add rel

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

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

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

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

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]

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

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

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]

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

data/yago11k/indices_test.txt

@@ -1,793 +0,0 @@
-0
-2
-4
-9
-11
-12
-16
-17
-19
-27
-29
-34
-35
-37
-38
-41
-42
-45
-49
-51
-52
-54
-56
-57
-61
-64
-65
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-76
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-79
-83
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-867
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-899
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-2032
-2036
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-2044
-2045
-2046
-2048

data/yago11k/indices_valid.txt

@@ -1,809 +0,0 @@
-7
-9
-12
-15
-17
-22
-24
-25
-28
-29
-32
-37
-38
-41
-43
-49
-52
-54
-57
-58
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-60
-66
-69
-72
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-251
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-255
-256
-257
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-265
-266
-271
-273
-274
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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

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}

log/poofnet.log

@@ -0,0 +1,2 @@
+nohup: ignoring input
+python: can't open file 'run.py': [Errno 2] No such file or directory

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}

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}

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}

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}

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}

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}

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}

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}

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}

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}

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.

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}

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}

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}

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}

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}

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

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}

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}

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}

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}

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}

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}

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}

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}

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}

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}

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}

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

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}

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}

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}

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

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 = Main(args, logger)
         model.fit()
-            except Exception as e:
-                print(e)
-                time.sleep(30)
-                del model
-                model = Main(args)
-                continue
-            break
+        # 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

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)
@@ -548,7 +552,14 @@ class FouriER(torch.nn.Module):
         # output only the features of last layer for image classification
         return x
     
-    def forward(self, sub, rel, neg_ents, strategy='one_to_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, 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):

requirements.txt

@@ -2,3 +2,5 @@ torch==1.12.1+cu116
 ordered-set==4.1.0
 numpy==1.21.5
 einops==0.4.1
+pandas
+timm==0.9.16

run.sh

@@ -38,3 +38,6 @@ 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 iceboth --data icews14_both --gpu 0 >run_log/iceboth.log 2>&1 &
+PID: 21984

run_log/wikidata12k/0.001.log

@@ -1,425 +0,0 @@
-nohup: ignoring input
-2023-05-27 04:41:18,497 - [INFO] - {'dataset': 'wikidata12k', 'name': 'wikidata12k_0.001', 'gpu': '3', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.001, '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}
-{'batch_size': 128,
- 'bias': False,
- 'config_dir': './config/',
- 'dataset': 'wikidata12k',
- 'drop': 0.0,
- 'drop_path': 0.0,
- 'embed_dim': 400,
- 'ent_vec_dim': 400,
- 'expansion_factor': 4,
- 'expansion_factor_token': 0.5,
- 'feat_drop': 0.2,
- 'filt_h': 1,
- 'filt_w': 9,
- 'form': 'plain',
- 'gpu': '3',
- 'grid_search': False,
- 'hid_drop': 0.5,
- 'image_h': 128,
- 'image_w': 128,
- 'in_channels': 1,
- 'inp_drop': 0.2,
- 'k_h': 20,
- 'k_w': 10,
- 'ker_sz': 9,
- 'l2': 0.0,
- 'lbl_smooth': 0.1,
- 'log_dir': './log/',
- 'lr': 0.001,
- 'max_epochs': 500,
- 'mixer_depth': 16,
- 'mixer_dim': 256,
- 'mixer_dropout': 0.2,
- 'name': 'wikidata12k_0.001',
- 'neg_num': 1000,
- 'num_filt': 96,
- 'num_workers': 0,
- 'opt': 'adam',
- 'out_channels': 32,
- 'patch_size': 8,
- 'perm': 1,
- 'rel_vec_dim': 400,
- 'restore': False,
- 'seed': 42,
- 'test_only': False,
- 'train_strategy': 'one_to_n'}
-2023-05-27 04:41:28,635 - [INFO] - [E:0| 0]: Train Loss:0.69813,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:42:32,570 - [INFO] - [E:0| 100]: Train Loss:0.053587,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:43:36,618 - [INFO] - [E:0| 200]: Train Loss:0.028724,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:44:40,687 - [INFO] - [E:0| 300]: Train Loss:0.020033,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:45:44,799 - [INFO] - [E:0| 400]: Train Loss:0.015589,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:46:48,901 - [INFO] - [E:0| 500]: Train Loss:0.012878,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:47:53,124 - [INFO] - [E:0| 600]: Train Loss:0.011054,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:48:57,224 - [INFO] - [E:0| 700]: Train Loss:0.0097532,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:50:01,352 - [INFO] - [E:0| 800]: Train Loss:0.008763,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:51:05,445 - [INFO] - [E:0| 900]: Train Loss:0.0079929,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:52:09,559 - [INFO] - [E:0| 1000]: Train Loss:0.0073745,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:53:13,624 - [INFO] - [E:0| 1100]: Train Loss:0.0068693,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:54:17,823 - [INFO] - [E:0| 1200]: Train Loss:0.0064497,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:55:21,967 - [INFO] - [E:0| 1300]: Train Loss:0.0060945,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:56:26,129 - [INFO] - [E:0| 1400]: Train Loss:0.0057879,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:57:30,256 - [INFO] - [E:0| 1500]: Train Loss:0.0055195,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:58:34,350 - [INFO] - [E:0| 1600]: Train Loss:0.0052845,  Val MRR:0.0, 	wikidata12k_0.001
-2023-05-27 04:59:16,259 - [INFO] - [Epoch:0]:  Training Loss:0.005147
-
-2023-05-27 04:59:16,481 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 04:59:38,187 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 04:59:50,745 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 05:00:12,609 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 05:00:25,062 - [INFO] - [Evaluating Epoch 0 valid]: 
-	MRR: Tail : 0.08049, Head : 0.01947, Avg : 0.04998
-
-2023-05-27 05:00:26,469 - [INFO] - [Epoch 0]:  Training Loss: 0.0051469,  Valid MRR: 0.04998, 
-
-
-
-2023-05-27 05:00:27,127 - [INFO] - [E:1| 0]: Train Loss:0.0016275,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:01:31,277 - [INFO] - [E:1| 100]: Train Loss:0.0017991,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:02:35,390 - [INFO] - [E:1| 200]: Train Loss:0.0017846,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:03:39,590 - [INFO] - [E:1| 300]: Train Loss:0.0017789,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:04:43,748 - [INFO] - [E:1| 400]: Train Loss:0.001772,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:05:47,967 - [INFO] - [E:1| 500]: Train Loss:0.0017692,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:06:52,036 - [INFO] - [E:1| 600]: Train Loss:0.0017597,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:07:56,215 - [INFO] - [E:1| 700]: Train Loss:0.0017589,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:09:00,363 - [INFO] - [E:1| 800]: Train Loss:0.0017555,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:10:04,516 - [INFO] - [E:1| 900]: Train Loss:0.0017507,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:11:08,719 - [INFO] - [E:1| 1000]: Train Loss:0.0017476,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:12:12,940 - [INFO] - [E:1| 1100]: Train Loss:0.0017427,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:13:17,076 - [INFO] - [E:1| 1200]: Train Loss:0.0017384,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:14:21,295 - [INFO] - [E:1| 1300]: Train Loss:0.0017345,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:15:25,462 - [INFO] - [E:1| 1400]: Train Loss:0.0017307,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:16:29,614 - [INFO] - [E:1| 1500]: Train Loss:0.0017243,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:17:33,705 - [INFO] - [E:1| 1600]: Train Loss:0.001719,  Val MRR:0.04998, 	wikidata12k_0.001
-2023-05-27 05:18:15,618 - [INFO] - [Epoch:1]:  Training Loss:0.001714
-
-2023-05-27 05:18:15,839 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 05:18:37,583 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 05:18:50,191 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 05:19:12,067 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 05:19:24,503 - [INFO] - [Evaluating Epoch 1 valid]: 
-	MRR: Tail : 0.1748, Head : 0.04108, Avg : 0.10794
-
-2023-05-27 05:19:25,566 - [INFO] - [Epoch 1]:  Training Loss: 0.0017143,  Valid MRR: 0.10794, 
-
-
-
-2023-05-27 05:19:26,219 - [INFO] - [E:2| 0]: Train Loss:0.0016961,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:20:30,344 - [INFO] - [E:2| 100]: Train Loss:0.0016227,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:21:34,535 - [INFO] - [E:2| 200]: Train Loss:0.0016161,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:22:38,770 - [INFO] - [E:2| 300]: Train Loss:0.0016161,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:23:43,004 - [INFO] - [E:2| 400]: Train Loss:0.0016106,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:24:47,137 - [INFO] - [E:2| 500]: Train Loss:0.0016058,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:25:51,362 - [INFO] - [E:2| 600]: Train Loss:0.0016067,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:26:55,499 - [INFO] - [E:2| 700]: Train Loss:0.0016013,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:27:59,761 - [INFO] - [E:2| 800]: Train Loss:0.0015978,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:29:03,935 - [INFO] - [E:2| 900]: Train Loss:0.0015935,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:30:08,210 - [INFO] - [E:2| 1000]: Train Loss:0.0015896,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:31:12,398 - [INFO] - [E:2| 1100]: Train Loss:0.0015856,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:32:16,608 - [INFO] - [E:2| 1200]: Train Loss:0.0015814,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:33:20,836 - [INFO] - [E:2| 1300]: Train Loss:0.0015758,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:34:25,014 - [INFO] - [E:2| 1400]: Train Loss:0.001571,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:35:29,265 - [INFO] - [E:2| 1500]: Train Loss:0.001565,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:36:33,450 - [INFO] - [E:2| 1600]: Train Loss:0.0015589,  Val MRR:0.10794, 	wikidata12k_0.001
-2023-05-27 05:37:15,383 - [INFO] - [Epoch:2]:  Training Loss:0.001556
-
-2023-05-27 05:37:15,603 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 05:37:37,308 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 05:37:49,874 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 05:38:11,738 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 05:38:24,173 - [INFO] - [Evaluating Epoch 2 valid]: 
-	MRR: Tail : 0.28305, Head : 0.07818, Avg : 0.18062
-
-2023-05-27 05:38:25,157 - [INFO] - [Epoch 2]:  Training Loss: 0.001556,  Valid MRR: 0.18062, 
-
-
-
-2023-05-27 05:38:25,813 - [INFO] - [E:3| 0]: Train Loss:0.0013897,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:39:30,024 - [INFO] - [E:3| 100]: Train Loss:0.0014599,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:40:34,122 - [INFO] - [E:3| 200]: Train Loss:0.0014516,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:41:38,261 - [INFO] - [E:3| 300]: Train Loss:0.0014552,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:42:42,459 - [INFO] - [E:3| 400]: Train Loss:0.0014541,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:43:46,707 - [INFO] - [E:3| 500]: Train Loss:0.0014521,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:44:50,829 - [INFO] - [E:3| 600]: Train Loss:0.0014476,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:45:54,979 - [INFO] - [E:3| 700]: Train Loss:0.0014439,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:46:59,115 - [INFO] - [E:3| 800]: Train Loss:0.0014396,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:48:03,341 - [INFO] - [E:3| 900]: Train Loss:0.0014367,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:49:07,419 - [INFO] - [E:3| 1000]: Train Loss:0.0014329,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:50:11,647 - [INFO] - [E:3| 1100]: Train Loss:0.0014308,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:51:15,783 - [INFO] - [E:3| 1200]: Train Loss:0.0014276,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:52:19,915 - [INFO] - [E:3| 1300]: Train Loss:0.0014245,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:53:24,121 - [INFO] - [E:3| 1400]: Train Loss:0.0014212,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:54:28,236 - [INFO] - [E:3| 1500]: Train Loss:0.0014184,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:55:32,482 - [INFO] - [E:3| 1600]: Train Loss:0.0014147,  Val MRR:0.18062, 	wikidata12k_0.001
-2023-05-27 05:56:14,438 - [INFO] - [Epoch:3]:  Training Loss:0.001413
-
-2023-05-27 05:56:14,658 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 05:56:36,372 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 05:56:48,954 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 05:57:10,881 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 05:57:23,328 - [INFO] - [Evaluating Epoch 3 valid]: 
-	MRR: Tail : 0.31549, Head : 0.09979, Avg : 0.20764
-
-2023-05-27 05:57:24,420 - [INFO] - [Epoch 3]:  Training Loss: 0.001413,  Valid MRR: 0.20764, 
-
-
-
-2023-05-27 05:57:25,077 - [INFO] - [E:4| 0]: Train Loss:0.0014323,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 05:58:29,238 - [INFO] - [E:4| 100]: Train Loss:0.0013524,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 05:59:33,410 - [INFO] - [E:4| 200]: Train Loss:0.0013439,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:00:37,566 - [INFO] - [E:4| 300]: Train Loss:0.0013507,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:01:41,692 - [INFO] - [E:4| 400]: Train Loss:0.0013525,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:02:45,877 - [INFO] - [E:4| 500]: Train Loss:0.0013497,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:03:50,088 - [INFO] - [E:4| 600]: Train Loss:0.0013468,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:04:54,238 - [INFO] - [E:4| 700]: Train Loss:0.0013447,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:05:58,490 - [INFO] - [E:4| 800]: Train Loss:0.0013417,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:07:02,645 - [INFO] - [E:4| 900]: Train Loss:0.001339,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:08:06,755 - [INFO] - [E:4| 1000]: Train Loss:0.0013377,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:09:10,902 - [INFO] - [E:4| 1100]: Train Loss:0.0013348,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:10:15,038 - [INFO] - [E:4| 1200]: Train Loss:0.0013326,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:11:19,143 - [INFO] - [E:4| 1300]: Train Loss:0.0013302,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:12:23,347 - [INFO] - [E:4| 1400]: Train Loss:0.0013283,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:13:27,477 - [INFO] - [E:4| 1500]: Train Loss:0.0013269,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:14:31,542 - [INFO] - [E:4| 1600]: Train Loss:0.0013247,  Val MRR:0.20764, 	wikidata12k_0.001
-2023-05-27 06:15:13,457 - [INFO] - [Epoch:4]:  Training Loss:0.001323
-
-2023-05-27 06:15:13,677 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 06:15:35,362 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 06:15:47,916 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 06:16:09,784 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 06:16:22,221 - [INFO] - [Evaluating Epoch 4 valid]: 
-	MRR: Tail : 0.36022, Head : 0.1037, Avg : 0.23196
-
-2023-05-27 06:16:23,220 - [INFO] - [Epoch 4]:  Training Loss: 0.0013235,  Valid MRR: 0.23196, 
-
-
-
-2023-05-27 06:16:23,875 - [INFO] - [E:5| 0]: Train Loss:0.0013387,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:17:28,154 - [INFO] - [E:5| 100]: Train Loss:0.0012781,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:18:32,286 - [INFO] - [E:5| 200]: Train Loss:0.0012786,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:19:36,495 - [INFO] - [E:5| 300]: Train Loss:0.0012809,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:20:40,588 - [INFO] - [E:5| 400]: Train Loss:0.0012857,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:21:44,792 - [INFO] - [E:5| 500]: Train Loss:0.0012853,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:22:49,006 - [INFO] - [E:5| 600]: Train Loss:0.0012833,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:23:53,190 - [INFO] - [E:5| 700]: Train Loss:0.0012812,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:24:57,311 - [INFO] - [E:5| 800]: Train Loss:0.0012813,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:26:01,510 - [INFO] - [E:5| 900]: Train Loss:0.0012801,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:27:05,756 - [INFO] - [E:5| 1000]: Train Loss:0.0012789,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:28:09,936 - [INFO] - [E:5| 1100]: Train Loss:0.0012769,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:29:14,145 - [INFO] - [E:5| 1200]: Train Loss:0.0012746,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:30:18,293 - [INFO] - [E:5| 1300]: Train Loss:0.0012721,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:31:22,538 - [INFO] - [E:5| 1400]: Train Loss:0.0012703,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:32:26,694 - [INFO] - [E:5| 1500]: Train Loss:0.0012689,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:33:30,913 - [INFO] - [E:5| 1600]: Train Loss:0.0012677,  Val MRR:0.23196, 	wikidata12k_0.001
-2023-05-27 06:34:12,771 - [INFO] - [Epoch:5]:  Training Loss:0.001267
-
-2023-05-27 06:34:12,992 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 06:34:34,725 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 06:34:47,309 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 06:35:09,233 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 06:35:21,676 - [INFO] - [Evaluating Epoch 5 valid]: 
-	MRR: Tail : 0.39017, Head : 0.12832, Avg : 0.25924
-
-2023-05-27 06:35:22,811 - [INFO] - [Epoch 5]:  Training Loss: 0.0012668,  Valid MRR: 0.25924, 
-
-
-
-2023-05-27 06:35:23,469 - [INFO] - [E:6| 0]: Train Loss:0.0011894,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:36:27,594 - [INFO] - [E:6| 100]: Train Loss:0.0012342,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:37:31,786 - [INFO] - [E:6| 200]: Train Loss:0.0012378,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:38:35,956 - [INFO] - [E:6| 300]: Train Loss:0.0012388,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:39:40,226 - [INFO] - [E:6| 400]: Train Loss:0.0012378,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:40:44,423 - [INFO] - [E:6| 500]: Train Loss:0.0012438,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:41:48,645 - [INFO] - [E:6| 600]: Train Loss:0.0012421,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:42:52,773 - [INFO] - [E:6| 700]: Train Loss:0.0012408,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:43:56,948 - [INFO] - [E:6| 800]: Train Loss:0.0012416,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:45:01,063 - [INFO] - [E:6| 900]: Train Loss:0.001242,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:46:05,216 - [INFO] - [E:6| 1000]: Train Loss:0.0012397,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:47:09,350 - [INFO] - [E:6| 1100]: Train Loss:0.0012386,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:48:13,445 - [INFO] - [E:6| 1200]: Train Loss:0.0012373,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:49:17,622 - [INFO] - [E:6| 1300]: Train Loss:0.0012363,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:50:21,832 - [INFO] - [E:6| 1400]: Train Loss:0.0012346,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:51:26,056 - [INFO] - [E:6| 1500]: Train Loss:0.0012342,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:52:30,214 - [INFO] - [E:6| 1600]: Train Loss:0.001233,  Val MRR:0.25924, 	wikidata12k_0.001
-2023-05-27 06:53:12,160 - [INFO] - [Epoch:6]:  Training Loss:0.001232
-
-2023-05-27 06:53:12,380 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 06:53:34,088 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 06:53:46,650 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 06:54:08,519 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 06:54:20,952 - [INFO] - [Evaluating Epoch 6 valid]: 
-	MRR: Tail : 0.37877, Head : 0.18554, Avg : 0.28215
-
-2023-05-27 06:54:22,025 - [INFO] - [Epoch 6]:  Training Loss: 0.0012324,  Valid MRR: 0.28215, 
-
-
-
-2023-05-27 06:54:22,682 - [INFO] - [E:7| 0]: Train Loss:0.0011315,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 06:55:26,826 - [INFO] - [E:7| 100]: Train Loss:0.001205,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 06:56:30,996 - [INFO] - [E:7| 200]: Train Loss:0.0012037,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 06:57:35,173 - [INFO] - [E:7| 300]: Train Loss:0.0012034,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 06:58:39,365 - [INFO] - [E:7| 400]: Train Loss:0.0012073,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 06:59:43,659 - [INFO] - [E:7| 500]: Train Loss:0.0012094,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:00:47,839 - [INFO] - [E:7| 600]: Train Loss:0.0012093,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:01:51,994 - [INFO] - [E:7| 700]: Train Loss:0.0012077,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:02:56,159 - [INFO] - [E:7| 800]: Train Loss:0.0012085,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:04:00,272 - [INFO] - [E:7| 900]: Train Loss:0.0012086,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:05:04,432 - [INFO] - [E:7| 1000]: Train Loss:0.0012104,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:06:08,565 - [INFO] - [E:7| 1100]: Train Loss:0.00121,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:07:12,766 - [INFO] - [E:7| 1200]: Train Loss:0.0012097,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:08:16,920 - [INFO] - [E:7| 1300]: Train Loss:0.0012101,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:09:21,081 - [INFO] - [E:7| 1400]: Train Loss:0.0012095,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:10:25,247 - [INFO] - [E:7| 1500]: Train Loss:0.0012082,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:11:29,494 - [INFO] - [E:7| 1600]: Train Loss:0.0012075,  Val MRR:0.28215, 	wikidata12k_0.001
-2023-05-27 07:12:11,381 - [INFO] - [Epoch:7]:  Training Loss:0.001208
-
-2023-05-27 07:12:11,602 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 07:12:33,359 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 07:12:45,946 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 07:13:07,852 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 07:13:20,334 - [INFO] - [Evaluating Epoch 7 valid]: 
-	MRR: Tail : 0.40626, Head : 0.21375, Avg : 0.31001
-
-2023-05-27 07:13:21,326 - [INFO] - [Epoch 7]:  Training Loss: 0.0012077,  Valid MRR: 0.31001, 
-
-
-
-2023-05-27 07:13:21,980 - [INFO] - [E:8| 0]: Train Loss:0.0012363,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:14:26,096 - [INFO] - [E:8| 100]: Train Loss:0.0011868,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:15:30,354 - [INFO] - [E:8| 200]: Train Loss:0.0011847,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:16:34,466 - [INFO] - [E:8| 300]: Train Loss:0.0011814,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:17:38,565 - [INFO] - [E:8| 400]: Train Loss:0.0011847,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:18:42,799 - [INFO] - [E:8| 500]: Train Loss:0.0011887,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:19:46,964 - [INFO] - [E:8| 600]: Train Loss:0.0011901,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:20:51,144 - [INFO] - [E:8| 700]: Train Loss:0.0011897,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:21:55,282 - [INFO] - [E:8| 800]: Train Loss:0.0011913,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:22:59,411 - [INFO] - [E:8| 900]: Train Loss:0.0011918,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:24:03,538 - [INFO] - [E:8| 1000]: Train Loss:0.0011908,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:25:07,761 - [INFO] - [E:8| 1100]: Train Loss:0.0011915,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:26:11,872 - [INFO] - [E:8| 1200]: Train Loss:0.0011925,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:27:16,041 - [INFO] - [E:8| 1300]: Train Loss:0.0011918,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:28:20,210 - [INFO] - [E:8| 1400]: Train Loss:0.0011905,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:29:24,336 - [INFO] - [E:8| 1500]: Train Loss:0.0011898,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:30:28,566 - [INFO] - [E:8| 1600]: Train Loss:0.0011888,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:31:10,538 - [INFO] - [Epoch:8]:  Training Loss:0.001189
-
-2023-05-27 07:31:10,758 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 07:31:32,478 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 07:31:45,038 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 07:32:06,913 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 07:32:19,354 - [INFO] - [Evaluating Epoch 8 valid]: 
-	MRR: Tail : 0.41408, Head : 0.20141, Avg : 0.30774
-
-2023-05-27 07:32:19,354 - [INFO] - [Epoch 8]:  Training Loss: 0.0011888,  Valid MRR: 0.31001, 
-
-
-
-2023-05-27 07:32:20,011 - [INFO] - [E:9| 0]: Train Loss:0.0011748,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:33:24,159 - [INFO] - [E:9| 100]: Train Loss:0.0011746,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:34:28,351 - [INFO] - [E:9| 200]: Train Loss:0.0011787,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:35:32,472 - [INFO] - [E:9| 300]: Train Loss:0.0011761,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:36:36,656 - [INFO] - [E:9| 400]: Train Loss:0.0011729,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:37:40,796 - [INFO] - [E:9| 500]: Train Loss:0.0011725,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:38:44,981 - [INFO] - [E:9| 600]: Train Loss:0.0011741,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:39:49,133 - [INFO] - [E:9| 700]: Train Loss:0.001173,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:40:53,329 - [INFO] - [E:9| 800]: Train Loss:0.0011736,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:41:57,558 - [INFO] - [E:9| 900]: Train Loss:0.0011731,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:43:01,737 - [INFO] - [E:9| 1000]: Train Loss:0.0011729,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:44:05,854 - [INFO] - [E:9| 1100]: Train Loss:0.001173,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:45:10,080 - [INFO] - [E:9| 1200]: Train Loss:0.0011727,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:46:14,191 - [INFO] - [E:9| 1300]: Train Loss:0.0011718,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:47:18,385 - [INFO] - [E:9| 1400]: Train Loss:0.001171,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:48:22,543 - [INFO] - [E:9| 1500]: Train Loss:0.0011709,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:49:26,748 - [INFO] - [E:9| 1600]: Train Loss:0.0011712,  Val MRR:0.31001, 	wikidata12k_0.001
-2023-05-27 07:50:08,734 - [INFO] - [Epoch:9]:  Training Loss:0.001171
-
-2023-05-27 07:50:08,954 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 07:50:30,672 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 07:50:43,251 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 07:51:05,138 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 07:51:17,628 - [INFO] - [Evaluating Epoch 9 valid]: 
-	MRR: Tail : 0.42849, Head : 0.23814, Avg : 0.33331
-	MR: Tail : 655.47, Head : 840.42, Avg : 747.94
-	Hit-1: Tail : 0.35832, Head : 0.15504, Avg : 0.25668
-	Hit-3: Tail : 0.45838, Head : 0.2739, Avg : 0.36614
-	Hit-10: Tail : 0.55785, Head : 0.39074, Avg : 0.47429
-2023-05-27 07:51:18,545 - [INFO] - [Epoch 9]:  Training Loss: 0.0011709,  Valid MRR: 0.33331, 
-
-
-
-2023-05-27 07:51:19,204 - [INFO] - [E:10| 0]: Train Loss:0.00113,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:52:23,358 - [INFO] - [E:10| 100]: Train Loss:0.0011531,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:53:27,523 - [INFO] - [E:10| 200]: Train Loss:0.0011557,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:54:31,758 - [INFO] - [E:10| 300]: Train Loss:0.0011545,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:55:36,004 - [INFO] - [E:10| 400]: Train Loss:0.0011554,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:56:40,140 - [INFO] - [E:10| 500]: Train Loss:0.001154,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:57:44,301 - [INFO] - [E:10| 600]: Train Loss:0.0011525,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:58:48,517 - [INFO] - [E:10| 700]: Train Loss:0.0011538,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 07:59:52,698 - [INFO] - [E:10| 800]: Train Loss:0.0011536,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:00:56,912 - [INFO] - [E:10| 900]: Train Loss:0.0011541,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:02:01,143 - [INFO] - [E:10| 1000]: Train Loss:0.0011546,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:03:05,293 - [INFO] - [E:10| 1100]: Train Loss:0.0011542,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:04:09,471 - [INFO] - [E:10| 1200]: Train Loss:0.0011539,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:05:13,701 - [INFO] - [E:10| 1300]: Train Loss:0.0011531,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:06:17,887 - [INFO] - [E:10| 1400]: Train Loss:0.0011534,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:07:22,089 - [INFO] - [E:10| 1500]: Train Loss:0.0011546,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:08:26,239 - [INFO] - [E:10| 1600]: Train Loss:0.0011552,  Val MRR:0.33331, 	wikidata12k_0.001
-2023-05-27 08:09:08,153 - [INFO] - [Epoch:10]:  Training Loss:0.001156
-
-2023-05-27 08:09:08,373 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 08:09:30,456 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 08:09:43,084 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 08:10:05,005 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 08:10:17,448 - [INFO] - [Evaluating Epoch 10 valid]: 
-	MRR: Tail : 0.45191, Head : 0.21626, Avg : 0.33409
-
-2023-05-27 08:10:18,436 - [INFO] - [Epoch 10]:  Training Loss: 0.0011556,  Valid MRR: 0.33409, 
-
-
-
-2023-05-27 08:10:19,090 - [INFO] - [E:11| 0]: Train Loss:0.0011363,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:11:23,530 - [INFO] - [E:11| 100]: Train Loss:0.0011426,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:12:27,950 - [INFO] - [E:11| 200]: Train Loss:0.0011483,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:13:32,143 - [INFO] - [E:11| 300]: Train Loss:0.0011472,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:14:36,469 - [INFO] - [E:11| 400]: Train Loss:0.0011477,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:15:40,641 - [INFO] - [E:11| 500]: Train Loss:0.0011474,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:16:44,731 - [INFO] - [E:11| 600]: Train Loss:0.0011465,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:17:48,900 - [INFO] - [E:11| 700]: Train Loss:0.0011469,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:18:53,113 - [INFO] - [E:11| 800]: Train Loss:0.0011469,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:19:57,285 - [INFO] - [E:11| 900]: Train Loss:0.0011457,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:21:01,406 - [INFO] - [E:11| 1000]: Train Loss:0.0011445,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:22:05,596 - [INFO] - [E:11| 1100]: Train Loss:0.0011434,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:23:09,693 - [INFO] - [E:11| 1200]: Train Loss:0.0011431,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:24:13,830 - [INFO] - [E:11| 1300]: Train Loss:0.001143,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:25:18,076 - [INFO] - [E:11| 1400]: Train Loss:0.0011426,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:26:22,160 - [INFO] - [E:11| 1500]: Train Loss:0.0011422,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:27:26,373 - [INFO] - [E:11| 1600]: Train Loss:0.0011418,  Val MRR:0.33409, 	wikidata12k_0.001
-2023-05-27 08:28:08,368 - [INFO] - [Epoch:11]:  Training Loss:0.001142
-
-2023-05-27 08:28:08,589 - [INFO] - [Valid, Tail_Batch Step 0]	wikidata12k_0.001
-2023-05-27 08:28:30,301 - [INFO] - [Valid, Tail_Batch Step 100]	wikidata12k_0.001
-2023-05-27 08:28:42,888 - [INFO] - [Valid, Head_Batch Step 0]	wikidata12k_0.001
-2023-05-27 08:29:04,760 - [INFO] - [Valid, Head_Batch Step 100]	wikidata12k_0.001
-2023-05-27 08:29:17,200 - [INFO] - [Evaluating Epoch 11 valid]: 
-	MRR: Tail : 0.4433, Head : 0.23916, Avg : 0.34123
-
-2023-05-27 08:29:18,266 - [INFO] - [Epoch 11]:  Training Loss: 0.0011416,  Valid MRR: 0.34123, 
-
-
-
-2023-05-27 08:29:18,927 - [INFO] - [E:12| 0]: Train Loss:0.0010957,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:30:23,063 - [INFO] - [E:12| 100]: Train Loss:0.0011303,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:31:27,243 - [INFO] - [E:12| 200]: Train Loss:0.001132,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:32:31,360 - [INFO] - [E:12| 300]: Train Loss:0.0011321,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:33:35,484 - [INFO] - [E:12| 400]: Train Loss:0.0011313,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:34:39,656 - [INFO] - [E:12| 500]: Train Loss:0.0011302,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:35:43,783 - [INFO] - [E:12| 600]: Train Loss:0.0011318,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:36:47,900 - [INFO] - [E:12| 700]: Train Loss:0.0011316,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:37:52,082 - [INFO] - [E:12| 800]: Train Loss:0.0011323,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:38:56,174 - [INFO] - [E:12| 900]: Train Loss:0.001132,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:40:00,316 - [INFO] - [E:12| 1000]: Train Loss:0.0011317,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:41:04,530 - [INFO] - [E:12| 1100]: Train Loss:0.0011322,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:42:08,648 - [INFO] - [E:12| 1200]: Train Loss:0.0011318,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:43:12,819 - [INFO] - [E:12| 1300]: Train Loss:0.0011314,  Val MRR:0.34123, 	wikidata12k_0.001
-2023-05-27 08:44:18,052 - [INFO] - [E:12| 1400]: Train Loss:0.0011312,  Val MRR:0.34123, 	wikidata12k_0.001

visualization_util.py

@@ -10,15 +10,36 @@ def extract_learning_curves(args):
     if len(paths) == 1 and os.path.isdir(paths[0]):
         paths = [os.path.join(paths[0], f) for f in os.listdir(paths[0]) if os.path.isfile(os.path.join(paths[0], f))]
     learning_curves = {}
+    print(paths)
     for path in paths:
+        print(path)
         learning_curve = []
         lines = open(path, 'r').readlines()
+        last_epoch = -1
+        stacked_epoch = -1
+        max_epoch = -1
         for line in lines:
             matched = re.match(r'[0-9\- :,]*\[INFO\] - \[Epoch ([0-9]+)\].*Valid MRR: ([0-9\.]+).*', line)
+            # matched = re.match(r'\tMRR: Tail : [0-9\.]+, Head : [0-9\.]+, Avg : ([0-9\.]+)', line)
             if matched:
+                this_epoch = int(matched.group(1))
+                if (this_epoch > max_epoch):
                     learning_curve.append(float(matched.group(2)))
-                if int(matched.group(1)) >= args.num_epochs:
+                    max_epoch = this_epoch
+                    stacked_epoch = this_epoch
+                elif (this_epoch < max_epoch and this_epoch > last_epoch):
+                    last_epoch = this_epoch
+                    max_epoch = stacked_epoch + 1 + this_epoch
+                    learning_curve.append(float(matched.group(2)))
+                if max_epoch >= args.num_epochs:
                     break
+            # if matched:
+            #     max_epoch += 1
+            #     learning_curve.append(float(matched.group(1)))
+            #     if max_epoch >= args.num_epochs:
+            #         break
+        while len(learning_curve) < args.num_epochs:
+            learning_curve.append(learning_curve[-1])
         learning_curves[os.path.basename(path)] = learning_curve
     return learning_curves
 
@@ -32,7 +53,7 @@ def draw_learning_curves(args, learning_curves):
             label = name
         plt.plot(epochs, learning_curves[name], label = label)
     plt.xlabel("Epochs")
-    plt.ylabel("MRR")
+    plt.ylabel("Best Valid MRR")
     plt.legend(title=args.legend_title)
     plt.savefig(os.path.join(args.out_path, str(round(datetime.utcnow().timestamp() * 1000)) + '.' + args.fig_filetype))
 

wikidata12k_at.out

@@ -1,75 +0,0 @@
-nohup: ignoring input
-2023-05-27 08:51:48,116 - [INFO] - {'dataset': 'wikidata12k', 'name': 'wikidata12k_at', '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, 'grid_search': False}
-{'batch_size': 128,
- 'bias': False,
- 'config_dir': './config/',
- 'dataset': 'wikidata12k',
- 'drop': 0.0,
- 'drop_path': 0.0,
- 'embed_dim': 400,
- 'ent_vec_dim': 400,
- 'expansion_factor': 4,
- 'expansion_factor_token': 0.5,
- 'feat_drop': 0.2,
- 'filt_h': 1,
- 'filt_w': 9,
- 'form': 'plain',
- 'gpu': '3',
- 'grid_search': False,
- 'hid_drop': 0.5,
- 'image_h': 128,
- 'image_w': 128,
- 'in_channels': 1,
- 'inp_drop': 0.2,
- 'k_h': 20,
- 'k_w': 10,
- 'ker_sz': 9,
- 'l2': 0.0,
- 'lbl_smooth': 0.1,
- 'log_dir': './log/',
- 'lr': 0.0001,
- 'max_epochs': 500,
- 'mixer_depth': 16,
- 'mixer_dim': 256,
- 'mixer_dropout': 0.2,
- 'name': 'wikidata12k_at',
- 'neg_num': 1000,
- 'num_filt': 96,
- 'num_workers': 0,
- 'opt': 'adam',
- 'out_channels': 32,
- 'patch_size': 8,
- 'perm': 1,
- 'rel_vec_dim': 400,
- 'restore': False,
- 'seed': 42,
- 'test_only': False,
- 'train_strategy': 'one_to_n'}
-Traceback (most recent call last):
-  File "main.py", line 693, in <module>
-    model.fit()
-  File "main.py", line 492, in fit
-    train_loss = self.run_epoch(epoch)
-  File "main.py", line 458, in run_epoch
-    pred = self.model.forward(sub, rel, neg_ent, self.p.train_strategy)
-  File "/root/kg_374/Thesis_split/models.py", line 558, in forward
-    z = self.forward_tokens(z)
-  File "/root/kg_374/Thesis_split/models.py", line 547, in forward_tokens
-    x = block(x)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
-    return forward_call(*input, **kwargs)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/nn/modules/container.py", line 139, in forward
-    input = module(input)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
-    return forward_call(*input, **kwargs)
-  File "/root/kg_374/Thesis_split/models.py", line 757, in forward
-    * self.mlp(self.norm2(x)))
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
-    return forward_call(*input, **kwargs)
-  File "/root/kg_374/Thesis_split/models.py", line 821, in forward
-    x = self.act(x)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
-    return forward_call(*input, **kwargs)
-  File "/opt/conda/envs/kgs2s/lib/python3.8/site-packages/torch/nn/modules/activation.py", line 681, in forward
-    return F.gelu(input, approximate=self.approximate)
-RuntimeError: CUDA out of memory. Tried to allocate 800.00 MiB (GPU 0; 31.72 GiB total capacity; 10.92 GiB already allocated; 669.94 MiB free; 10.98 GiB 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