add rel

test
2024-06-22 20:42:41 +07:00 · 2024-06-19 00:24:20 +07:00 · 2024-06-19 00:20:44 +07:00 · 2024-06-19 00:19:31 +07:00 · 2024-06-19 00:16:05 +07:00 · 2024-06-19 00:12:43 +07:00
103 changed files with 514568 additions and 779496 deletions
--- a/data/icews14/relations.dict
+++ b/data/icews14/relations.dict
@ -12407,3 +12407,233 @@
 12406	Carry out roadside bombing[65]
 12407	Appeal for target to allow international involvement (non-mediation)[1]
 12408	Reject request for change in leadership[179]
+12409	Criticize or denounce
+12410	Express intent to meet or negotiate
+12411	Consult
+12412	Make an appeal or request
+12413	Abduct, hijack, or take hostage
+12414	Praise or endorse
+12415	Engage in negotiation
+12416	Use unconventional violence
+12417	Make statement
+12418	Arrest, detain, or charge with legal action
+12419	Use conventional military force
+12420	Complain officially
+12421	Impose administrative sanctions
+12422	Express intent to cooperate
+12423	Make a visit
+12424	Appeal for de-escalation of military engagement
+12425	Sign formal agreement
+12426	Attempt to assassinate
+12427	Host a visit
+12428	Increase military alert status
+12429	Impose embargo, boycott, or sanctions
+12430	Provide economic aid
+12431	Demonstrate or rally
+12432	Express intent to engage in diplomatic cooperation (such as policy support)
+12433	Appeal for intelligence
+12434	Demand
+12435	Carry out suicide bombing
+12436	Threaten
+12437	Express intent to provide material aid
+12438	Grant diplomatic recognition
+12439	Meet at a 'third' location
+12440	Accuse
+12441	Investigate
+12442	Reject
+12443	Appeal for diplomatic cooperation (such as policy support)
+12444	Engage in symbolic act
+12445	Defy norms, law
+12446	Consider policy option
+12447	Provide aid
+12448	Sexually assault
+12449	Make empathetic comment
+12450	Bring lawsuit against
+12451	Impose blockade, restrict movement
+12452	Make pessimistic comment
+12453	Protest violently, riot
+12454	Reduce or break diplomatic relations
+12455	Grant asylum
+12456	Engage in diplomatic cooperation
+12457	Make optimistic comment
+12458	Torture
+12459	Refuse to yield
+12460	Appeal for change in leadership
+12461	Cooperate militarily
+12462	Mobilize or increase armed forces
+12463	fight with small arms and light weapons
+12464	Ease administrative sanctions
+12465	Appeal for political reform
+12466	Return, release person(s)
+12467	Discuss by telephone
+12468	Demonstrate for leadership change
+12469	Impose restrictions on political freedoms
+12470	Reduce relations
+12471	Investigate crime, corruption
+12472	Engage in material cooperation
+12473	Appeal to others to meet or negotiate
+12474	Provide humanitarian aid
+12475	Use tactics of violent repression
+12476	Occupy territory
+12477	Demand humanitarian aid
+12478	Threaten non-force
+12479	Express intent to cooperate economically
+12480	Conduct suicide, car, or other non-military bombing
+12481	Demand diplomatic cooperation (such as policy support)
+12482	Demand meeting, negotiation
+12483	Deny responsibility
+12484	Express intent to change institutions, regime
+12485	Give ultimatum
+12486	Appeal for judicial cooperation
+12487	Rally support on behalf of
+12488	Obstruct passage, block
+12489	Share intelligence or information
+12490	Expel or deport individuals
+12491	Confiscate property
+12492	Accuse of aggression
+12493	Physically assault
+12494	Retreat or surrender militarily
+12495	Veto
+12496	Kill by physical assault
+12497	Assassinate
+12498	Appeal for change in institutions, regime
+12499	Forgive
+12500	Reject proposal to meet, discuss, or negotiate
+12501	Express intent to provide humanitarian aid
+12502	Appeal for release of persons or property
+12503	Acknowledge or claim responsibility
+12504	Ease economic sanctions, boycott, embargo
+12505	Express intent to cooperate militarily
+12506	Cooperate economically
+12507	Express intent to provide economic aid
+12508	Mobilize or increase police power
+12509	Employ aerial weapons
+12510	Accuse of human rights abuses
+12511	Conduct strike or boycott
+12512	Appeal for policy change
+12513	Demonstrate military or police power
+12514	Provide military aid
+12515	Reject plan, agreement to settle dispute
+12516	Yield
+12517	Appeal for easing of administrative sanctions
+12518	Mediate
+12519	Apologize
+12520	Express intent to release persons or property
+12521	Express intent to de-escalate military engagement
+12522	Accede to demands for rights
+12523	Demand economic aid
+12524	Impose state of emergency or martial law
+12525	Receive deployment of peacekeepers
+12526	Demand de-escalation of military engagement
+12527	Declare truce, ceasefire
+12528	Reduce or stop humanitarian assistance
+12529	Appeal to others to settle dispute
+12530	Reject request for military aid
+12531	Threaten with political dissent, protest
+12532	Appeal to engage in or accept mediation
+12533	Express intent to ease economic sanctions, boycott, or embargo
+12534	Coerce
+12535	fight with artillery and tanks
+12536	Express intent to cooperate on intelligence
+12537	Express intent to settle dispute
+12538	Express accord
+12539	Decline comment
+12540	Rally opposition against
+12541	Halt negotiations
+12542	Demand that target yields
+12543	Appeal for military aid
+12544	Threaten with military force
+12545	Express intent to provide military protection or peacekeeping
+12546	Threaten with sanctions, boycott, embargo
+12547	Express intent to provide military aid
+12548	Demand change in leadership
+12549	Appeal for economic aid
+12550	Refuse to de-escalate military engagement
+12551	Refuse to release persons or property
+12552	Increase police alert status
+12553	Return, release property
+12554	Ease military blockade
+12555	Appeal for material cooperation
+12556	Express intent to cooperate on judicial matters
+12557	Appeal for economic cooperation
+12558	Demand settling of dispute
+12559	Accuse of crime, corruption
+12560	Defend verbally
+12561	Provide military protection or peacekeeping
+12562	Accuse of espionage, treason
+12563	Seize or damage property
+12564	Accede to requests or demands for political reform
+12565	Appeal for easing of economic sanctions, boycott, or embargo
+12566	Threaten to reduce or stop aid
+12567	Engage in judicial cooperation
+12568	Appeal to yield
+12569	Demand military aid
+12570	Refuse to ease administrative sanctions
+12571	Demand release of persons or property
+12572	Accede to demands for change in leadership
+12573	Appeal for humanitarian aid
+12574	Threaten with repression
+12575	Demand change in institutions, regime
+12576	Demonstrate for policy change
+12577	Appeal for aid
+12578	Appeal for rights
+12579	Engage in violent protest for rights
+12580	Express intent to mediate
+12581	Expel or withdraw peacekeepers
+12582	Appeal for military protection or peacekeeping
+12583	Engage in mass killings
+12584	Accuse of war crimes
+12585	Reject military cooperation
+12586	Threaten to halt negotiations
+12587	Ban political parties or politicians
+12588	Express intent to change leadership
+12589	Demand material cooperation
+12590	Express intent to institute political reform
+12591	Demand easing of administrative sanctions
+12592	Express intent to engage in material cooperation
+12593	Reduce or stop economic assistance
+12594	Express intent to ease administrative sanctions
+12595	Demand intelligence cooperation
+12596	Ease curfew
+12597	Receive inspectors
+12598	Demand rights
+12599	Demand political reform
+12600	Demand judicial cooperation
+12601	Engage in political dissent
+12602	Detonate nuclear weapons
+12603	Violate ceasefire
+12604	Express intent to accept mediation
+12605	Refuse to ease economic sanctions, boycott, or embargo
+12606	Demand mediation
+12607	Obstruct passage to demand leadership change
+12608	Express intent to yield
+12609	Conduct hunger strike
+12610	Threaten to halt mediation
+12611	Reject judicial cooperation
+12612	Reduce or stop military assistance
+12613	Ease political dissent
+12614	Threaten to reduce or break relations
+12615	Demobilize armed forces
+12616	Use as human shield
+12617	Demand policy change
+12618	Accede to demands for change in institutions, regime
+12619	Reject economic cooperation
+12620	Reject material cooperation
+12621	Halt mediation
+12622	Accede to demands for change in policy
+12623	Investigate war crimes
+12624	Threaten with administrative sanctions
+12625	Reduce or stop material aid
+12626	Destroy property
+12627	Express intent to change policy
+12628	Use chemical, biological, or radiological weapons
+12629	Reject request for military protection or peacekeeping
+12630	Demand material aid
+12631	Engage in mass expulsion
+12632	Investigate human rights abuses
+12633	Carry out car bombing
+12634	Expel or withdraw
+12635	Ease state of emergency or martial law
+12636	Carry out roadside bombing
+12637	Appeal for target to allow international involvement (non-mediation)
+12638	Reject request for change in leadership
--- a/data/wikidata12k/about.txt
+++ b/data/wikidata12k/about.txt
@ -1,15 +1,15 @@
-# triples: 291818 
-# entities: 12554 
-# relations: 423 
-# timesteps: 70 
-# test triples: 19271 
-# valid triples: 20208 
-# train triples: 252339 
-Measure method:  N/A  
-Target Size :  423  
-Grow Factor:  0  
-Shrink Factor:  4.0  
-Epsilon Factor: 0  
-Search method: N/A  
-filter_dupes: inter
-nonames: False
+# triples: 291818 
+# entities: 12554 
+# relations: 423 
+# timesteps: 70 
+# test triples: 19271 
+# valid triples: 20208 
+# train triples: 252339 
+Measure method:  N/A  
+Target Size :  423  
+Grow Factor:  0  
+Shrink Factor:  4.0  
+Epsilon Factor: 0  
+Search method: N/A  
+filter_dupes: inter
+nonames: False
--- a/data/wikidata12k/entities.dict
+++ b/data/wikidata12k/entities.dict
--- a/data/wikidata12k/relations.dict
+++ b/data/wikidata12k/relations.dict
@ -1,423 +1,423 @@
-0	P131[0-0]
-1	P131[1-1]
-2	P131[2-2]
-3	P131[3-3]
-4	P131[4-4]
-5	P131[5-5]
-6	P131[6-6]
-7	P131[7-7]
-8	P131[8-8]
-9	P131[9-9]
-10	P131[10-10]
-11	P131[11-11]
-12	P131[12-12]
-13	P131[13-13]
-14	P131[14-14]
-15	P131[15-15]
-16	P131[16-16]
-17	P131[17-17]
-18	P131[18-18]
-19	P131[19-19]
-20	P131[20-20]
-21	P131[21-21]
-22	P131[22-22]
-23	P131[23-23]
-24	P131[24-24]
-25	P131[25-25]
-26	P131[26-26]
-27	P131[27-27]
-28	P131[28-28]
-29	P131[29-29]
-30	P131[30-30]
-31	P131[31-31]
-32	P131[32-32]
-33	P131[33-33]
-34	P131[34-34]
-35	P131[35-35]
-36	P131[36-36]
-37	P131[37-37]
-38	P131[38-38]
-39	P131[39-39]
-40	P131[40-40]
-41	P131[41-41]
-42	P131[42-42]
-43	P131[43-43]
-44	P131[44-44]
-45	P131[45-45]
-46	P131[46-46]
-47	P131[47-47]
-48	P131[48-48]
-49	P131[49-49]
-50	P131[50-50]
-51	P131[51-51]
-52	P131[52-52]
-53	P131[53-53]
-54	P131[54-54]
-55	P131[55-55]
-56	P131[56-56]
-57	P131[57-57]
-58	P131[58-58]
-59	P131[59-59]
-60	P131[60-60]
-61	P131[61-61]
-62	P131[62-62]
-63	P131[63-63]
-64	P131[64-64]
-65	P131[65-65]
-66	P131[66-66]
-67	P131[67-67]
-68	P131[68-68]
-69	P131[69-69]
-70	P1435[65-65]
-71	P39[49-49]
-72	P39[50-50]
-73	P39[51-51]
-74	P39[52-52]
-75	P39[53-53]
-76	P39[54-54]
-77	P39[55-55]
-78	P39[56-56]
-79	P39[57-57]
-80	P39[58-58]
-81	P39[59-59]
-82	P39[60-60]
-83	P39[61-61]
-84	P39[62-62]
-85	P39[63-63]
-86	P39[64-64]
-87	P39[65-65]
-88	P39[66-66]
-89	P39[67-67]
-90	P39[68-68]
-91	P39[69-69]
-92	P54[40-40]
-93	P54[41-41]
-94	P54[42-42]
-95	P54[43-43]
-96	P54[44-44]
-97	P54[45-45]
-98	P54[46-46]
-99	P54[47-47]
-100	P54[48-48]
-101	P54[49-49]
-102	P54[50-50]
-103	P54[51-51]
-104	P54[52-52]
-105	P54[53-53]
-106	P54[54-54]
-107	P54[55-55]
-108	P54[56-56]
-109	P54[57-57]
-110	P54[58-58]
-111	P54[59-59]
-112	P54[60-60]
-113	P54[61-61]
-114	P54[62-62]
-115	P54[63-63]
-116	P54[64-64]
-117	P54[65-65]
-118	P54[66-66]
-119	P54[67-67]
-120	P54[68-68]
-121	P54[69-69]
-122	P31[0-0]
-123	P31[1-1]
-124	P31[2-2]
-125	P31[3-3]
-126	P31[4-4]
-127	P31[5-5]
-128	P31[6-6]
-129	P31[7-7]
-130	P31[8-8]
-131	P31[9-9]
-132	P31[10-10]
-133	P31[11-11]
-134	P31[12-12]
-135	P31[13-13]
-136	P31[14-14]
-137	P31[15-15]
-138	P31[16-16]
-139	P31[17-17]
-140	P31[18-18]
-141	P31[19-19]
-142	P31[20-20]
-143	P31[21-21]
-144	P31[22-22]
-145	P31[23-23]
-146	P31[24-24]
-147	P31[25-25]
-148	P31[26-26]
-149	P31[27-27]
-150	P31[28-28]
-151	P31[29-29]
-152	P31[30-30]
-153	P31[31-31]
-154	P31[32-32]
-155	P31[33-33]
-156	P31[34-34]
-157	P31[35-35]
-158	P31[36-36]
-159	P31[37-37]
-160	P31[38-38]
-161	P31[39-39]
-162	P31[40-40]
-163	P31[41-41]
-164	P31[42-42]
-165	P31[43-43]
-166	P31[44-44]
-167	P31[45-45]
-168	P31[46-46]
-169	P31[47-47]
-170	P31[48-48]
-171	P31[49-49]
-172	P31[50-50]
-173	P31[51-51]
-174	P31[52-52]
-175	P31[53-53]
-176	P31[54-54]
-177	P31[55-55]
-178	P31[56-56]
-179	P31[57-57]
-180	P31[58-58]
-181	P31[59-59]
-182	P31[60-60]
-183	P31[61-61]
-184	P31[62-62]
-185	P31[63-63]
-186	P31[64-64]
-187	P31[65-65]
-188	P31[66-66]
-189	P31[67-67]
-190	P31[68-68]
-191	P31[69-69]
-192	P463[26-26]
-193	P463[27-27]
-194	P463[28-28]
-195	P463[29-29]
-196	P463[30-30]
-197	P463[31-31]
-198	P463[32-32]
-199	P463[33-33]
-200	P463[34-34]
-201	P463[35-35]
-202	P463[36-36]
-203	P463[37-37]
-204	P463[38-38]
-205	P463[39-39]
-206	P463[40-40]
-207	P463[41-41]
-208	P463[42-42]
-209	P463[43-43]
-210	P463[44-44]
-211	P463[45-45]
-212	P463[46-46]
-213	P463[47-47]
-214	P463[48-48]
-215	P463[49-49]
-216	P463[50-50]
-217	P463[51-51]
-218	P463[52-52]
-219	P463[53-53]
-220	P463[54-54]
-221	P463[55-55]
-222	P463[56-56]
-223	P463[57-57]
-224	P463[58-58]
-225	P463[59-59]
-226	P463[60-60]
-227	P463[61-61]
-228	P463[62-62]
-229	P463[63-63]
-230	P463[64-64]
-231	P463[65-65]
-232	P463[66-66]
-233	P463[67-67]
-234	P463[68-68]
-235	P463[69-69]
-236	P512[4-69]
-237	P190[0-29]
-238	P150[0-3]
-239	P1376[39-47]
-240	P463[0-7]
-241	P166[0-7]
-242	P2962[18-30]
-243	P108[29-36]
-244	P39[0-3]
-245	P17[47-48]
-246	P166[21-23]
-247	P793[46-69]
-248	P69[32-41]
-249	P17[57-58]
-250	P190[42-45]
-251	P2962[39-42]
-252	P54[0-18]
-253	P26[56-61]
-254	P150[14-17]
-255	P463[16-17]
-256	P26[39-46]
-257	P579[36-43]
-258	P579[16-23]
-259	P2962[59-60]
-260	P1411[59-61]
-261	P26[20-27]
-262	P6[4-69]
-263	P1435[33-34]
-264	P166[52-53]
-265	P108[49-57]
-266	P150[10-13]
-267	P1346[47-68]
-268	P150[18-21]
-269	P1346[13-46]
-270	P69[20-23]
-271	P39[31-32]
-272	P1411[32-37]
-273	P166[62-63]
-274	P150[44-47]
-275	P2962[61-62]
-276	P150[48-51]
-277	P150[52-55]
-278	P1411[62-67]
-279	P1435[35-36]
-280	P1411[48-51]
-281	P150[22-25]
-282	P2962[63-64]
-283	P2962[65-66]
-284	P166[58-59]
-285	P190[46-49]
-286	P54[34-35]
-287	P1435[4-16]
-288	P463[18-19]
-289	P150[31-34]
-290	P150[35-38]
-291	P39[35-36]
-292	P26[62-69]
-293	P1411[56-58]
-294	P1435[37-38]
-295	P166[60-61]
-296	P39[33-34]
-297	P102[24-31]
-298	P2962[43-46]
-299	P108[37-48]
-300	P190[50-53]
-301	P39[4-6]
-302	P1435[39-40]
-303	P793[0-45]
-304	P150[64-69]
-305	P39[19-22]
-306	P27[30-38]
-307	P2962[31-38]
-308	P1411[24-31]
-309	P102[40-45]
-310	P39[37-38]
-311	P463[8-11]
-312	P1435[41-42]
-313	P27[52-59]
-314	P69[16-19]
-315	P17[16-18]
-316	P190[54-57]
-317	P1435[43-44]
-318	P166[8-15]
-319	P166[45-47]
-320	P2962[47-50]
-321	P39[39-40]
-322	P1411[52-55]
-323	P108[58-69]
-324	P463[20-21]
-325	P39[41-42]
-326	P150[26-30]
-327	P150[39-43]
-328	P1435[45-46]
-329	P26[28-38]
-330	P54[27-30]
-331	P190[58-61]
-332	P17[59-61]
-333	P54[36-37]
-334	P166[16-20]
-335	P166[37-40]
-336	P1435[47-48]
-337	P17[0-3]
-338	P26[47-55]
-339	P1435[49-50]
-340	P1435[25-28]
-341	P150[4-9]
-342	P102[63-69]
-343	P26[0-19]
-344	P1435[17-24]
-345	P39[23-26]
-346	P1435[51-52]
-347	P39[7-11]
-348	P69[12-15]
-349	P69[24-31]
-350	P102[0-23]
-351	P39[43-44]
-352	P579[24-35]
-353	P190[62-65]
-354	P1435[53-54]
-355	P1376[0-18]
-356	P27[0-14]
-357	P463[12-15]
-358	P166[33-36]
-359	P102[32-39]
-360	P17[4-7]
-361	P190[30-41]
-362	P166[24-28]
-363	P190[66-69]
-364	P69[42-69]
-365	P1435[55-56]
-366	P54[31-33]
-367	P39[45-46]
-368	P17[12-15]
-369	P1435[57-58]
-370	P54[19-26]
-371	P2962[51-54]
-372	P2962[67-69]
-373	P1435[59-60]
-374	P579[44-56]
-375	P1435[61-62]
-376	P166[41-44]
-377	P17[19-22]
-378	P1376[19-38]
-379	P17[23-26]
-380	P1376[48-69]
-381	P463[22-23]
-382	P17[27-30]
-383	P1435[63-64]
-384	P69[0-3]
-385	P1435[66-67]
-386	P17[35-38]
-387	P69[8-11]
-388	P1435[68-69]
-389	P17[31-34]
-390	P102[46-53]
-391	P27[60-69]
-392	P579[57-69]
-393	P69[4-7]
-394	P1411[7-14]
-395	P551[0-35]
-396	P108[0-28]
-397	P17[8-11]
-398	P1411[38-47]
-399	P17[43-46]
-400	P17[49-52]
-401	P166[64-69]
-402	P1435[29-32]
-403	P54[38-39]
-404	P39[27-30]
-405	P2962[55-58]
-406	P463[24-25]
-407	P17[39-42]
-408	P17[53-56]
-409	P17[66-69]
-410	P17[62-65]
-411	P1411[15-23]
-412	P166[48-51]
-413	P27[15-29]
-414	P150[56-63]
-415	P27[39-51]
-416	P39[47-48]
-417	P166[29-32]
-418	P39[12-18]
-419	P166[54-57]
-420	P551[36-69]
-421	P579[0-15]
-422	P102[54-62]
+0	P131[0-0]
+1	P131[1-1]
+2	P131[2-2]
+3	P131[3-3]
+4	P131[4-4]
+5	P131[5-5]
+6	P131[6-6]
+7	P131[7-7]
+8	P131[8-8]
+9	P131[9-9]
+10	P131[10-10]
+11	P131[11-11]
+12	P131[12-12]
+13	P131[13-13]
+14	P131[14-14]
+15	P131[15-15]
+16	P131[16-16]
+17	P131[17-17]
+18	P131[18-18]
+19	P131[19-19]
+20	P131[20-20]
+21	P131[21-21]
+22	P131[22-22]
+23	P131[23-23]
+24	P131[24-24]
+25	P131[25-25]
+26	P131[26-26]
+27	P131[27-27]
+28	P131[28-28]
+29	P131[29-29]
+30	P131[30-30]
+31	P131[31-31]
+32	P131[32-32]
+33	P131[33-33]
+34	P131[34-34]
+35	P131[35-35]
+36	P131[36-36]
+37	P131[37-37]
+38	P131[38-38]
+39	P131[39-39]
+40	P131[40-40]
+41	P131[41-41]
+42	P131[42-42]
+43	P131[43-43]
+44	P131[44-44]
+45	P131[45-45]
+46	P131[46-46]
+47	P131[47-47]
+48	P131[48-48]
+49	P131[49-49]
+50	P131[50-50]
+51	P131[51-51]
+52	P131[52-52]
+53	P131[53-53]
+54	P131[54-54]
+55	P131[55-55]
+56	P131[56-56]
+57	P131[57-57]
+58	P131[58-58]
+59	P131[59-59]
+60	P131[60-60]
+61	P131[61-61]
+62	P131[62-62]
+63	P131[63-63]
+64	P131[64-64]
+65	P131[65-65]
+66	P131[66-66]
+67	P131[67-67]
+68	P131[68-68]
+69	P131[69-69]
+70	P1435[65-65]
+71	P39[49-49]
+72	P39[50-50]
+73	P39[51-51]
+74	P39[52-52]
+75	P39[53-53]
+76	P39[54-54]
+77	P39[55-55]
+78	P39[56-56]
+79	P39[57-57]
+80	P39[58-58]
+81	P39[59-59]
+82	P39[60-60]
+83	P39[61-61]
+84	P39[62-62]
+85	P39[63-63]
+86	P39[64-64]
+87	P39[65-65]
+88	P39[66-66]
+89	P39[67-67]
+90	P39[68-68]
+91	P39[69-69]
+92	P54[40-40]
+93	P54[41-41]
+94	P54[42-42]
+95	P54[43-43]
+96	P54[44-44]
+97	P54[45-45]
+98	P54[46-46]
+99	P54[47-47]
+100	P54[48-48]
+101	P54[49-49]
+102	P54[50-50]
+103	P54[51-51]
+104	P54[52-52]
+105	P54[53-53]
+106	P54[54-54]
+107	P54[55-55]
+108	P54[56-56]
+109	P54[57-57]
+110	P54[58-58]
+111	P54[59-59]
+112	P54[60-60]
+113	P54[61-61]
+114	P54[62-62]
+115	P54[63-63]
+116	P54[64-64]
+117	P54[65-65]
+118	P54[66-66]
+119	P54[67-67]
+120	P54[68-68]
+121	P54[69-69]
+122	P31[0-0]
+123	P31[1-1]
+124	P31[2-2]
+125	P31[3-3]
+126	P31[4-4]
+127	P31[5-5]
+128	P31[6-6]
+129	P31[7-7]
+130	P31[8-8]
+131	P31[9-9]
+132	P31[10-10]
+133	P31[11-11]
+134	P31[12-12]
+135	P31[13-13]
+136	P31[14-14]
+137	P31[15-15]
+138	P31[16-16]
+139	P31[17-17]
+140	P31[18-18]
+141	P31[19-19]
+142	P31[20-20]
+143	P31[21-21]
+144	P31[22-22]
+145	P31[23-23]
+146	P31[24-24]
+147	P31[25-25]
+148	P31[26-26]
+149	P31[27-27]
+150	P31[28-28]
+151	P31[29-29]
+152	P31[30-30]
+153	P31[31-31]
+154	P31[32-32]
+155	P31[33-33]
+156	P31[34-34]
+157	P31[35-35]
+158	P31[36-36]
+159	P31[37-37]
+160	P31[38-38]
+161	P31[39-39]
+162	P31[40-40]
+163	P31[41-41]
+164	P31[42-42]
+165	P31[43-43]
+166	P31[44-44]
+167	P31[45-45]
+168	P31[46-46]
+169	P31[47-47]
+170	P31[48-48]
+171	P31[49-49]
+172	P31[50-50]
+173	P31[51-51]
+174	P31[52-52]
+175	P31[53-53]
+176	P31[54-54]
+177	P31[55-55]
+178	P31[56-56]
+179	P31[57-57]
+180	P31[58-58]
+181	P31[59-59]
+182	P31[60-60]
+183	P31[61-61]
+184	P31[62-62]
+185	P31[63-63]
+186	P31[64-64]
+187	P31[65-65]
+188	P31[66-66]
+189	P31[67-67]
+190	P31[68-68]
+191	P31[69-69]
+192	P463[26-26]
+193	P463[27-27]
+194	P463[28-28]
+195	P463[29-29]
+196	P463[30-30]
+197	P463[31-31]
+198	P463[32-32]
+199	P463[33-33]
+200	P463[34-34]
+201	P463[35-35]
+202	P463[36-36]
+203	P463[37-37]
+204	P463[38-38]
+205	P463[39-39]
+206	P463[40-40]
+207	P463[41-41]
+208	P463[42-42]
+209	P463[43-43]
+210	P463[44-44]
+211	P463[45-45]
+212	P463[46-46]
+213	P463[47-47]
+214	P463[48-48]
+215	P463[49-49]
+216	P463[50-50]
+217	P463[51-51]
+218	P463[52-52]
+219	P463[53-53]
+220	P463[54-54]
+221	P463[55-55]
+222	P463[56-56]
+223	P463[57-57]
+224	P463[58-58]
+225	P463[59-59]
+226	P463[60-60]
+227	P463[61-61]
+228	P463[62-62]
+229	P463[63-63]
+230	P463[64-64]
+231	P463[65-65]
+232	P463[66-66]
+233	P463[67-67]
+234	P463[68-68]
+235	P463[69-69]
+236	P512[4-69]
+237	P190[0-29]
+238	P150[0-3]
+239	P1376[39-47]
+240	P463[0-7]
+241	P166[0-7]
+242	P2962[18-30]
+243	P108[29-36]
+244	P39[0-3]
+245	P17[47-48]
+246	P166[21-23]
+247	P793[46-69]
+248	P69[32-41]
+249	P17[57-58]
+250	P190[42-45]
+251	P2962[39-42]
+252	P54[0-18]
+253	P26[56-61]
+254	P150[14-17]
+255	P463[16-17]
+256	P26[39-46]
+257	P579[36-43]
+258	P579[16-23]
+259	P2962[59-60]
+260	P1411[59-61]
+261	P26[20-27]
+262	P6[4-69]
+263	P1435[33-34]
+264	P166[52-53]
+265	P108[49-57]
+266	P150[10-13]
+267	P1346[47-68]
+268	P150[18-21]
+269	P1346[13-46]
+270	P69[20-23]
+271	P39[31-32]
+272	P1411[32-37]
+273	P166[62-63]
+274	P150[44-47]
+275	P2962[61-62]
+276	P150[48-51]
+277	P150[52-55]
+278	P1411[62-67]
+279	P1435[35-36]
+280	P1411[48-51]
+281	P150[22-25]
+282	P2962[63-64]
+283	P2962[65-66]
+284	P166[58-59]
+285	P190[46-49]
+286	P54[34-35]
+287	P1435[4-16]
+288	P463[18-19]
+289	P150[31-34]
+290	P150[35-38]
+291	P39[35-36]
+292	P26[62-69]
+293	P1411[56-58]
+294	P1435[37-38]
+295	P166[60-61]
+296	P39[33-34]
+297	P102[24-31]
+298	P2962[43-46]
+299	P108[37-48]
+300	P190[50-53]
+301	P39[4-6]
+302	P1435[39-40]
+303	P793[0-45]
+304	P150[64-69]
+305	P39[19-22]
+306	P27[30-38]
+307	P2962[31-38]
+308	P1411[24-31]
+309	P102[40-45]
+310	P39[37-38]
+311	P463[8-11]
+312	P1435[41-42]
+313	P27[52-59]
+314	P69[16-19]
+315	P17[16-18]
+316	P190[54-57]
+317	P1435[43-44]
+318	P166[8-15]
+319	P166[45-47]
+320	P2962[47-50]
+321	P39[39-40]
+322	P1411[52-55]
+323	P108[58-69]
+324	P463[20-21]
+325	P39[41-42]
+326	P150[26-30]
+327	P150[39-43]
+328	P1435[45-46]
+329	P26[28-38]
+330	P54[27-30]
+331	P190[58-61]
+332	P17[59-61]
+333	P54[36-37]
+334	P166[16-20]
+335	P166[37-40]
+336	P1435[47-48]
+337	P17[0-3]
+338	P26[47-55]
+339	P1435[49-50]
+340	P1435[25-28]
+341	P150[4-9]
+342	P102[63-69]
+343	P26[0-19]
+344	P1435[17-24]
+345	P39[23-26]
+346	P1435[51-52]
+347	P39[7-11]
+348	P69[12-15]
+349	P69[24-31]
+350	P102[0-23]
+351	P39[43-44]
+352	P579[24-35]
+353	P190[62-65]
+354	P1435[53-54]
+355	P1376[0-18]
+356	P27[0-14]
+357	P463[12-15]
+358	P166[33-36]
+359	P102[32-39]
+360	P17[4-7]
+361	P190[30-41]
+362	P166[24-28]
+363	P190[66-69]
+364	P69[42-69]
+365	P1435[55-56]
+366	P54[31-33]
+367	P39[45-46]
+368	P17[12-15]
+369	P1435[57-58]
+370	P54[19-26]
+371	P2962[51-54]
+372	P2962[67-69]
+373	P1435[59-60]
+374	P579[44-56]
+375	P1435[61-62]
+376	P166[41-44]
+377	P17[19-22]
+378	P1376[19-38]
+379	P17[23-26]
+380	P1376[48-69]
+381	P463[22-23]
+382	P17[27-30]
+383	P1435[63-64]
+384	P69[0-3]
+385	P1435[66-67]
+386	P17[35-38]
+387	P69[8-11]
+388	P1435[68-69]
+389	P17[31-34]
+390	P102[46-53]
+391	P27[60-69]
+392	P579[57-69]
+393	P69[4-7]
+394	P1411[7-14]
+395	P551[0-35]
+396	P108[0-28]
+397	P17[8-11]
+398	P1411[38-47]
+399	P17[43-46]
+400	P17[49-52]
+401	P166[64-69]
+402	P1435[29-32]
+403	P54[38-39]
+404	P39[27-30]
+405	P2962[55-58]
+406	P463[24-25]
+407	P17[39-42]
+408	P17[53-56]
+409	P17[66-69]
+410	P17[62-65]
+411	P1411[15-23]
+412	P166[48-51]
+413	P27[15-29]
+414	P150[56-63]
+415	P27[39-51]
+416	P39[47-48]
+417	P166[29-32]
+418	P39[12-18]
+419	P166[54-57]
+420	P551[36-69]
+421	P579[0-15]
+422	P102[54-62]
--- a/data/wikidata12k/test.txt
+++ b/data/wikidata12k/test.txt
--- a/data/wikidata12k/time_map.dict
+++ b/data/wikidata12k/time_map.dict
@ -1,71 +1,71 @@
-0	19	19
-1	20	1643
-2	1644	1790
-3	1791	1816
-4	1817	1855
-5	1856	1871
-6	1872	1893
-7	1894	1905
-8	1906	1913
-9	1914	1918
-10	1919	1920
-11	1921	1924
-12	1925	1929
-13	1930	1933
-14	1934	1937
-15	1938	1941
-16	1942	1945
-17	1946	1948
-18	1949	1950
-19	1951	1953
-20	1954	1956
-21	1957	1959
-22	1960	1961
-23	1962	1963
-24	1964	1965
-25	1966	1967
-26	1968	1968
-27	1969	1970
-28	1971	1972
-29	1973	1974
-30	1975	1976
-31	1977	1978
-32	1979	1980
-33	1981	1982
-34	1983	1983
-35	1984	1984
-36	1985	1985
-37	1986	1986
-38	1987	1987
-39	1988	1988
-40	1989	1989
-41	1990	1990
-42	1991	1991
-43	1992	1992
-44	1993	1993
-45	1994	1994
-46	1995	1995
-47	1996	1996
-48	1997	1997
-49	1998	1998
-50	1999	1999
-51	2000	2000
-52	2001	2001
-53	2002	2002
-54	2003	2003
-55	2004	2004
-56	2005	2005
-57	2006	2006
-58	2007	2007
-59	2008	2008
-60	2009	2009
-61	2010	2010
-62	2011	2011
-63	2012	2012
-64	2013	2013
-65	2014	2014
-66	2015	2015
-67	2016	2016
-68	2017	2017
-69	2018	2020
-70	2021	2021
+0	19	19
+1	20	1643
+2	1644	1790
+3	1791	1816
+4	1817	1855
+5	1856	1871
+6	1872	1893
+7	1894	1905
+8	1906	1913
+9	1914	1918
+10	1919	1920
+11	1921	1924
+12	1925	1929
+13	1930	1933
+14	1934	1937
+15	1938	1941
+16	1942	1945
+17	1946	1948
+18	1949	1950
+19	1951	1953
+20	1954	1956
+21	1957	1959
+22	1960	1961
+23	1962	1963
+24	1964	1965
+25	1966	1967
+26	1968	1968
+27	1969	1970
+28	1971	1972
+29	1973	1974
+30	1975	1976
+31	1977	1978
+32	1979	1980
+33	1981	1982
+34	1983	1983
+35	1984	1984
+36	1985	1985
+37	1986	1986
+38	1987	1987
+39	1988	1988
+40	1989	1989
+41	1990	1990
+42	1991	1991
+43	1992	1992
+44	1993	1993
+45	1994	1994
+46	1995	1995
+47	1996	1996
+48	1997	1997
+49	1998	1998
+50	1999	1999
+51	2000	2000
+52	2001	2001
+53	2002	2002
+54	2003	2003
+55	2004	2004
+56	2005	2005
+57	2006	2006
+58	2007	2007
+59	2008	2008
+60	2009	2009
+61	2010	2010
+62	2011	2011
+63	2012	2012
+64	2013	2013
+65	2014	2014
+66	2015	2015
+67	2016	2016
+68	2017	2017
+69	2018	2020
+70	2021	2021
--- a/data/wikidata12k/train.txt
+++ b/data/wikidata12k/train.txt
--- a/data/wikidata12k/valid.txt
+++ b/data/wikidata12k/valid.txt
--- a/data/wikidata12k_both/relations.dict
+++ b/data/wikidata12k_both/relations.dict
@ -421,3 +421,27 @@
 420	P551[36-69]
 421	P579[0-15]
 422	P102[54-62]
+423	P131
+424	P1435
+425	P39
+426	P54
+427	P31
+428	P463
+429	P512
+430	P190
+431	P150
+432	P1376
+433	P166
+434	P2962
+435	P108
+436	P17
+437	P793
+438	P69
+439	P26
+440	P579
+441	P1411
+442	P6
+443	P1346
+444	P102
+445	P27
+446	P551
--- a/data/wikidata12k_old/about.txt
+++ b/data/wikidata12k_old/about.txt
@ -1,15 +0,0 @@
-# triples: 291818 
-# entities: 12554 
-# relations: 423 
-# timesteps: 70 
-# test triples: 19271 
-# valid triples: 20208 
-# train triples: 252339 
-Measure method:  N/A  
-Target Size :  423  
-Grow Factor:  0  
-Shrink Factor:  4.0  
-Epsilon Factor: 0  
-Search method: N/A  
-filter_dupes: inter
-nonames: False
--- a/data/wikidata12k_old/entities.dict
+++ b/data/wikidata12k_old/entities.dict
--- a/data/wikidata12k_old/indices_test.txt
+++ b/data/wikidata12k_old/indices_test.txt
--- a/data/wikidata12k_old/indices_train.txt
+++ b/data/wikidata12k_old/indices_train.txt
--- a/data/wikidata12k_old/indices_valid.txt
+++ b/data/wikidata12k_old/indices_valid.txt
--- a/data/wikidata12k_old/raw_entity2id.txt
+++ b/data/wikidata12k_old/raw_entity2id.txt
--- a/data/wikidata12k_old/raw_rel2id.txt
+++ b/data/wikidata12k_old/raw_rel2id.txt
@ -1,24 +0,0 @@
-P1376	0
-P512	4
-P579	3
-P150	18
-P190	5
-P551	19
-P131	1
-P793	21
-P1435	13
-P39	14
-P17	6
-P54	22
-P31	15
-P6	7
-P1411	20
-P2962	2
-P463	9
-P1346	16
-P108	10
-P69	23
-P166	17
-P102	11
-P27	12
-P26	8
--- a/data/wikidata12k_old/raw_test.txt
+++ b/data/wikidata12k_old/raw_test.txt
--- a/data/wikidata12k_old/relations.dict
+++ b/data/wikidata12k_old/relations.dict
@ -1,423 +0,0 @@
-0	P131[0-0]
-1	P131[1-1]
-2	P131[2-2]
-3	P131[3-3]
-4	P131[4-4]
-5	P131[5-5]
-6	P131[6-6]
-7	P131[7-7]
-8	P131[8-8]
-9	P131[9-9]
-10	P131[10-10]
-11	P131[11-11]
-12	P131[12-12]
-13	P131[13-13]
-14	P131[14-14]
-15	P131[15-15]
-16	P131[16-16]
-17	P131[17-17]
-18	P131[18-18]
-19	P131[19-19]
-20	P131[20-20]
-21	P131[21-21]
-22	P131[22-22]
-23	P131[23-23]
-24	P131[24-24]
-25	P131[25-25]
-26	P131[26-26]
-27	P131[27-27]
-28	P131[28-28]
-29	P131[29-29]
-30	P131[30-30]
-31	P131[31-31]
-32	P131[32-32]
-33	P131[33-33]
-34	P131[34-34]
-35	P131[35-35]
-36	P131[36-36]
-37	P131[37-37]
-38	P131[38-38]
-39	P131[39-39]
-40	P131[40-40]
-41	P131[41-41]
-42	P131[42-42]
-43	P131[43-43]
-44	P131[44-44]
-45	P131[45-45]
-46	P131[46-46]
-47	P131[47-47]
-48	P131[48-48]
-49	P131[49-49]
-50	P131[50-50]
-51	P131[51-51]
-52	P131[52-52]
-53	P131[53-53]
-54	P131[54-54]
-55	P131[55-55]
-56	P131[56-56]
-57	P131[57-57]
-58	P131[58-58]
-59	P131[59-59]
-60	P131[60-60]
-61	P131[61-61]
-62	P131[62-62]
-63	P131[63-63]
-64	P131[64-64]
-65	P131[65-65]
-66	P131[66-66]
-67	P131[67-67]
-68	P131[68-68]
-69	P131[69-69]
-70	P1435[65-65]
-71	P39[49-49]
-72	P39[50-50]
-73	P39[51-51]
-74	P39[52-52]
-75	P39[53-53]
-76	P39[54-54]
-77	P39[55-55]
-78	P39[56-56]
-79	P39[57-57]
-80	P39[58-58]
-81	P39[59-59]
-82	P39[60-60]
-83	P39[61-61]
-84	P39[62-62]
-85	P39[63-63]
-86	P39[64-64]
-87	P39[65-65]
-88	P39[66-66]
-89	P39[67-67]
-90	P39[68-68]
-91	P39[69-69]
-92	P54[40-40]
-93	P54[41-41]
-94	P54[42-42]
-95	P54[43-43]
-96	P54[44-44]
-97	P54[45-45]
-98	P54[46-46]
-99	P54[47-47]
-100	P54[48-48]
-101	P54[49-49]
-102	P54[50-50]
-103	P54[51-51]
-104	P54[52-52]
-105	P54[53-53]
-106	P54[54-54]
-107	P54[55-55]
-108	P54[56-56]
-109	P54[57-57]
-110	P54[58-58]
-111	P54[59-59]
-112	P54[60-60]
-113	P54[61-61]
-114	P54[62-62]
-115	P54[63-63]
-116	P54[64-64]
-117	P54[65-65]
-118	P54[66-66]
-119	P54[67-67]
-120	P54[68-68]
-121	P54[69-69]
-122	P31[0-0]
-123	P31[1-1]
-124	P31[2-2]
-125	P31[3-3]
-126	P31[4-4]
-127	P31[5-5]
-128	P31[6-6]
-129	P31[7-7]
-130	P31[8-8]
-131	P31[9-9]
-132	P31[10-10]
-133	P31[11-11]
-134	P31[12-12]
-135	P31[13-13]
-136	P31[14-14]
-137	P31[15-15]
-138	P31[16-16]
-139	P31[17-17]
-140	P31[18-18]
-141	P31[19-19]
-142	P31[20-20]
-143	P31[21-21]
-144	P31[22-22]
-145	P31[23-23]
-146	P31[24-24]
-147	P31[25-25]
-148	P31[26-26]
-149	P31[27-27]
-150	P31[28-28]
-151	P31[29-29]
-152	P31[30-30]
-153	P31[31-31]
-154	P31[32-32]
-155	P31[33-33]
-156	P31[34-34]
-157	P31[35-35]
-158	P31[36-36]
-159	P31[37-37]
-160	P31[38-38]
-161	P31[39-39]
-162	P31[40-40]
-163	P31[41-41]
-164	P31[42-42]
-165	P31[43-43]
-166	P31[44-44]
-167	P31[45-45]
-168	P31[46-46]
-169	P31[47-47]
-170	P31[48-48]
-171	P31[49-49]
-172	P31[50-50]
-173	P31[51-51]
-174	P31[52-52]
-175	P31[53-53]
-176	P31[54-54]
-177	P31[55-55]
-178	P31[56-56]
-179	P31[57-57]
-180	P31[58-58]
-181	P31[59-59]
-182	P31[60-60]
-183	P31[61-61]
-184	P31[62-62]
-185	P31[63-63]
-186	P31[64-64]
-187	P31[65-65]
-188	P31[66-66]
-189	P31[67-67]
-190	P31[68-68]
-191	P31[69-69]
-192	P463[26-26]
-193	P463[27-27]
-194	P463[28-28]
-195	P463[29-29]
-196	P463[30-30]
-197	P463[31-31]
-198	P463[32-32]
-199	P463[33-33]
-200	P463[34-34]
-201	P463[35-35]
-202	P463[36-36]
-203	P463[37-37]
-204	P463[38-38]
-205	P463[39-39]
-206	P463[40-40]
-207	P463[41-41]
-208	P463[42-42]
-209	P463[43-43]
-210	P463[44-44]
-211	P463[45-45]
-212	P463[46-46]
-213	P463[47-47]
-214	P463[48-48]
-215	P463[49-49]
-216	P463[50-50]
-217	P463[51-51]
-218	P463[52-52]
-219	P463[53-53]
-220	P463[54-54]
-221	P463[55-55]
-222	P463[56-56]
-223	P463[57-57]
-224	P463[58-58]
-225	P463[59-59]
-226	P463[60-60]
-227	P463[61-61]
-228	P463[62-62]
-229	P463[63-63]
-230	P463[64-64]
-231	P463[65-65]
-232	P463[66-66]
-233	P463[67-67]
-234	P463[68-68]
-235	P463[69-69]
-236	P512[4-69]
-237	P190[0-29]
-238	P150[0-3]
-239	P1376[39-47]
-240	P463[0-7]
-241	P166[0-7]
-242	P2962[18-30]
-243	P108[29-36]
-244	P39[0-3]
-245	P17[47-48]
-246	P166[21-23]
-247	P793[46-69]
-248	P69[32-41]
-249	P17[57-58]
-250	P190[42-45]
-251	P2962[39-42]
-252	P54[0-18]
-253	P26[56-61]
-254	P150[14-17]
-255	P463[16-17]
-256	P26[39-46]
-257	P579[36-43]
-258	P579[16-23]
-259	P2962[59-60]
-260	P1411[59-61]
-261	P26[20-27]
-262	P6[4-69]
-263	P1435[33-34]
-264	P166[52-53]
-265	P108[49-57]
-266	P150[10-13]
-267	P1346[47-68]
-268	P150[18-21]
-269	P1346[13-46]
-270	P69[20-23]
-271	P39[31-32]
-272	P1411[32-37]
-273	P166[62-63]
-274	P150[44-47]
-275	P2962[61-62]
-276	P150[48-51]
-277	P150[52-55]
-278	P1411[62-67]
-279	P1435[35-36]
-280	P1411[48-51]
-281	P150[22-25]
-282	P2962[63-64]
-283	P2962[65-66]
-284	P166[58-59]
-285	P190[46-49]
-286	P54[34-35]
-287	P1435[4-16]
-288	P463[18-19]
-289	P150[31-34]
-290	P150[35-38]
-291	P39[35-36]
-292	P26[62-69]
-293	P1411[56-58]
-294	P1435[37-38]
-295	P166[60-61]
-296	P39[33-34]
-297	P102[24-31]
-298	P2962[43-46]
-299	P108[37-48]
-300	P190[50-53]
-301	P39[4-6]
-302	P1435[39-40]
-303	P793[0-45]
-304	P150[64-69]
-305	P39[19-22]
-306	P27[30-38]
-307	P2962[31-38]
-308	P1411[24-31]
-309	P102[40-45]
-310	P39[37-38]
-311	P463[8-11]
-312	P1435[41-42]
-313	P27[52-59]
-314	P69[16-19]
-315	P17[16-18]
-316	P190[54-57]
-317	P1435[43-44]
-318	P166[8-15]
-319	P166[45-47]
-320	P2962[47-50]
-321	P39[39-40]
-322	P1411[52-55]
-323	P108[58-69]
-324	P463[20-21]
-325	P39[41-42]
-326	P150[26-30]
-327	P150[39-43]
-328	P1435[45-46]
-329	P26[28-38]
-330	P54[27-30]
-331	P190[58-61]
-332	P17[59-61]
-333	P54[36-37]
-334	P166[16-20]
-335	P166[37-40]
-336	P1435[47-48]
-337	P17[0-3]
-338	P26[47-55]
-339	P1435[49-50]
-340	P1435[25-28]
-341	P150[4-9]
-342	P102[63-69]
-343	P26[0-19]
-344	P1435[17-24]
-345	P39[23-26]
-346	P1435[51-52]
-347	P39[7-11]
-348	P69[12-15]
-349	P69[24-31]
-350	P102[0-23]
-351	P39[43-44]
-352	P579[24-35]
-353	P190[62-65]
-354	P1435[53-54]
-355	P1376[0-18]
-356	P27[0-14]
-357	P463[12-15]
-358	P166[33-36]
-359	P102[32-39]
-360	P17[4-7]
-361	P190[30-41]
-362	P166[24-28]
-363	P190[66-69]
-364	P69[42-69]
-365	P1435[55-56]
-366	P54[31-33]
-367	P39[45-46]
-368	P17[12-15]
-369	P1435[57-58]
-370	P54[19-26]
-371	P2962[51-54]
-372	P2962[67-69]
-373	P1435[59-60]
-374	P579[44-56]
-375	P1435[61-62]
-376	P166[41-44]
-377	P17[19-22]
-378	P1376[19-38]
-379	P17[23-26]
-380	P1376[48-69]
-381	P463[22-23]
-382	P17[27-30]
-383	P1435[63-64]
-384	P69[0-3]
-385	P1435[66-67]
-386	P17[35-38]
-387	P69[8-11]
-388	P1435[68-69]
-389	P17[31-34]
-390	P102[46-53]
-391	P27[60-69]
-392	P579[57-69]
-393	P69[4-7]
-394	P1411[7-14]
-395	P551[0-35]
-396	P108[0-28]
-397	P17[8-11]
-398	P1411[38-47]
-399	P17[43-46]
-400	P17[49-52]
-401	P166[64-69]
-402	P1435[29-32]
-403	P54[38-39]
-404	P39[27-30]
-405	P2962[55-58]
-406	P463[24-25]
-407	P17[39-42]
-408	P17[53-56]
-409	P17[66-69]
-410	P17[62-65]
-411	P1411[15-23]
-412	P166[48-51]
-413	P27[15-29]
-414	P150[56-63]
-415	P27[39-51]
-416	P39[47-48]
-417	P166[29-32]
-418	P39[12-18]
-419	P166[54-57]
-420	P551[36-69]
-421	P579[0-15]
-422	P102[54-62]
--- a/data/wikidata12k_old/test.txt
+++ b/data/wikidata12k_old/test.txt
--- a/data/wikidata12k_old/time_map.dict
+++ b/data/wikidata12k_old/time_map.dict
@ -1,71 +0,0 @@
-0	19	19
-1	20	1643
-2	1644	1790
-3	1791	1816
-4	1817	1855
-5	1856	1871
-6	1872	1893
-7	1894	1905
-8	1906	1913
-9	1914	1918
-10	1919	1920
-11	1921	1924
-12	1925	1929
-13	1930	1933
-14	1934	1937
-15	1938	1941
-16	1942	1945
-17	1946	1948
-18	1949	1950
-19	1951	1953
-20	1954	1956
-21	1957	1959
-22	1960	1961
-23	1962	1963
-24	1964	1965
-25	1966	1967
-26	1968	1968
-27	1969	1970
-28	1971	1972
-29	1973	1974
-30	1975	1976
-31	1977	1978
-32	1979	1980
-33	1981	1982
-34	1983	1983
-35	1984	1984
-36	1985	1985
-37	1986	1986
-38	1987	1987
-39	1988	1988
-40	1989	1989
-41	1990	1990
-42	1991	1991
-43	1992	1992
-44	1993	1993
-45	1994	1994
-46	1995	1995
-47	1996	1996
-48	1997	1997
-49	1998	1998
-50	1999	1999
-51	2000	2000
-52	2001	2001
-53	2002	2002
-54	2003	2003
-55	2004	2004
-56	2005	2005
-57	2006	2006
-58	2007	2007
-59	2008	2008
-60	2009	2009
-61	2010	2010
-62	2011	2011
-63	2012	2012
-64	2013	2013
-65	2014	2014
-66	2015	2015
-67	2016	2016
-68	2017	2017
-69	2018	2020
-70	2021	2021
--- a/data/wikidata12k_old/train.txt
+++ b/data/wikidata12k_old/train.txt
--- a/data/wikidata12k_old/valid.txt
+++ b/data/wikidata12k_old/valid.txt
--- a/data/yago/about.txt
+++ b/data/yago/about.txt
@ -1,15 +0,0 @@
-# triples: 78032 
-# entities: 10526 
-# relations: 177 
-# timesteps: 46 
-# test triples: 6909 
-# valid triples: 7198 
-# train triples: 63925 
-Measure method:  N/A  
-Target Size :  0  
-Grow Factor:  0  
-Shrink Factor:  0  
-Epsilon Factor: 5.0  
-Search method: N/A  
-filter_dupes: inter
-nonames: False
--- a/data/yago/entities.dict
+++ b/data/yago/entities.dict
--- a/data/yago/relations.dict
+++ b/data/yago/relations.dict
@ -1,177 +0,0 @@
-0	<wasBornIn>[0-2]
-1	<wasBornIn>[2-5]
-2	<wasBornIn>[5-7]
-3	<wasBornIn>[7-10]
-4	<wasBornIn>[10-12]
-5	<wasBornIn>[12-15]
-6	<wasBornIn>[15-17]
-7	<wasBornIn>[17-20]
-8	<wasBornIn>[20-22]
-9	<wasBornIn>[22-25]
-10	<wasBornIn>[25-27]
-11	<wasBornIn>[27-30]
-12	<wasBornIn>[30-32]
-13	<wasBornIn>[32-35]
-14	<wasBornIn>[35-45]
-15	<wasBornIn>[52-52]
-16	<diedIn>[0-3]
-17	<diedIn>[3-5]
-18	<diedIn>[5-7]
-19	<diedIn>[7-10]
-20	<diedIn>[10-12]
-21	<diedIn>[12-14]
-22	<diedIn>[14-17]
-23	<diedIn>[17-19]
-24	<diedIn>[19-21]
-25	<diedIn>[21-23]
-26	<diedIn>[23-25]
-27	<diedIn>[25-27]
-28	<diedIn>[27-29]
-29	<diedIn>[29-32]
-30	<diedIn>[32-34]
-31	<diedIn>[34-36]
-32	<diedIn>[36-38]
-33	<diedIn>[38-40]
-34	<diedIn>[40-42]
-35	<diedIn>[42-44]
-36	<diedIn>[44-47]
-37	<diedIn>[47-49]
-38	<diedIn>[49-51]
-39	<diedIn>[51-53]
-40	<diedIn>[53-55]
-41	<diedIn>[55-57]
-42	<diedIn>[59-59]
-43	<worksAt>[0-3]
-44	<worksAt>[3-5]
-45	<worksAt>[5-7]
-46	<worksAt>[7-10]
-47	<worksAt>[10-12]
-48	<worksAt>[12-14]
-49	<worksAt>[14-17]
-50	<worksAt>[17-19]
-51	<worksAt>[19-21]
-52	<worksAt>[21-23]
-53	<worksAt>[23-25]
-54	<worksAt>[25-27]
-55	<worksAt>[27-29]
-56	<worksAt>[29-32]
-57	<worksAt>[32-34]
-58	<worksAt>[34-36]
-59	<worksAt>[36-40]
-60	<worksAt>[40-42]
-61	<worksAt>[42-47]
-62	<worksAt>[47-53]
-63	<worksAt>[59-59]
-64	<playsFor>[0-3]
-65	<playsFor>[3-5]
-66	<playsFor>[5-23]
-67	<playsFor>[23-25]
-68	<playsFor>[25-27]
-69	<playsFor>[27-29]
-70	<playsFor>[29-32]
-71	<playsFor>[32-34]
-72	<playsFor>[34-36]
-73	<playsFor>[36-38]
-74	<playsFor>[38-40]
-75	<playsFor>[40-42]
-76	<playsFor>[42-44]
-77	<playsFor>[44-47]
-78	<playsFor>[47-51]
-79	<playsFor>[59-59]
-80	<hasWonPrize>[1-4]
-81	<hasWonPrize>[4-6]
-82	<hasWonPrize>[6-8]
-83	<hasWonPrize>[8-11]
-84	<hasWonPrize>[11-15]
-85	<hasWonPrize>[15-18]
-86	<hasWonPrize>[18-22]
-87	<hasWonPrize>[22-26]
-88	<hasWonPrize>[26-30]
-89	<hasWonPrize>[30-33]
-90	<hasWonPrize>[33-37]
-91	<hasWonPrize>[37-47]
-92	<hasWonPrize>[47-53]
-93	<hasWonPrize>[59-59]
-94	<isMarriedTo>[0-3]
-95	<isMarriedTo>[3-5]
-96	<isMarriedTo>[5-7]
-97	<isMarriedTo>[7-10]
-98	<isMarriedTo>[10-12]
-99	<isMarriedTo>[12-14]
-100	<isMarriedTo>[14-17]
-101	<isMarriedTo>[17-19]
-102	<isMarriedTo>[19-21]
-103	<isMarriedTo>[21-23]
-104	<isMarriedTo>[23-25]
-105	<isMarriedTo>[25-27]
-106	<isMarriedTo>[27-29]
-107	<isMarriedTo>[29-32]
-108	<isMarriedTo>[32-34]
-109	<isMarriedTo>[34-38]
-110	<isMarriedTo>[38-42]
-111	<isMarriedTo>[42-47]
-112	<isMarriedTo>[47-51]
-113	<isMarriedTo>[51-55]
-114	<isMarriedTo>[59-59]
-115	<owns>[0-10]
-116	<owns>[10-17]
-117	<owns>[17-19]
-118	<owns>[19-23]
-119	<owns>[23-36]
-120	<owns>[36-38]
-121	<owns>[59-59]
-122	<graduatedFrom>[0-3]
-123	<graduatedFrom>[3-5]
-124	<graduatedFrom>[5-7]
-125	<graduatedFrom>[7-10]
-126	<graduatedFrom>[10-14]
-127	<graduatedFrom>[14-17]
-128	<graduatedFrom>[17-19]
-129	<graduatedFrom>[19-21]
-130	<graduatedFrom>[21-23]
-131	<graduatedFrom>[23-27]
-132	<graduatedFrom>[27-32]
-133	<graduatedFrom>[32-34]
-134	<graduatedFrom>[34-38]
-135	<graduatedFrom>[38-42]
-136	<graduatedFrom>[59-59]
-137	<isAffiliatedTo>[1-4]
-138	<isAffiliatedTo>[4-6]
-139	<isAffiliatedTo>[6-8]
-140	<isAffiliatedTo>[8-11]
-141	<isAffiliatedTo>[11-13]
-142	<isAffiliatedTo>[13-15]
-143	<isAffiliatedTo>[15-18]
-144	<isAffiliatedTo>[18-20]
-145	<isAffiliatedTo>[20-22]
-146	<isAffiliatedTo>[22-24]
-147	<isAffiliatedTo>[24-26]
-148	<isAffiliatedTo>[26-28]
-149	<isAffiliatedTo>[28-30]
-150	<isAffiliatedTo>[30-33]
-151	<isAffiliatedTo>[33-35]
-152	<isAffiliatedTo>[35-37]
-153	<isAffiliatedTo>[37-40]
-154	<isAffiliatedTo>[40-42]
-155	<isAffiliatedTo>[42-44]
-156	<isAffiliatedTo>[44-47]
-157	<isAffiliatedTo>[47-49]
-158	<isAffiliatedTo>[49-51]
-159	<isAffiliatedTo>[51-53]
-160	<isAffiliatedTo>[53-55]
-161	<isAffiliatedTo>[55-57]
-162	<isAffiliatedTo>[59-59]
-163	<created>[0-3]
-164	<created>[3-5]
-165	<created>[5-10]
-166	<created>[10-12]
-167	<created>[12-17]
-168	<created>[17-19]
-169	<created>[19-25]
-170	<created>[25-29]
-171	<created>[29-32]
-172	<created>[32-36]
-173	<created>[36-42]
-174	<created>[42-47]
-175	<created>[47-53]
-176	<created>[59-59]
--- a/data/yago/test.txt
+++ b/data/yago/test.txt
--- a/data/yago/time_map.dict
+++ b/data/yago/time_map.dict
@ -1,60 +0,0 @@
-0	-431	1782
-1	1783	1848
-2	1849	1870
-3	1871	1888
-4	1889	1899
-5	1900	1906
-6	1907	1912
-7	1913	1917
-8	1918	1922
-9	1923	1926
-10	1927	1930
-11	1931	1934
-12	1935	1938
-13	1939	1941
-14	1942	1944
-15	1945	1947
-16	1948	1950
-17	1951	1953
-18	1954	1956
-19	1957	1959
-20	1960	1962
-21	1963	1965
-22	1966	1967
-23	1968	1969
-24	1970	1971
-25	1972	1973
-26	1974	1975
-27	1976	1977
-28	1978	1979
-29	1980	1981
-30	1982	1983
-31	1984	1985
-32	1986	1987
-33	1988	1989
-34	1990	1991
-35	1992	1993
-36	1994	1994
-37	1995	1996
-38	1997	1997
-39	1998	1998
-40	1999	1999
-41	2000	2000
-42	2001	2001
-43	2002	2002
-44	2003	2003
-45	2004	2004
-46	2005	2005
-47	2006	2006
-48	2007	2007
-49	2008	2008
-50	2009	2009
-51	2010	2010
-52	2011	2011
-53	2012	2012
-54	2013	2013
-55	2014	2014
-56	2015	2015
-57	2016	2016
-58	2017	2017
-59	2018	2018
--- a/data/yago/train.txt
+++ b/data/yago/train.txt
--- a/data/yago/valid.txt
+++ b/data/yago/valid.txt
--- a/data/yago11k/indices_test.txt
+++ b/data/yago11k/indices_test.txt
@ -1,793 +0,0 @@
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--- a/data/yago11k/indices_train.txt
+++ b/data/yago11k/indices_train.txt
--- a/data/yago11k/indices_valid.txt
+++ b/data/yago11k/indices_valid.txt
@ -1,809 +0,0 @@
-7
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--- a/icews14_both.log
+++ b/icews14_both.log
@ -0,0 +1,3 @@
+nohup: ignoring input
+2023-06-20 09:22:51,618 - [INFO] - {'dataset': 'icews14_both', 'name': 'icews14_both', 'gpu': '2', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
+2023-06-20 09:22:57,979 - [INFO] - [E:0| 0]: Train Loss:0.70005,  Val MRR:0.0, 	icews14_both
--- a/log/ice00001
+++ b/log/ice00001
--- a/log/ice0003
+++ b/log/ice0003
--- a/log/ice0003_2
+++ b/log/ice0003_2
--- a/log/ice001
+++ b/log/ice001
--- a/log/ice14ws_128
+++ b/log/ice14ws_128
--- a/log/iceboth
+++ b/log/iceboth
--- a/log/icews14
+++ b/log/icews14
--- a/log/icews14_128
+++ b/log/icews14_128
@ -0,0 +1 @@
+2023-05-13 03:52:44,141 - icews14_128 - [INFO] - {'dataset': 'icews14', 'name': 'icews14_128', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': True, 'filtered': False}
--- a/log/icews14_both
+++ b/log/icews14_both
--- a/log/poofnet.log
+++ b/log/poofnet.log
@ -0,0 +1,2 @@
+nohup: ignoring input
+python: can't open file 'run.py': [Errno 2] No such file or directory
--- a/log/testrun_227cb2f9
+++ b/log/testrun_227cb2f9
@ -0,0 +1 @@
+2023-05-17 06:54:57,988 - testrun_227cb2f9 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_227cb2f9', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_30d70322
+++ b/log/testrun_30d70322
@ -0,0 +1 @@
+2023-05-17 06:23:34,181 - testrun_30d70322 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_30d70322', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_3212b281
+++ b/log/testrun_3212b281
@ -0,0 +1 @@
+2023-05-17 06:53:01,668 - testrun_3212b281 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_3212b281', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_3dbc9e89
+++ b/log/testrun_3dbc9e89
@ -0,0 +1 @@
+2023-05-06 08:35:38,753 - testrun_3dbc9e89 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_3dbc9e89', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
--- a/log/testrun_43389ddf
+++ b/log/testrun_43389ddf
@ -0,0 +1 @@
+2023-05-17 06:38:00,469 - testrun_43389ddf - [INFO] - {'dataset': 'icews14', 'name': 'testrun_43389ddf', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_47ede3b9
+++ b/log/testrun_47ede3b9
@ -0,0 +1 @@
+2023-05-17 06:13:02,952 - testrun_47ede3b9 - [INFO] - {'dataset': 'FB15k-237', 'name': 'testrun_47ede3b9', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_49495af8
+++ b/log/testrun_49495af8
@ -0,0 +1 @@
+2023-05-06 08:37:18,939 - testrun_49495af8 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_49495af8', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
--- a/log/testrun_4a235016
+++ b/log/testrun_4a235016
--- a/log/testrun_4f5d8391
+++ b/log/testrun_4f5d8391
@ -0,0 +1 @@
+2023-05-06 08:35:13,356 - testrun_4f5d8391 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_4f5d8391', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
--- a/log/testrun_540f6a03
+++ b/log/testrun_540f6a03
@ -0,0 +1 @@
+2023-05-06 08:34:55,992 - testrun_540f6a03 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_540f6a03', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
--- a/log/testrun_5a901712
+++ b/log/testrun_5a901712
@ -0,0 +1 @@
+2023-05-17 07:04:56,051 - testrun_5a901712 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_5a901712', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_5cafe61a
+++ b/log/testrun_5cafe61a
@ -0,0 +1,44 @@
+2023-05-17 06:48:57,396 - testrun_5cafe61a - [INFO] - {'dataset': 'icews14', 'name': 'testrun_5cafe61a', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
+2023-05-17 06:49:44,802 - concurrent.futures - [ERROR] - exception calling callback for <Future at 0x7efb51b74160 state=finished raised BrokenProcessPool>
+joblib.externals.loky.process_executor._RemoteTraceback: 
+"""
+Traceback (most recent call last):
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py", line 391, in _process_worker
+    call_item = call_queue.get(block=True, timeout=timeout)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/multiprocessing/queues.py", line 116, in get
+    return _ForkingPickler.loads(res)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/storage.py", line 222, in _load_from_bytes
+    return torch.load(io.BytesIO(b))
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 713, in load
+    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 930, in _legacy_load
+    result = unpickler.load()
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 876, in persistent_load
+    wrap_storage=restore_location(obj, location),
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 175, in default_restore_location
+    result = fn(storage, location)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/serialization.py", line 155, in _cuda_deserialize
+    return torch._UntypedStorage(obj.nbytes(), device=torch.device(location))
+RuntimeError: CUDA out of memory. Tried to allocate 678.00 MiB (GPU 0; 31.72 GiB total capacity; 0 bytes already allocated; 593.94 MiB free; 0 bytes reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+"""
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/_base.py", line 26, in _invoke_callbacks
+    callback(self)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 385, in __call__
+    self.parallel.dispatch_next()
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 834, in dispatch_next
+    if not self.dispatch_one_batch(self._original_iterator):
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 901, in dispatch_one_batch
+    self._dispatch(tasks)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/parallel.py", line 819, in _dispatch
+    job = self._backend.apply_async(batch, callback=cb)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 556, in apply_async
+    future = self._workers.submit(SafeFunction(func))
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/reusable_executor.py", line 176, in submit
+    return super().submit(fn, *args, **kwargs)
+  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/joblib/externals/loky/process_executor.py", line 1129, in submit
+    raise self._flags.broken
+joblib.externals.loky.process_executor.BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.
--- a/log/testrun_6fd94d59
+++ b/log/testrun_6fd94d59
@ -0,0 +1 @@
+2023-05-06 08:34:33,652 - testrun_6fd94d59 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_6fd94d59', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
--- a/log/testrun_7c096a18
+++ b/log/testrun_7c096a18
@ -0,0 +1 @@
+2023-05-17 06:56:35,124 - testrun_7c096a18 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_7c096a18', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_7fb885ee
+++ b/log/testrun_7fb885ee
@ -0,0 +1 @@
+2023-05-17 07:13:14,777 - testrun_7fb885ee - [INFO] - {'dataset': 'icews14', 'name': 'testrun_7fb885ee', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_8f32040f
+++ b/log/testrun_8f32040f
@ -0,0 +1 @@
+2023-05-17 06:59:35,220 - testrun_8f32040f - [INFO] - {'dataset': 'icews14', 'name': 'testrun_8f32040f', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_958ef154
+++ b/log/testrun_958ef154
@ -0,0 +1 @@
+2023-05-17 06:16:45,427 - testrun_958ef154 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_958ef154', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_9acdfb58
+++ b/log/testrun_9acdfb58
@ -0,0 +1,2 @@
+2023-05-06 08:36:46,668 - testrun_9acdfb58 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_9acdfb58', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
+2023-05-06 08:36:57,409 - testrun_9acdfb58 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	testrun_9acdfb58
--- a/log/testrun_a051cf32
+++ b/log/testrun_a051cf32
@ -0,0 +1 @@
+2023-05-17 06:36:14,606 - testrun_a051cf32 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_a051cf32', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_a06d39d0
+++ b/log/testrun_a06d39d0
@ -0,0 +1 @@
+2023-05-17 06:13:16,274 - testrun_a06d39d0 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_a06d39d0', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_aca2b734
+++ b/log/testrun_aca2b734
@ -0,0 +1 @@
+2023-05-17 06:41:20,654 - testrun_aca2b734 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_aca2b734', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_ad7a0edb
+++ b/log/testrun_ad7a0edb
@ -0,0 +1 @@
+2023-05-17 06:45:54,332 - testrun_ad7a0edb - [INFO] - {'dataset': 'icews14', 'name': 'testrun_ad7a0edb', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_ae6f81ee
+++ b/log/testrun_ae6f81ee
--- a/log/testrun_b381870f
+++ b/log/testrun_b381870f
@ -0,0 +1 @@
+2023-05-30 17:54:20,857 - testrun_b381870f - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_b381870f', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
--- a/log/testrun_b396dcde
+++ b/log/testrun_b396dcde
@ -0,0 +1,2 @@
+2023-05-30 17:56:25,430 - testrun_b396dcde - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_b396dcde', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
+2023-05-30 17:57:00,673 - testrun_b396dcde - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_b396dcde', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False, 'num_ent': 12554, 'num_rel': 423}
--- a/log/testrun_bbf65ab5
+++ b/log/testrun_bbf65ab5
@ -0,0 +1 @@
+2023-05-17 06:21:14,228 - testrun_bbf65ab5 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_bbf65ab5', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_bfaa042b
+++ b/log/testrun_bfaa042b
@ -0,0 +1 @@
+2023-05-17 06:50:58,251 - testrun_bfaa042b - [INFO] - {'dataset': 'icews14', 'name': 'testrun_bfaa042b', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_c77a8ec3
+++ b/log/testrun_c77a8ec3
@ -0,0 +1 @@
+2023-05-17 06:37:11,288 - testrun_c77a8ec3 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_c77a8ec3', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_cb3528f3
+++ b/log/testrun_cb3528f3
@ -0,0 +1 @@
+2023-05-17 07:08:13,688 - testrun_cb3528f3 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_cb3528f3', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_cd333c33
+++ b/log/testrun_cd333c33
@ -0,0 +1 @@
+2023-05-17 06:25:12,047 - testrun_cd333c33 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_cd333c33', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_d0367b19
+++ b/log/testrun_d0367b19
@ -0,0 +1,2 @@
+2023-05-06 08:37:25,129 - testrun_d0367b19 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_d0367b19', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
+2023-05-06 08:37:36,239 - testrun_d0367b19 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	testrun_d0367b19
--- a/log/testrun_d2ab6391
+++ b/log/testrun_d2ab6391
--- a/log/testrun_e1726b98
+++ b/log/testrun_e1726b98
--- a/log/testrun_f0394b3c
+++ b/log/testrun_f0394b3c
@ -0,0 +1 @@
+2023-05-17 06:47:48,537 - testrun_f0394b3c - [INFO] - {'dataset': 'icews14', 'name': 'testrun_f0394b3c', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/testrun_f42f568c
+++ b/log/testrun_f42f568c
@ -0,0 +1 @@
+2023-05-30 17:55:52,461 - testrun_f42f568c - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_f42f568c', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0003, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
--- a/log/testrun_fdb0e82c
+++ b/log/testrun_fdb0e82c
@ -0,0 +1 @@
+2023-05-17 06:39:01,301 - testrun_fdb0e82c - [INFO] - {'dataset': 'icews14', 'name': 'testrun_fdb0e82c', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
--- a/log/wikidata12k
+++ b/log/wikidata12k
--- a/log/wikidata12k_0.00003
+++ b/log/wikidata12k_0.00003
@ -0,0 +1,2 @@
+2023-06-04 17:05:45,012 - wikidata12k_0.00003 - [INFO] - {'dataset': 'wikidata12k', 'name': 'wikidata12k_0.00003', 'gpu': '2', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': False}
+2023-06-04 17:06:06,702 - wikidata12k_0.00003 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	wikidata12k_0.00003
--- a/log/wikidata12k_0.001
+++ b/log/wikidata12k_0.001
--- a/log/wikidata12k_1n
+++ b/log/wikidata12k_1n
--- a/log/wikidata12k_both
+++ b/log/wikidata12k_both
--- a/log/yago11k
+++ b/log/yago11k
--- a/log/yago11k_0.00003
+++ b/log/yago11k_0.00003
--- a/log/yago11k_0.0003
+++ b/log/yago11k_0.0003
--- a/log/yago11k_0.001
+++ b/log/yago11k_0.001
--- a/log/yago11k_0.001.log
+++ b/log/yago11k_0.001.log
--- a/log/yago11k_both
+++ b/log/yago11k_both
--- a/log/yago11k_both_0.001
+++ b/log/yago11k_both_0.001
--- a/main.py
+++ b/main.py
@ -3,10 +3,12 @@ import uuid
 import argparse
 import logging
 import logging.config
-import time
+import pandas as pd
+import sys

 import torch
 import numpy as np
+import time

 from collections import defaultdict as ddict
 from pprint import pprint
@ -18,11 +20,12 @@ from data_loader import TrainDataset, TestDataset
 from utils import get_logger, get_combined_results, set_gpu, prepare_env, set_seed

 from models import ComplEx, ConvE, HypER, InteractE, FouriER, TuckER
+import traceback


 class Main(object):

-    def __init__(self, params):
+    def __init__(self, params, logger):
        """
        Constructor of the runner class
        Parameters
@ -35,11 +38,9 @@ class Main(object):

        """
        self.p = params
-        self.logger = get_logger(
-            self.p.name, self.p.log_dir, self.p.config_dir)
+        self.logger = logger

        self.logger.info(vars(self.p))
-        pprint(vars(self.p))

        if self.p.gpu != '-1' and torch.cuda.is_available():
            self.device = torch.device('cuda')
@ -84,15 +85,17 @@ class Main(object):
        
        self.ent2id = {}
        for line in open('./data/{}/{}'.format(self.p.dataset, "entities.dict")):
-            id, ent = map(str.lower, line.strip().split('\t'))
+            id, ent = map(str.lower, line.replace('\xa0', '').strip().split('\t'))
            self.ent2id[ent] = int(id)
        self.rel2id = {}
        for line in open('./data/{}/{}'.format(self.p.dataset, "relations.dict")):
            id, rel = map(str.lower, line.strip().split('\t'))
            self.rel2id[rel] = int(id)
+            rel_set.add(rel)

        # self.ent2id = {ent: idx for idx, ent in enumerate(ent_set)}
        # self.rel2id = {rel: idx for idx, rel in enumerate(rel_set)}
+        
        self.rel2id.update({rel+'_reverse': idx+len(self.rel2id)
                           for idx, rel in enumerate(rel_set)})

@ -108,59 +111,52 @@ class Main(object):
        sr2o = ddict(set)

        for split in ['train', 'test', 'valid']:
-            samples = 0
-            for i, line in enumerate(open('./data/{}/{}.txt'.format(self.p.dataset, split))):
-                sub, rel, obj, rel_type, *_ = map(str.lower, line.strip().split('\t'))
-                if (split == 'test' and self.p.rel_type is not None):
-                    if rel_type != self.p.rel_type:
-                        continue
-                sub, rel, obj = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj]
-                self.data[split].append((sub, rel, obj))
+            for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)):
+                sub, rel, obj, *_ = map(str.lower, line.replace('\xa0', '').strip().split('\t'))
+                nt_rel = rel.split('[')[0]
+                sub, rel, obj, nt_rel = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj], self.rel2id[nt_rel]
+                self.data[split].append((sub, rel, obj, nt_rel))

                if split == 'train':
-                    sr2o[(sub, rel)].add(obj)
-                    sr2o[(obj, rel+self.p.num_rel)].add(sub)
-                samples += 1
-            print(split.capitalize() + ': ' + str(samples) + ' samples')
+                    sr2o[(sub, rel, nt_rel)].add(obj)
+                    sr2o[(obj, rel+self.p.num_rel, nt_rel + self.p.num_rel)].add(sub)
        self.data = dict(self.data)

        self.sr2o = {k: list(v) for k, v in sr2o.items()}
        for split in ['test', 'valid']:
-            for sub, rel, obj in self.data[split]:
-                sr2o[(sub, rel)].add(obj)
-                sr2o[(obj, rel+self.p.num_rel)].add(sub)
+            for sub, rel, obj, nt_rel in self.data[split]:
+                sr2o[(sub, rel, nt_rel)].add(obj)
+                sr2o[(obj, rel+self.p.num_rel, nt_rel + self.p.num_rel)].add(sub)

        self.sr2o_all = {k: list(v) for k, v in sr2o.items()}

        self.triples = ddict(list)

        if self.p.train_strategy == 'one_to_n':
-            for (sub, rel), obj in self.sr2o.items():
+            for (sub, rel, nt_rel), obj in self.sr2o.items():
                self.triples['train'].append(
-                    {'triple': (sub, rel, -1), 'label': self.sr2o[(sub, rel)], 'sub_samp': 1})
+                    {'triple': (sub, rel, -1, nt_rel), 'label': self.sr2o[(sub, rel, nt_rel)], 'sub_samp': 1})
        else:
-            for sub, rel, obj in self.data['train']:
+            for sub, rel, obj, nt_rel in self.data['train']:
                rel_inv = rel + self.p.num_rel
-                sub_samp = len(self.sr2o[(sub, rel)]) + \
-                    len(self.sr2o[(obj, rel_inv)])
+                sub_samp = len(self.sr2o[(sub, rel, nt_rel)]) + \
+                    len(self.sr2o[(obj, rel_inv, nt_rel + self.p.num_rel)])
                sub_samp = np.sqrt(1/sub_samp)

                self.triples['train'].append({'triple': (
-                    sub, rel, obj),     'label': self.sr2o[(sub, rel)],     'sub_samp': sub_samp})
+                    sub, rel, obj, nt_rel),     'label': self.sr2o[(sub, rel, nt_rel)],     'sub_samp': sub_samp})
                self.triples['train'].append({'triple': (
-                    obj, rel_inv, sub), 'label': self.sr2o[(obj, rel_inv)], 'sub_samp': sub_samp})
+                    obj, rel_inv, sub, nt_rel + self.p.num_rel), 'label': self.sr2o[(obj, rel_inv, nt_rel + self.p.num_rel)], 'sub_samp': sub_samp})

        for split in ['test', 'valid']:
-            for sub, rel, obj in self.data[split]:
+            for sub, rel, obj, nt_rel in self.data[split]:
                rel_inv = rel + self.p.num_rel
                self.triples['{}_{}'.format(split, 'tail')].append(
-                    {'triple': (sub, rel, obj), 	   'label': self.sr2o_all[(sub, rel)]})
+                    {'triple': (sub, rel, obj, nt_rel), 	   'label': self.sr2o_all[(sub, rel, nt_rel)]})
                self.triples['{}_{}'.format(split, 'head')].append(
-                    {'triple': (obj, rel_inv, sub), 'label': self.sr2o_all[(obj, rel_inv)]})
+                    {'triple': (obj, rel_inv, sub, nt_rel + self.p.num_rel), 'label': self.sr2o_all[(obj, rel_inv, nt_rel + self.p.num_rel)]})

        self.triples = dict(self.triples)
-        print(len(self.triples['test_head']))
-        print(len(self.triples['test_tail']))

        def get_data_loader(dataset_class, split, batch_size, shuffle=True):
            return DataLoader(
@ -282,13 +278,13 @@ class Main(object):
            if self.p.train_strategy == 'one_to_x':
                triple, label, neg_ent, sub_samp = [
                    _.to(self.device) for _ in batch]
-                return triple[:, 0], triple[:, 1], triple[:, 2], label, neg_ent, sub_samp
+                return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, neg_ent, sub_samp
            else:
                triple, label = [_.to(self.device) for _ in batch]
-                return triple[:, 0], triple[:, 1], triple[:, 2], label, None, None
+                return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label, None, None
        else:
            triple, label = [_.to(self.device) for _ in batch]
-            return triple[:, 0], triple[:, 1], triple[:, 2], label
+            return triple[:, 0], triple[:, 1], triple[:, 2], triple[:, 3], label

    def save_model(self, save_path):
        """
@ -415,16 +411,35 @@ class Main(object):
            train_iter = iter(
                self.data_iter['{}_{}'.format(split, mode.split('_')[0])])

+            sub_all = []
+            obj_all = []
+            rel_all = []
+            target_score = []
+            target_rank = []
+            obj_pred = []
+            obj_pred_score = []
            for step, batch in enumerate(train_iter):
-                sub, rel, obj, label = self.read_batch(batch, split)
-                pred = self.model.forward(sub, rel, None, 'one_to_n')
+                sub, rel, obj, nt_rel, label = self.read_batch(batch, split)
+                pred = self.model.forward(sub, rel, nt_rel, None, 'one_to_n')
                b_range = torch.arange(pred.size()[0], device=self.device)
                target_pred = pred[b_range, obj]
                pred = torch.where(label.byte(), torch.zeros_like(pred), pred)
                pred[b_range, obj] = target_pred
+
+                highest = torch.argsort(pred, dim=1, descending=True)[:,0]
+                highest_score = pred[b_range, highest]
+
                ranks = 1 + torch.argsort(torch.argsort(pred, dim=1,
                                          descending=True), dim=1, descending=False)[b_range, obj]

+                sub_all.extend(sub.cpu().numpy())
+                obj_all.extend(obj.cpu().numpy())
+                rel_all.extend(rel.cpu().numpy())
+                target_score.extend(target_pred.cpu().numpy())
+                target_rank.extend(ranks.cpu().numpy())
+                obj_pred.extend(highest.cpu().numpy())
+                obj_pred_score.extend(highest_score.cpu().numpy())
+
                ranks = ranks.float()
                results['count'] = torch.numel(
                    ranks) + results.get('count', 0.0)
@ -439,7 +454,8 @@ class Main(object):
                if step % 100 == 0:
                    self.logger.info('[{}, {} Step {}]\t{}'.format(
                        split.title(), mode.title(), step, self.p.name))
-
+        df = pd.DataFrame({"sub":sub_all,"rel":rel_all,"obj":obj_all, "rank": target_rank,"score":target_score, "pred":obj_pred,"pred_score":obj_pred_score})
+        df.to_csv(f"{self.p.name}_result.csv",header=True, index=False)
        return results

    def run_epoch(self, epoch):
@ -461,10 +477,10 @@ class Main(object):
        for step, batch in enumerate(train_iter):
            self.optimizer.zero_grad()

-            sub, rel, obj, label, neg_ent, sub_samp = self.read_batch(
+            sub, rel, obj, nt_rel, label, neg_ent, sub_samp = self.read_batch(
                batch, 'train')

-            pred = self.model.forward(sub, rel, neg_ent, self.p.train_strategy)
+            pred = self.model.forward(sub, rel, nt_rel, neg_ent, self.p.train_strategy)
            loss = self.model.loss(pred, label, sub_samp)

            loss.backward()
@ -635,7 +651,6 @@ if __name__ == "__main__":
    
    parser.add_argument('--test_only', action='store_true', default=False)
    parser.add_argument('--grid_search', action='store_true', default=False)
-    parser.add_argument('--rel_type', default=None, type=str)

    args = parser.parse_args()

@ -644,9 +659,10 @@ if __name__ == "__main__":
    set_gpu(args.gpu)
    set_seed(args.seed)

-    model = Main(args)

    if (args.grid_search):
+        
+        model = Main(args)
        from sklearn.model_selection import GridSearchCV
        from skorch import NeuralNet

@ -677,7 +693,7 @@ if __name__ == "__main__":
                collate_fn=TrainDataset.collate_fn
            ))
        for step, batch in enumerate(dataloader):
-            sub, rel, obj, label, neg_ent, sub_samp = model.read_batch(
+            sub, rel, obj, nt_rel, label, neg_ent, sub_samp = model.read_batch(
                batch, 'train')
            
            if (neg_ent is None):
@ -695,18 +711,27 @@ if __name__ == "__main__":
            search = grid.fit(inputs, label)
            print("BEST SCORE: ", search.best_score_)
            print("BEST PARAMS: ", search.best_params_)
+    logger = get_logger(
+            args.name, args.log_dir, args.config_dir)
    if (args.test_only):
+        model = Main(args, logger)
        save_path = os.path.join('./torch_saved', args.name)
        model.load_model(save_path)
        model.evaluate('test')
    else:
-        while True:
-            try:
-                model.fit()
-            except Exception as e:
-                print(e)
-                time.sleep(30)
-                del model
-                model = Main(args)
-                continue
-            break
+        model = Main(args, logger)
+        model.fit()
+        # while True:
+        #     try:
+        #         model = Main(args, logger)
+        #         model.fit()
+        #     except Exception as e:
+        #         print(e)
+        #         traceback.print_exc()
+        #         try:
+        #             del model
+        #         except Exception:
+        #             pass
+        #         time.sleep(30)
+        #         continue
+        #     break
--- a/models.py
+++ b/models.py
@ -9,7 +9,7 @@ from layers import *
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.models.layers import DropPath, trunc_normal_
 from timm.models.registry import register_model
-from timm.models.layers.helpers import to_2tuple
+from timm.layers.helpers import to_2tuple


 class ConvE(torch.nn.Module):
@ -466,6 +466,10 @@ class FouriER(torch.nn.Module):
            self.p.ent_vec_dim, image_h*image_w)
        torch.nn.init.xavier_normal_(self.ent_fusion.weight)

+        self.ent_attn = torch.nn.Linear(
+            128, 128)
+        torch.nn.init.xavier_normal_(self.ent_attn.weight)
+
        self.rel_fusion = torch.nn.Linear(
            self.p.rel_vec_dim, image_h*image_w)
        torch.nn.init.xavier_normal_(self.rel_fusion.weight)
@ -547,8 +551,15 @@ class FouriER(torch.nn.Module):
            x = block(x)
        # output only the features of last layer for image classification
        return x
+    
+    def fuse_attention(self, s_embedding, l_embedding):
+        w1 = self.ent_attn(torch.tanh(s_embedding))
+        w2 = self.ent_attn(torch.tanh(l_embedding))
+        aff = F.softmax(torch.cat((w1,w2),1), 1)
+        en_embedding = aff[:,0].unsqueeze(1) * s_embedding + aff[:, 1].unsqueeze(1) * l_embedding
+        return en_embedding

-    def forward(self, sub, rel, neg_ents, strategy='one_to_x'):
+    def forward(self, sub, rel, nt_rel, neg_ents, strategy='one_to_x'):
        sub_emb = self.ent_fusion(self.ent_embed(sub))
        rel_emb = self.rel_fusion(self.rel_embed(rel))
        comb_emb = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
@ -557,6 +568,17 @@ class FouriER(torch.nn.Module):
        z = self.forward_embeddings(y)
        z = self.forward_tokens(z)
        z = z.mean([-2, -1])
+
+        nt_rel_emb = self.rel_fusion(self.rel_embed(nt_rel))
+        comb_emb_1 = torch.stack([sub_emb.view(-1, self.p.image_h, self.p.image_w), nt_rel_emb.view(-1, self.p.image_h, self.p.image_w)], dim=1)
+        y_1 = comb_emb_1.view(-1, 2, self.p.image_h, self.p.image_w)
+        y_1 = self.bn0(y_1)
+        z_1 = self.forward_embeddings(y_1)
+        z_1 = self.forward_tokens(z_1)
+        z_1 = z_1.mean([-2, -1])
+
+        z = self.fuse_attention(z, z_1)
+
        z = self.norm(z)
        x = self.head(z)
        x = self.hidden_drop(x)
@ -707,6 +729,166 @@ def basic_blocks(dim, index, layers,

    return blocks

+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, C, H, W = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+                 pretrained_window_size=[0, 0]):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.pretrained_window_size = pretrained_window_size
+        self.num_heads = num_heads
+
+        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+        # mlp to generate continuous relative position bias
+        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+                                     nn.ReLU(inplace=True),
+                                     nn.Linear(512, num_heads, bias=False))
+
+        # get relative_coords_table
+        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+        relative_coords_table = torch.stack(
+            torch.meshgrid([relative_coords_h,
+                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
+        if pretrained_window_size[0] > 0:
+            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+        else:
+            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+        relative_coords_table *= 8  # normalize to -8, 8
+        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+        self.register_buffer("relative_coords_table", relative_coords_table)
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(dim))
+            self.v_bias = nn.Parameter(torch.zeros(dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        # cosine attention
+        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).cuda()).exp()
+        attn = attn * logit_scale
+
+        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, ' \
+               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+    
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, -1, H, W)
+    return x

 class PoolFormerBlock(nn.Module):
    """
@ -731,7 +913,10 @@ class PoolFormerBlock(nn.Module):

        self.norm1 = norm_layer(dim)
        #self.token_mixer = Pooling(pool_size=pool_size)
-        self.token_mixer = FNetBlock()
+        # self.token_mixer = FNetBlock()
+        self.window_size = 4
+        self.attn_mask = None
+        self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(self.window_size), num_heads=4)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, 
@ -748,15 +933,21 @@ class PoolFormerBlock(nn.Module):
                layer_scale_init_value * torch.ones((dim)), requires_grad=True)

    def forward(self, x):
+        B, C, H, W = x.shape
+        x_windows = window_partition(x, self.window_size)
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
+        attn_windows = self.token_mixer(x_windows, mask=self.attn_mask)
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        x_attn = window_reverse(attn_windows, self.window_size, H, W)
        if self.use_layer_scale:
            x = x + self.drop_path(
                self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)
-                * self.token_mixer(self.norm1(x)))
+                * x_attn)
            x = x + self.drop_path(
                self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
                * self.mlp(self.norm2(x)))
        else:
-            x = x + self.drop_path(self.token_mixer(self.norm1(x)))
+            x = x + self.drop_path(x_attn)
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x
 class PatchEmbed(nn.Module):
--- a/requirements.txt
+++ b/requirements.txt
@ -1,4 +1,6 @@
 torch==1.12.1+cu116
 ordered-set==4.1.0
 numpy==1.21.5
-einops==0.4.1
+einops==0.4.1
+pandas
+timm==0.9.16
--- a/run.sh
+++ b/run.sh
@ -37,4 +37,7 @@ nohup python main.py --name ice00001 --lr 0.00001 --data icews14 --gpu 2 >run_lo
 PID:

 ___
-nohup python main.py --name ice001 --lr 0.001 --data icews14 --gpu 3 >run_log/0.001.log 2>&1 &
+nohup python main.py --name ice001 --lr 0.001 --data icews14 --gpu 3 >run_log/0.001.log 2>&1 &
+___
+nohup python main.py --name iceboth --data icews14_both --gpu 0 >run_log/iceboth.log 2>&1 &
+PID: 21984
--- a/run_log/icews14/0.00001.log
+++ b/run_log/icews14/0.00001.log
--- a/run_log/icews14/0.00003.log
+++ b/run_log/icews14/0.00003.log
--- a/run_log/icews14/0.0001.out
+++ b/run_log/icews14/0.0001.out
--- a/run_log/icews14/0.0003.log
+++ b/run_log/icews14/0.0003.log
--- a/run_log/icews14/0.001.log
+++ b/run_log/icews14/0.001.log
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
thanhvc3	3f0018fedc	add rel	2024-06-22 20:42:41 +07:00
thanhvc3	9502c8d009	test	2024-06-19 00:24:20 +07:00
thanhvc3	2637f53848	test	2024-06-19 00:20:44 +07:00
thanhvc3	975a0a77c2	test	2024-06-19 00:19:31 +07:00
thanhvc3	a064d12763	test	2024-06-19 00:16:05 +07:00
thanhvc3	6d43b88599	test	2024-06-19 00:12:43 +07:00
thanhvc3	7448528eec	test	2024-06-19 00:11:37 +07:00
thanhvc3	7194f8046c	test	2024-06-19 00:10:51 +07:00
thanhvc3	417a38d2e5	test	2024-06-19 00:08:27 +07:00
thanhvc3	03f42561c6	test	2024-06-19 00:05:57 +07:00
thanhvc3	936c37d0f6	test	2024-06-19 00:03:14 +07:00
thanhvc3	39734013c4	test	2024-06-18 23:46:20 +07:00
thanhvc3	bb9856ecd1	test	2024-06-18 21:11:18 +07:00
Cong Thanh Vu	c2b17ec1ba	try to add attn	2024-06-16 19:09:47 +07:00
thanhvc3	f8e969cbd1	try swin	2024-04-27 11:52:23 +07:00
thanhvc3	ae0f43ab4d	try swin	2024-04-27 11:51:35 +07:00
thanhvc3	dda7f13dbd	try swin	2024-04-27 11:49:07 +07:00
thanhvc3	1dd423edf0	try swin	2024-04-27 11:48:25 +07:00
thanhvc3	a1bf2d7389	try swin	2024-04-27 11:46:32 +07:00
thanhvc3	c31588cc5f	try swin	2024-04-27 11:45:24 +07:00
thanhvc3	c03e24f4c2	try swin	2024-04-27 11:43:15 +07:00
thanhvc3	a47a60f6a1	try swin	2024-04-27 11:40:27 +07:00
thanhvc3	ba388148d4	try swin	2024-04-27 11:27:38 +07:00
thanhvc3	1b816fed50	try swin	2024-04-27 11:24:57 +07:00
thanhvc3	32962bf421	try swin	2024-04-27 11:23:28 +07:00
thanhvc3	b9efe68d3c	try swin	2024-04-27 11:12:52 +07:00
thanhvc3	465f98bef8	try swin	2024-04-27 11:08:46 +07:00
thanhvc3	d4ac470c54	try swin	2024-04-27 11:07:48 +07:00
thanhvc3	28a8352044	try swin	2024-04-27 10:59:11 +07:00
thanhvc3	b77c79708e	try swin	2024-04-27 10:56:10 +07:00
thanhvc3	22d44d1a99	try swin	2024-04-27 10:32:08 +07:00
thanhvc3	63ccb4ec75	try swin	2024-04-27 10:26:58 +07:00
thanhvc3	6ec566505f	try swin	2024-04-27 10:18:48 +07:00
thanhvc3	30805a0af9	try swin	2024-04-27 10:04:41 +07:00
Cong Thanh Vu	2e2b12571a	update viz util	2023-06-24 04:11:17 +00:00
Cong Thanh Vu	d4b29eec2c	auto retry	2023-06-08 06:40:16 +00:00
				`@ -0,0 +1 @@`
				2023-05-13 03:52:44,141 - icews14_128 - [INFO] - {'dataset': 'icews14', 'name': 'icews14_128', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': True, 'filtered': False}
				`@ -0,0 +1 @@`
				2023-05-17 06:54:57,988 - testrun_227cb2f9 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_227cb2f9', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
				`@ -0,0 +1 @@`
				2023-05-17 06:23:34,181 - testrun_30d70322 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_30d70322', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
				`@ -0,0 +1 @@`
				2023-05-17 06:53:01,668 - testrun_3212b281 - [INFO] - {'dataset': 'icews14', 'name': 'testrun_3212b281', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
				`@ -0,0 +1 @@`
				2023-05-06 08:35:38,753 - testrun_3dbc9e89 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_3dbc9e89', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
				`@ -0,0 +1 @@`
				2023-05-17 06:38:00,469 - testrun_43389ddf - [INFO] - {'dataset': 'icews14', 'name': 'testrun_43389ddf', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
				`@ -0,0 +1 @@`
				2023-05-17 06:13:02,952 - testrun_47ede3b9 - [INFO] - {'dataset': 'FB15k-237', 'name': 'testrun_47ede3b9', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False, 'grid_search': True}
				`@ -0,0 +1 @@`
				2023-05-06 08:37:18,939 - testrun_49495af8 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_49495af8', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
				`@ -0,0 +1 @@`
				2023-05-06 08:35:13,356 - testrun_4f5d8391 - [INFO] - {'dataset': 'wikidata12k', 'name': 'testrun_4f5d8391', 'gpu': '0', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.0, 'drop': 0.0, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}