=1
if self.sem == 'extensional' or self.sem == 'both':
if r.item() in self.dataset.r2ts.keys():
if len(self.dataset.r2ts[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_tail[:10], r.item(), side='tail', k=10)
ext_CWA['sem1_t'], ext_CWA['sem3_t'], ext_CWA['sem5_t'], ext_CWA['sem10_t'] = ext_CWA['sem1_t']+s1, \
ext_CWA['sem3_t']+s3, ext_CWA['sem5_t']+s5, ext_CWA['sem10_t']+s10
sem_t_triples_ext+=1
if self.metric == 'ranks' or self.metric == 'all':
filt_hit10_t += torch.where(indices_tail[:10] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_t += torch.where(indices_tail[:5] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_t += torch.where(indices_tail[:3] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_t += torch.where(indices_tail[:1] == t.item(), one_tensor, zero_tensor).sum().item()
# reversed triples
for triple in tqdm(X_valid_or_test_inv):
h,r,t = triple[0], triple[1], triple[2]
rm_idx_t = self.get_observed_t(h,r,t)
tails_predictions = self.model.get_score(h.to(self.device),r.to(self.device),t.to(self.device)).squeeze()
tails_predictions[[rm_idx_t]] = - np.inf
indices_tail = tails_predictions.argsort(descending=True)
filt_rank_h = (indices_tail == t).nonzero(as_tuple=True)[0].item() + 1
filt_mrr_h.append(1.0/filt_rank_h)
if self.metric == 'sem' or self.metric == 'all':
if self.sem == 'schema' or self.sem == 'both':
if r.item() in self.dataset.r2id2range2id.keys() and self.dataset.r2id2range2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2range2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
sem_h_triples_CWA+=1
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
elif self.setting=='OWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA_wp['sem1_h'], schema_CWA_wp['sem3_h'], schema_CWA_wp['sem5_h'], schema_CWA_wp['sem10_h'] = schema_CWA_wp['sem1_h']+s1, \
schema_CWA_wp['sem3_h']+s3, schema_CWA_wp['sem5_h']+s5, schema_CWA_wp['sem10_h']+s10
sem_h_triples_CWA+=1
if self.sem == 'extensional' or self.sem == 'both':
if r.item() in self.dataset.r2ts.keys():
if len(self.dataset.r2ts[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_tail[:10], r.item(), side='tail', k=10)
ext_CWA['sem1_h'], ext_CWA['sem3_h'], ext_CWA['sem5_h'], ext_CWA['sem10_h'] = ext_CWA['sem1_h']+s1, \
ext_CWA['sem3_h']+s3, ext_CWA['sem5_h']+s5, ext_CWA['sem10_h']+s10
sem_h_triples_ext+=1
if self.metric == 'ranks' or self.metric == 'all':
filt_hit10_h += torch.where(indices_tail[:10] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_h += torch.where(indices_tail[:5] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_h += torch.where(indices_tail[:3] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_h += torch.where(indices_tail[:1] == t.item(), one_tensor, zero_tensor).sum().item()
else:
for triple in tqdm(X_valid_or_test):
h,r,t = triple[0], triple[1], triple[2]
rm_idx_t = self.get_observed_t(h,r,t)
rm_idx_h = self.get_observed_h(h,r,t)
heads_predictions, tails_predictions = self.predictions(h,r,t,all_entities)
heads_predictions[[rm_idx_h]], tails_predictions[[rm_idx_t]] = -np.inf, -np.inf #sûr à 100% => OK CORRIGER observed_heads/tails DBpedia
indices_tail, indices_head = tails_predictions.argsort(descending=True), heads_predictions.argsort(descending=True)
filt_rank_h = (indices_head == h).nonzero(as_tuple=True)[0].item() + 1
filt_rank_t = (indices_tail == t).nonzero(as_tuple=True)[0].item() + 1
# Filtered MR and MRR
filt_mrr_h.append(1.0/filt_rank_h)
filt_mrr_t.append(1.0/filt_rank_t)
if self.metric == 'sem' or self.metric == 'all':
if self.sem == 'schema' or self.sem == 'both':
if r.item() in self.dataset.r2id2dom2id.keys() and self.dataset.r2id2dom2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2dom2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_head[:100], r.item(), side='head', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
sem_h_triples_CWA+=1
s1, s3, s5, s10 = self.sem_at_k(indices_head[:100], r.item(), side='head', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
elif self.setting=='OWA':
s1, s3, s5, s10 = self.sem_at_k(indices_head[:100], r.item(), side='head', k=10, setting='OWA')
schema_OWA['sem1_h'], schema_OWA['sem3_h'], schema_OWA['sem5_h'], schema_OWA['sem10_h'] = schema_OWA['sem1_h']+s1, \
schema_OWA['sem3_h']+s3, schema_OWA['sem5_h']+s5, schema_OWA['sem10_h']+s10
sem_h_triples_OWA+=1
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_head[:100], r.item(), side='head', k=10, setting='CWA')
schema_CWA['sem1_h'], schema_CWA['sem3_h'], schema_CWA['sem5_h'], schema_CWA['sem10_h'] = schema_CWA['sem1_h']+s1, \
schema_CWA['sem3_h']+s3, schema_CWA['sem5_h']+s5, schema_CWA['sem10_h']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_head[:100], r.item(), side='head', k=10, setting='CWA')
schema_CWA_wp['sem1_h'], schema_CWA_wp['sem3_h'], schema_CWA_wp['sem5_h'], schema_CWA_wp['sem10_h'] = schema_CWA_wp['sem1_h']+s1, \
schema_CWA_wp['sem3_h']+s3, schema_CWA_wp['sem5_h']+s5, schema_CWA_wp['sem10_h']+s10
sem_h_triples_CWA+=1
if r.item() in self.dataset.r2id2range2id.keys() and self.dataset.r2id2range2id[r.item()] in self.dataset.class2id2ent2id.keys():
if len(self.dataset.class2id2ent2id[self.dataset.r2id2range2id[r.item()]]) >= 10:
if self.setting == 'both':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
sem_t_triples_CWA+=1
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
elif self.setting=='OWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='OWA')
schema_OWA['sem1_t'], schema_OWA['sem3_t'], schema_OWA['sem5_t'], schema_OWA['sem10_t'] = schema_OWA['sem1_t']+s1, \
schema_OWA['sem3_t']+s3, schema_OWA['sem5_t']+s5, schema_OWA['sem10_t']+s10
sem_t_triples_OWA+=1
elif self.setting=='CWA':
s1, s3, s5, s10 = self.sem_at_k(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA['sem1_t'], schema_CWA['sem3_t'], schema_CWA['sem5_t'], schema_CWA['sem10_t'] = schema_CWA['sem1_t']+s1, \
schema_CWA['sem3_t']+s3, schema_CWA['sem5_t']+s5, schema_CWA['sem10_t']+s10
s1, s3, s5, s10 = self.sem_at_k_wp(indices_tail[:100], r.item(), side='tail', k=10, setting='CWA')
schema_CWA_wp['sem1_t'], schema_CWA_wp['sem3_t'], schema_CWA_wp['sem5_t'], schema_CWA_wp['sem10_t'] = schema_CWA_wp['sem1_t']+s1, \
schema_CWA_wp['sem3_t']+s3, schema_CWA_wp['sem5_t']+s5, schema_CWA_wp['sem10_t']+s10
sem_t_triples_CWA+=1
if self.sem == 'extensional' or self.sem == 'both':
if r.item() in self.dataset.r2hs.keys():
if len(self.dataset.r2hs[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_head[:10], r.item(), side='head', k=10)
ext_CWA['sem1_h'], ext_CWA['sem3_h'], ext_CWA['sem5_h'], ext_CWA['sem10_h'] = ext_CWA['sem1_h']+s1, \
ext_CWA['sem3_h']+s3, ext_CWA['sem5_h']+s5, ext_CWA['sem10_h']+s10
sem_h_triples_ext+=1
if r.item() in self.dataset.r2ts.keys():
if len(self.dataset.r2ts[r.item()]) >= 10:
s1, s3, s5, s10 = self.sem_at_k_ext(indices_tail[:10], r.item(), side='tail', k=10)
ext_CWA['sem1_t'], ext_CWA['sem3_t'], ext_CWA['sem5_t'], ext_CWA['sem10_t'] = ext_CWA['sem1_t']+s1, \
ext_CWA['sem3_t']+s3, ext_CWA['sem5_t']+s5, ext_CWA['sem10_t']+s10
sem_t_triples_ext+=1
if self.metric == 'ranks' or self.metric == 'all':
filt_hit10_t += torch.where(indices_tail[:10] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_t += torch.where(indices_tail[:5] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_t += torch.where(indices_tail[:3] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_t += torch.where(indices_tail[:1] == t.item(), one_tensor, zero_tensor).sum().item()
filt_hit10_h += torch.where(indices_head[:10] == h.item(), one_tensor, zero_tensor).sum().item()
filt_hit5_h += torch.where(indices_head[:5] == h.item(), one_tensor, zero_tensor).sum().item()
filt_hit3_h += torch.where(indices_head[:3] == h.item(), one_tensor, zero_tensor).sum().item()
filt_hit1_h += torch.where(indices_head[:1] == h.item(), one_tensor, zero_tensor).sum().item()
print(time.time() - start)
filt_mrr_t = np.mean(filt_mrr_t)
filt_mrr_h = np.mean(filt_mrr_h)
filt_mrr = (filt_mrr_h + filt_mrr_t)/2
if self.model_name =='ConvE':
filtered_hits_at_10 = (filt_hit10_h + filt_hit10_t)/(X_valid_or_test_tails.shape[0]+X_valid_or_test_inv.shape[0])*100
filtered_hits_at_5 = (filt_hit5_h + filt_hit5_t)/(X_valid_or_test_tails.shape[0]+X_valid_or_test_inv.shape[0])*100
filtered_hits_at_3 = (filt_hit3_h + filt_hit3_t)/(X_valid_or_test_tails.shape[0]+X_valid_or_test_inv.shape[0])*100
filtered_hits_at_1 = (filt_hit1_h + filt_hit1_t)/(X_valid_or_test_tails.shape[0]+X_valid_or_test_inv.shape[0])*100
filt_h10_t, filt_h5_t, filt_h3_t, filt_h1_t = filt_hit10_t/X_valid_or_test_tails.shape[0]*100, filt_hit5_t/X_valid_or_test_tails.shape[0]*100, filt_hit3_t/X_valid_or_test_tails.shape[0]*100, filt_hit1_t/X_valid_or_test_tails.shape[0]*100
filt_h10_h, filt_h5_h, filt_h3_h, filt_h1_h = filt_hit10_h/X_valid_or_test_inv.shape[0]*100, filt_hit5_h/X_valid_or_test_inv.shape[0]*100, filt_hit3_h/X_valid_or_test_inv.shape[0]*100, filt_hit1_h/X_valid_or_test_inv.shape[0]*100
else:
filtered_hits_at_10 = (filt_hit10_h + filt_hit10_t)/(2*X_valid_or_test.shape[0])*100
filtered_hits_at_5 = (filt_hit5_h + filt_hit5_t)/(2*X_valid_or_test.shape[0])*100
filtered_hits_at_3 = (filt_hit3_h + filt_hit3_t)/(2*X_valid_or_test.shape[0])*100
filtered_hits_at_1 = (filt_hit1_h + filt_hit1_t)/(2*X_valid_or_test.shape[0])*100
filt_h10_t, filt_h5_t, filt_h3_t, filt_h1_t = filt_hit10_t/X_valid_or_test.shape[0]*100, filt_hit5_t/X_valid_or_test.shape[0]*100, filt_hit3_t/X_valid_or_test.shape[0]*100, filt_hit1_t/X_valid_or_test.shape[0]*100
filt_h10_h, filt_h5_h, filt_h3_h, filt_h1_h = filt_hit10_h/X_valid_or_test.shape[0]*100, filt_hit5_h/X_valid_or_test.shape[0]*100, filt_hit3_h/X_valid_or_test.shape[0]*100, filt_hit1_h/X_valid_or_test.shape[0]*100
logger.info('{} MRR: {}'.format('Filtered', filt_mrr))
logger.info('{} Hits@{}: {}'.format('Filtered', 1, filtered_hits_at_1))
logger.info('{} Hits@{}: {}'.format('Filtered', 3, filtered_hits_at_3))
logger.info('{} Hits@{}: {}'.format('Filtered', 5, filtered_hits_at_5))
logger.info('{} Hits@{}: {}'.format('Filtered', 10, filtered_hits_at_10))
if self.sem == 'schema' or self.sem == 'both':
if self.setting == 'both' or self.setting == 'CWA':
print('sem_t_triples_CWA: ', sem_t_triples_CWA)
print('sem_h_triples_CWA: ', sem_h_triples_CWA)
schema_CWA['sem1'] = (((schema_CWA['sem1_h']/sem_h_triples_CWA) + (schema_CWA['sem1_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem3'] = (((schema_CWA['sem3_h']/sem_h_triples_CWA) + (schema_CWA['sem3_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem5'] = (((schema_CWA['sem5_h']/sem_h_triples_CWA) + (schema_CWA['sem5_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem10'] = (((schema_CWA['sem10_h']/sem_h_triples_CWA) + (schema_CWA['sem10_t']/sem_t_triples_CWA))/2)*100
schema_CWA['sem1_h'], schema_CWA['sem1_t'] = (schema_CWA['sem1_h']/sem_h_triples_CWA)*100, (schema_CWA['sem1_t']/sem_t_triples_CWA)*100
schema_CWA['sem3_h'], schema_CWA['sem3_t'] = (schema_CWA['sem3_h']/sem_h_triples_CWA)*100, (schema_CWA['sem3_t']/sem_t_triples_CWA)*100
schema_CWA['sem5_h'], schema_CWA['sem5_t'] = (schema_CWA['sem5_h']/sem_h_triples_CWA)*100, (schema_CWA['sem5_t']/sem_t_triples_CWA)*100
schema_CWA['sem10_h'], schema_CWA['sem10_t'] = (schema_CWA['sem10_h']/sem_h_triples_CWA)*100, (schema_CWA['sem10_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem1'] = (((schema_CWA_wp['sem1_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem1_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem3'] = (((schema_CWA_wp['sem3_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem3_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem5'] = (((schema_CWA_wp['sem5_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem5_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem10'] = (((schema_CWA_wp['sem10_h']/sem_h_triples_CWA) + (schema_CWA_wp['sem10_t']/sem_t_triples_CWA))/2)*100
schema_CWA_wp['sem1_h'], schema_CWA_wp['sem1_t'] = (schema_CWA_wp['sem1_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem1_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem3_h'], schema_CWA_wp['sem3_t'] = (schema_CWA_wp['sem3_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem3_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem5_h'], schema_CWA_wp['sem5_t'] = (schema_CWA_wp['sem5_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem5_t']/sem_t_triples_CWA)*100
schema_CWA_wp['sem10_h'], schema_CWA_wp['sem10_t'] = (schema_CWA_wp['sem10_h']/sem_h_triples_CWA)*100, (schema_CWA_wp['sem10_t']/sem_t_triples_CWA)*100
for k in [1, 3, 5, 10]:
logger.info('[Schema|CWA] Sem@{}: {}'.format(k, (eval("schema_CWA['sem"+str(k)+"']"))))
logger.info('[Schema|CWA|Wu-Palmer] Sem@{}: {}'.format(k, (eval("schema_CWA_wp['sem"+str(k)+"']"))))
if self.setting == 'both' or self.setting == 'OWA':
schema_OWA['sem1'] = (((schema_OWA['sem1_h']/sem_h_triples_OWA) + (schema_OWA['sem1_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem3'] = (((schema_OWA['sem3_h']/sem_h_triples_OWA) + (schema_OWA['sem3_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem5'] = (((schema_OWA['sem5_h']/sem_h_triples_OWA) + (schema_OWA['sem5_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem10'] = (((schema_OWA['sem10_h']/sem_h_triples_OWA) + (schema_OWA['sem10_t']/sem_t_triples_OWA))/2)*100
schema_OWA['sem1_h'], schema_OWA['sem1_t'] = (schema_OWA['sem1_h']/sem_h_triples_OWA)*100, (schema_OWA['sem1_t']/sem_t_triples_OWA)*100
schema_OWA['sem3_h'], schema_OWA['sem3_t'] = (schema_OWA['sem3_h']/sem_h_triples_OWA)*100, (schema_OWA['sem3_t']/sem_t_triples_OWA)*100
schema_OWA['sem5_h'], schema_OWA['sem5_t'] = (schema_OWA['sem5_h']/sem_h_triples_OWA)*100, (schema_OWA['sem5_t']/sem_t_triples_OWA)*100
schema_OWA['sem10_h'], schema_OWA['sem10_t'] = (schema_OWA['sem10_h']/sem_h_triples_OWA)*100, (schema_OWA['sem10_t']/sem_t_triples_OWA)*100
for k in [1, 3, 5, 10]:
logger.info('[Schema|OWA] Sem@{}: {}'.format(k, (eval("schema_OWA['sem"+str(k)+"']"))))
if self.sem == 'extensional' or self.sem == 'both':
ext_CWA['sem1'] = (((ext_CWA['sem1_h']/sem_h_triples_ext) + (ext_CWA['sem1_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem3'] = (((ext_CWA['sem3_h']/sem_h_triples_ext) + (ext_CWA['sem3_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem5'] = (((ext_CWA['sem5_h']/sem_h_triples_ext) + (ext_CWA['sem5_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem10'] = (((ext_CWA['sem10_h']/sem_h_triples_ext) + (ext_CWA['sem10_t']/sem_t_triples_ext))/2)*100
ext_CWA['sem1_h'], ext_CWA['sem1_t'] = (ext_CWA['sem1_h']/sem_h_triples_ext)*100, (ext_CWA['sem1_t']/sem_t_triples_ext)*100
ext_CWA['sem3_h'], ext_CWA['sem3_t'] = (ext_CWA['sem3_h']/sem_h_triples_ext)*100, (ext_CWA['sem3_t']/sem_t_triples_ext)*100
ext_CWA['sem5_h'], ext_CWA['sem5_t'] = (ext_CWA['sem5_h']/sem_h_triples_ext)*100, (ext_CWA['sem5_t']/sem_t_triples_ext)*100
ext_CWA['sem10_h'], ext_CWA['sem10_t'] = (ext_CWA['sem10_h']/sem_h_triples_ext)*100, (ext_CWA['sem10_t']/sem_t_triples_ext)*100
for k in [1, 3, 5, 10]:
logger.info('[Extensional] Sem@{}: {}'.format(k, (eval("ext_CWA['sem"+str(k)+"']"))))
if self.metric == 'sem' or self.metric == 'all':
return filt_mrr, filtered_hits_at_1, filtered_hits_at_3, filtered_hits_at_5, filtered_hits_at_10, filt_mrr_h, filt_mrr_t, \
filt_h1_h, filt_h1_t, filt_h3_h, filt_h3_t, filt_h5_h, filt_h5_t, filt_h10_h, filt_h10_t, schema_CWA, schema_OWA, ext_CWA, schema_CWA_wp
else:
return filt_mrr, filtered_hits_at_1, filtered_hits_at_3, filtered_hits_at_5, filtered_hits_at_10,\
filt_mrr_h, filt_mrr_t, filt_h1_h, filt_h1_t, filt_h3_h, filt_h3_t, filt_h5_h, filt_h5_t, filt_h10_h, filt_h10_t
def sem_at_k(self, preds, rel, side='head', k=10, setting='CWA'):
'''
example: [known, unknown, known, known, unknown, known, known, unknown....]
in that example, to compute Sem@5, we look at the types of scored entities n° 1, 3, 4, 6 and 7.
'''
valid_types = []
for pred in preds.tolist():
if len(valid_types) == 10:
return((valid_types[0]), np.mean(valid_types[:3]), np.mean(valid_types[:5]), np.mean(valid_types[:10]))
else:
try:
classes = self.dataset.instype_all[pred]
if side=='head':
dom = self.dataset.r2id2dom2id[rel]
valid_types.append(1 if dom in classes else 0)
elif side=='tail':
rang = self.dataset.r2id2range2id[rel]
valid_types.append(1 if rang in classes else 0)
except KeyError:
valid_types.append(0)
def sem_at_k_wp(self, preds, rel, side='head', k=10, setting='CWA'):
valid_types = []
for pred in preds.tolist():
if len(valid_types) == 10:
return((valid_types[0]), np.mean(valid_types[:3]), np.mean(valid_types[:5]), np.mean(valid_types[:10]))
else:
try:
classes = self.dataset.instype_all[pred]
if side=='head':
dom = self.dataset.r2id2dom2id[rel]
if self.name == 'FB15K237':
valid_types.append(1 if dom in classes else self.wupalmer_fb15(dom, classes, rel, side))
else:
valid_types.append(1 if dom in classes else self.wupalmer(dom, classes))
elif side=='tail':
rang = self.dataset.r2id2range2id[rel]
if self.name == 'FB15K237':
valid_types.append(1 if rang in classes else self.wupalmer_fb15(rang, classes, rel, side))
else:
valid_types.append(1 if rang in classes else self.wupalmer(rang, classes))
except KeyError:
continue
def wupalmer_fb15(self, true_cl, candidate_cls, rel, side):
if candidate_cls == []:
return 0.0
if side=='head':
return(max([0.5 if candidate_cls[cl] == self.dataset.r2id2metadom2id[rel] else 0 for cl in range(len(candidate_cls))]))
elif side=='tail':
return(max([0.5 if candidate_cls[cl] == self.dataset.r2id2metarange2id[rel] else 0 for cl in range(len(candidate_cls))]))
def wupalmer(self, true_cl, candidate_cls):
if self.name == 'DB93K':
truth = (self.dataset.id2class[true_cl])[1:-1]
wp_lst = [str(concept.similarity(truth, (self.dataset.id2class[cl])[1:-1], 'wup')) for cl in candidate_cls]
wp_lst = list(map(lambda x: float(x.replace('link error', '0.0')), wp_lst))
return (max(wp_lst))
elif 'YAGO' in self.name:
lca = [self.find_lca(true_cl, cl) for cl in candidate_cls]
if self.name == 'YAGO3-37K':
wp_lst = [(2.0*self.dataset.dist_classes2id[lca[i]][37747] / (self.dataset.dist_classes2id[true_cl][lca[i]] + self.dataset.dist_classes2id[candidate_cls[i]][lca[i]] + \
(2.0*self.dataset.dist_classes2id[lca[i]][37747]))) for i in range(len(candidate_cls))]
elif self.name == 'YAGO4-18K':
wp_lst = [(2.0*self.dataset.dist_classes2id[lca[i]][18978] / (self.dataset.dist_classes2id[true_cl][lca[i]] + self.dataset.dist_classes2id[candidate_cls[i]][lca[i]] + \
(2.0*self.dataset.dist_classes2id[lca[i]][18978]))) for i in range(len(candidate_cls))]
return (max(wp_lst))
else:
print('Wu-Palmer not implemented for this dataset.')
return 0
def find_lca(self, c1, c2):
if c1 in self.dataset.dist_classes2id[c2]:
lca = c1
elif c2 in self.dataset.dist_classes2id[c1]:
lca = c2
else:
dict_seq = [self.dataset.dist_classes2id[c1], self.dataset.dist_classes2id[c2]]
res = reduce(lambda d1,d2: {k: d1.get(k,0)+d2.get(k,0) for k in set(d1)&set(d2)}, dict_seq)
lca = min(res, key=res.get)
return lca
def sem_at_k_ext(self, preds, rel, side='head', k=10):
'''Extensional version of Sem@K'''
preds = preds[:10]
if side=='head':
seen_entities = self.dataset.r2hs[rel]
else:
seen_entities = self.dataset.r2ts[rel]
valid_types = []
for pred in preds.tolist():
valid_types.append(1 if pred in (seen_entities) else 0)
return ((valid_types[0]), np.mean(valid_types[:3]), np.mean(valid_types[:5]), np.mean(valid_types[:10]))
def sem_until(self, preds, rel, side, thresholds = [1,3,5,10]):
kmax = max(thresholds)
ks = thresholds.copy()
for k in thresholds:
globals()[f'lst{k}'] = []
if side=='head':
for pred in preds.tolist():
if sum(globals()[f'lst{k}']) == kmax:
return [len(globals()[f'lst{k}']) for k in thresholds]
if pred in self.dataset.instype_all:
for k in ks:
globals()[f'lst{k}'].append(0 if self.dataset.r2id2dom2id[rel] not in (self.dataset.instype_all[pred]) else 1)
for k in thresholds:
if sum(globals()[f'lst{k}'] == k) :
globals()[f'cpt{k}'] = len(globals()[f'lst{k}'])
del ks[k]
else:
continue