try modify swin

data/wikidata12k/about.txt

@@ -1,15 +1,15 @@
-# triples: 291818 
+# triples: 291818 
-# entities: 12554 
+# entities: 12554 
-# relations: 423 
+# relations: 423 
-# timesteps: 70 
+# timesteps: 70 
-# test triples: 19271 
+# test triples: 19271 
-# valid triples: 20208 
+# valid triples: 20208 
-# train triples: 252339 
+# train triples: 252339 
-Measure method:  N/A  
+Measure method:  N/A  
-Target Size :  423  
+Target Size :  423  
-Grow Factor:  0  
+Grow Factor:  0  
-Shrink Factor:  4.0  
+Shrink Factor:  4.0  
-Epsilon Factor: 0  
+Epsilon Factor: 0  
-Search method: N/A  
+Search method: N/A  
-filter_dupes: inter
+filter_dupes: inter
-nonames: False
+nonames: False

data/wikidata12k/relations.dict

@@ -1,423 +1,423 @@
-0	P131[0-0]
+0	P131[0-0]
-1	P131[1-1]
+1	P131[1-1]
-2	P131[2-2]
+2	P131[2-2]
-3	P131[3-3]
+3	P131[3-3]
-4	P131[4-4]
+4	P131[4-4]
-5	P131[5-5]
+5	P131[5-5]
-6	P131[6-6]
+6	P131[6-6]
-7	P131[7-7]
+7	P131[7-7]
-8	P131[8-8]
+8	P131[8-8]
-9	P131[9-9]
+9	P131[9-9]
-10	P131[10-10]
+10	P131[10-10]
-11	P131[11-11]
+11	P131[11-11]
-12	P131[12-12]
+12	P131[12-12]
-13	P131[13-13]
+13	P131[13-13]
-14	P131[14-14]
+14	P131[14-14]
-15	P131[15-15]
+15	P131[15-15]
-16	P131[16-16]
+16	P131[16-16]
-17	P131[17-17]
+17	P131[17-17]
-18	P131[18-18]
+18	P131[18-18]
-19	P131[19-19]
+19	P131[19-19]
-20	P131[20-20]
+20	P131[20-20]
-21	P131[21-21]
+21	P131[21-21]
-22	P131[22-22]
+22	P131[22-22]
-23	P131[23-23]
+23	P131[23-23]
-24	P131[24-24]
+24	P131[24-24]
-25	P131[25-25]
+25	P131[25-25]
-26	P131[26-26]
+26	P131[26-26]
-27	P131[27-27]
+27	P131[27-27]
-28	P131[28-28]
+28	P131[28-28]
-29	P131[29-29]
+29	P131[29-29]
-30	P131[30-30]
+30	P131[30-30]
-31	P131[31-31]
+31	P131[31-31]
-32	P131[32-32]
+32	P131[32-32]
-33	P131[33-33]
+33	P131[33-33]
-34	P131[34-34]
+34	P131[34-34]
-35	P131[35-35]
+35	P131[35-35]
-36	P131[36-36]
+36	P131[36-36]
-37	P131[37-37]
+37	P131[37-37]
-38	P131[38-38]
+38	P131[38-38]
-39	P131[39-39]
+39	P131[39-39]
-40	P131[40-40]
+40	P131[40-40]
-41	P131[41-41]
+41	P131[41-41]
-42	P131[42-42]
+42	P131[42-42]
-43	P131[43-43]
+43	P131[43-43]
-44	P131[44-44]
+44	P131[44-44]
-45	P131[45-45]
+45	P131[45-45]
-46	P131[46-46]
+46	P131[46-46]
-47	P131[47-47]
+47	P131[47-47]
-48	P131[48-48]
+48	P131[48-48]
-49	P131[49-49]
+49	P131[49-49]
-50	P131[50-50]
+50	P131[50-50]
-51	P131[51-51]
+51	P131[51-51]
-52	P131[52-52]
+52	P131[52-52]
-53	P131[53-53]
+53	P131[53-53]
-54	P131[54-54]
+54	P131[54-54]
-55	P131[55-55]
+55	P131[55-55]
-56	P131[56-56]
+56	P131[56-56]
-57	P131[57-57]
+57	P131[57-57]
-58	P131[58-58]
+58	P131[58-58]
-59	P131[59-59]
+59	P131[59-59]
-60	P131[60-60]
+60	P131[60-60]
-61	P131[61-61]
+61	P131[61-61]
-62	P131[62-62]
+62	P131[62-62]
-63	P131[63-63]
+63	P131[63-63]
-64	P131[64-64]
+64	P131[64-64]
-65	P131[65-65]
+65	P131[65-65]
-66	P131[66-66]
+66	P131[66-66]
-67	P131[67-67]
+67	P131[67-67]
-68	P131[68-68]
+68	P131[68-68]
-69	P131[69-69]
+69	P131[69-69]
-70	P1435[65-65]
+70	P1435[65-65]
-71	P39[49-49]
+71	P39[49-49]
-72	P39[50-50]
+72	P39[50-50]
-73	P39[51-51]
+73	P39[51-51]
-74	P39[52-52]
+74	P39[52-52]
-75	P39[53-53]
+75	P39[53-53]
-76	P39[54-54]
+76	P39[54-54]
-77	P39[55-55]
+77	P39[55-55]
-78	P39[56-56]
+78	P39[56-56]
-79	P39[57-57]
+79	P39[57-57]
-80	P39[58-58]
+80	P39[58-58]
-81	P39[59-59]
+81	P39[59-59]
-82	P39[60-60]
+82	P39[60-60]
-83	P39[61-61]
+83	P39[61-61]
-84	P39[62-62]
+84	P39[62-62]
-85	P39[63-63]
+85	P39[63-63]
-86	P39[64-64]
+86	P39[64-64]
-87	P39[65-65]
+87	P39[65-65]
-88	P39[66-66]
+88	P39[66-66]
-89	P39[67-67]
+89	P39[67-67]
-90	P39[68-68]
+90	P39[68-68]
-91	P39[69-69]
+91	P39[69-69]
-92	P54[40-40]
+92	P54[40-40]
-93	P54[41-41]
+93	P54[41-41]
-94	P54[42-42]
+94	P54[42-42]
-95	P54[43-43]
+95	P54[43-43]
-96	P54[44-44]
+96	P54[44-44]
-97	P54[45-45]
+97	P54[45-45]
-98	P54[46-46]
+98	P54[46-46]
-99	P54[47-47]
+99	P54[47-47]
-100	P54[48-48]
+100	P54[48-48]
-101	P54[49-49]
+101	P54[49-49]
-102	P54[50-50]
+102	P54[50-50]
-103	P54[51-51]
+103	P54[51-51]
-104	P54[52-52]
+104	P54[52-52]
-105	P54[53-53]
+105	P54[53-53]
-106	P54[54-54]
+106	P54[54-54]
-107	P54[55-55]
+107	P54[55-55]
-108	P54[56-56]
+108	P54[56-56]
-109	P54[57-57]
+109	P54[57-57]
-110	P54[58-58]
+110	P54[58-58]
-111	P54[59-59]
+111	P54[59-59]
-112	P54[60-60]
+112	P54[60-60]
-113	P54[61-61]
+113	P54[61-61]
-114	P54[62-62]
+114	P54[62-62]
-115	P54[63-63]
+115	P54[63-63]
-116	P54[64-64]
+116	P54[64-64]
-117	P54[65-65]
+117	P54[65-65]
-118	P54[66-66]
+118	P54[66-66]
-119	P54[67-67]
+119	P54[67-67]
-120	P54[68-68]
+120	P54[68-68]
-121	P54[69-69]
+121	P54[69-69]
-122	P31[0-0]
+122	P31[0-0]
-123	P31[1-1]
+123	P31[1-1]
-124	P31[2-2]
+124	P31[2-2]
-125	P31[3-3]
+125	P31[3-3]
-126	P31[4-4]
+126	P31[4-4]
-127	P31[5-5]
+127	P31[5-5]
-128	P31[6-6]
+128	P31[6-6]
-129	P31[7-7]
+129	P31[7-7]
-130	P31[8-8]
+130	P31[8-8]
-131	P31[9-9]
+131	P31[9-9]
-132	P31[10-10]
+132	P31[10-10]
-133	P31[11-11]
+133	P31[11-11]
-134	P31[12-12]
+134	P31[12-12]
-135	P31[13-13]
+135	P31[13-13]
-136	P31[14-14]
+136	P31[14-14]
-137	P31[15-15]
+137	P31[15-15]
-138	P31[16-16]
+138	P31[16-16]
-139	P31[17-17]
+139	P31[17-17]
-140	P31[18-18]
+140	P31[18-18]
-141	P31[19-19]
+141	P31[19-19]
-142	P31[20-20]
+142	P31[20-20]
-143	P31[21-21]
+143	P31[21-21]
-144	P31[22-22]
+144	P31[22-22]
-145	P31[23-23]
+145	P31[23-23]
-146	P31[24-24]
+146	P31[24-24]
-147	P31[25-25]
+147	P31[25-25]
-148	P31[26-26]
+148	P31[26-26]
-149	P31[27-27]
+149	P31[27-27]
-150	P31[28-28]
+150	P31[28-28]
-151	P31[29-29]
+151	P31[29-29]
-152	P31[30-30]
+152	P31[30-30]
-153	P31[31-31]
+153	P31[31-31]
-154	P31[32-32]
+154	P31[32-32]
-155	P31[33-33]
+155	P31[33-33]
-156	P31[34-34]
+156	P31[34-34]
-157	P31[35-35]
+157	P31[35-35]
-158	P31[36-36]
+158	P31[36-36]
-159	P31[37-37]
+159	P31[37-37]
-160	P31[38-38]
+160	P31[38-38]
-161	P31[39-39]
+161	P31[39-39]
-162	P31[40-40]
+162	P31[40-40]
-163	P31[41-41]
+163	P31[41-41]
-164	P31[42-42]
+164	P31[42-42]
-165	P31[43-43]
+165	P31[43-43]
-166	P31[44-44]
+166	P31[44-44]
-167	P31[45-45]
+167	P31[45-45]
-168	P31[46-46]
+168	P31[46-46]
-169	P31[47-47]
+169	P31[47-47]
-170	P31[48-48]
+170	P31[48-48]
-171	P31[49-49]
+171	P31[49-49]
-172	P31[50-50]
+172	P31[50-50]
-173	P31[51-51]
+173	P31[51-51]
-174	P31[52-52]
+174	P31[52-52]
-175	P31[53-53]
+175	P31[53-53]
-176	P31[54-54]
+176	P31[54-54]
-177	P31[55-55]
+177	P31[55-55]
-178	P31[56-56]
+178	P31[56-56]
-179	P31[57-57]
+179	P31[57-57]
-180	P31[58-58]
+180	P31[58-58]
-181	P31[59-59]
+181	P31[59-59]
-182	P31[60-60]
+182	P31[60-60]
-183	P31[61-61]
+183	P31[61-61]
-184	P31[62-62]
+184	P31[62-62]
-185	P31[63-63]
+185	P31[63-63]
-186	P31[64-64]
+186	P31[64-64]
-187	P31[65-65]
+187	P31[65-65]
-188	P31[66-66]
+188	P31[66-66]
-189	P31[67-67]
+189	P31[67-67]
-190	P31[68-68]
+190	P31[68-68]
-191	P31[69-69]
+191	P31[69-69]
-192	P463[26-26]
+192	P463[26-26]
-193	P463[27-27]
+193	P463[27-27]
-194	P463[28-28]
+194	P463[28-28]
-195	P463[29-29]
+195	P463[29-29]
-196	P463[30-30]
+196	P463[30-30]
-197	P463[31-31]
+197	P463[31-31]
-198	P463[32-32]
+198	P463[32-32]
-199	P463[33-33]
+199	P463[33-33]
-200	P463[34-34]
+200	P463[34-34]
-201	P463[35-35]
+201	P463[35-35]
-202	P463[36-36]
+202	P463[36-36]
-203	P463[37-37]
+203	P463[37-37]
-204	P463[38-38]
+204	P463[38-38]
-205	P463[39-39]
+205	P463[39-39]
-206	P463[40-40]
+206	P463[40-40]
-207	P463[41-41]
+207	P463[41-41]
-208	P463[42-42]
+208	P463[42-42]
-209	P463[43-43]
+209	P463[43-43]
-210	P463[44-44]
+210	P463[44-44]
-211	P463[45-45]
+211	P463[45-45]
-212	P463[46-46]
+212	P463[46-46]
-213	P463[47-47]
+213	P463[47-47]
-214	P463[48-48]
+214	P463[48-48]
-215	P463[49-49]
+215	P463[49-49]
-216	P463[50-50]
+216	P463[50-50]
-217	P463[51-51]
+217	P463[51-51]
-218	P463[52-52]
+218	P463[52-52]
-219	P463[53-53]
+219	P463[53-53]
-220	P463[54-54]
+220	P463[54-54]
-221	P463[55-55]
+221	P463[55-55]
-222	P463[56-56]
+222	P463[56-56]
-223	P463[57-57]
+223	P463[57-57]
-224	P463[58-58]
+224	P463[58-58]
-225	P463[59-59]
+225	P463[59-59]
-226	P463[60-60]
+226	P463[60-60]
-227	P463[61-61]
+227	P463[61-61]
-228	P463[62-62]
+228	P463[62-62]
-229	P463[63-63]
+229	P463[63-63]
-230	P463[64-64]
+230	P463[64-64]
-231	P463[65-65]
+231	P463[65-65]
-232	P463[66-66]
+232	P463[66-66]
-233	P463[67-67]
+233	P463[67-67]
-234	P463[68-68]
+234	P463[68-68]
-235	P463[69-69]
+235	P463[69-69]
-236	P512[4-69]
+236	P512[4-69]
-237	P190[0-29]
+237	P190[0-29]
-238	P150[0-3]
+238	P150[0-3]
-239	P1376[39-47]
+239	P1376[39-47]
-240	P463[0-7]
+240	P463[0-7]
-241	P166[0-7]
+241	P166[0-7]
-242	P2962[18-30]
+242	P2962[18-30]
-243	P108[29-36]
+243	P108[29-36]
-244	P39[0-3]
+244	P39[0-3]
-245	P17[47-48]
+245	P17[47-48]
-246	P166[21-23]
+246	P166[21-23]
-247	P793[46-69]
+247	P793[46-69]
-248	P69[32-41]
+248	P69[32-41]
-249	P17[57-58]
+249	P17[57-58]
-250	P190[42-45]
+250	P190[42-45]
-251	P2962[39-42]
+251	P2962[39-42]
-252	P54[0-18]
+252	P54[0-18]
-253	P26[56-61]
+253	P26[56-61]
-254	P150[14-17]
+254	P150[14-17]
-255	P463[16-17]
+255	P463[16-17]
-256	P26[39-46]
+256	P26[39-46]
-257	P579[36-43]
+257	P579[36-43]
-258	P579[16-23]
+258	P579[16-23]
-259	P2962[59-60]
+259	P2962[59-60]
-260	P1411[59-61]
+260	P1411[59-61]
-261	P26[20-27]
+261	P26[20-27]
-262	P6[4-69]
+262	P6[4-69]
-263	P1435[33-34]
+263	P1435[33-34]
-264	P166[52-53]
+264	P166[52-53]
-265	P108[49-57]
+265	P108[49-57]
-266	P150[10-13]
+266	P150[10-13]
-267	P1346[47-68]
+267	P1346[47-68]
-268	P150[18-21]
+268	P150[18-21]
-269	P1346[13-46]
+269	P1346[13-46]
-270	P69[20-23]
+270	P69[20-23]
-271	P39[31-32]
+271	P39[31-32]
-272	P1411[32-37]
+272	P1411[32-37]
-273	P166[62-63]
+273	P166[62-63]
-274	P150[44-47]
+274	P150[44-47]
-275	P2962[61-62]
+275	P2962[61-62]
-276	P150[48-51]
+276	P150[48-51]
-277	P150[52-55]
+277	P150[52-55]
-278	P1411[62-67]
+278	P1411[62-67]
-279	P1435[35-36]
+279	P1435[35-36]
-280	P1411[48-51]
+280	P1411[48-51]
-281	P150[22-25]
+281	P150[22-25]
-282	P2962[63-64]
+282	P2962[63-64]
-283	P2962[65-66]
+283	P2962[65-66]
-284	P166[58-59]
+284	P166[58-59]
-285	P190[46-49]
+285	P190[46-49]
-286	P54[34-35]
+286	P54[34-35]
-287	P1435[4-16]
+287	P1435[4-16]
-288	P463[18-19]
+288	P463[18-19]
-289	P150[31-34]
+289	P150[31-34]
-290	P150[35-38]
+290	P150[35-38]
-291	P39[35-36]
+291	P39[35-36]
-292	P26[62-69]
+292	P26[62-69]
-293	P1411[56-58]
+293	P1411[56-58]
-294	P1435[37-38]
+294	P1435[37-38]
-295	P166[60-61]
+295	P166[60-61]
-296	P39[33-34]
+296	P39[33-34]
-297	P102[24-31]
+297	P102[24-31]
-298	P2962[43-46]
+298	P2962[43-46]
-299	P108[37-48]
+299	P108[37-48]
-300	P190[50-53]
+300	P190[50-53]
-301	P39[4-6]
+301	P39[4-6]
-302	P1435[39-40]
+302	P1435[39-40]
-303	P793[0-45]
+303	P793[0-45]
-304	P150[64-69]
+304	P150[64-69]
-305	P39[19-22]
+305	P39[19-22]
-306	P27[30-38]
+306	P27[30-38]
-307	P2962[31-38]
+307	P2962[31-38]
-308	P1411[24-31]
+308	P1411[24-31]
-309	P102[40-45]
+309	P102[40-45]
-310	P39[37-38]
+310	P39[37-38]
-311	P463[8-11]
+311	P463[8-11]
-312	P1435[41-42]
+312	P1435[41-42]
-313	P27[52-59]
+313	P27[52-59]
-314	P69[16-19]
+314	P69[16-19]
-315	P17[16-18]
+315	P17[16-18]
-316	P190[54-57]
+316	P190[54-57]
-317	P1435[43-44]
+317	P1435[43-44]
-318	P166[8-15]
+318	P166[8-15]
-319	P166[45-47]
+319	P166[45-47]
-320	P2962[47-50]
+320	P2962[47-50]
-321	P39[39-40]
+321	P39[39-40]
-322	P1411[52-55]
+322	P1411[52-55]
-323	P108[58-69]
+323	P108[58-69]
-324	P463[20-21]
+324	P463[20-21]
-325	P39[41-42]
+325	P39[41-42]
-326	P150[26-30]
+326	P150[26-30]
-327	P150[39-43]
+327	P150[39-43]
-328	P1435[45-46]
+328	P1435[45-46]
-329	P26[28-38]
+329	P26[28-38]
-330	P54[27-30]
+330	P54[27-30]
-331	P190[58-61]
+331	P190[58-61]
-332	P17[59-61]
+332	P17[59-61]
-333	P54[36-37]
+333	P54[36-37]
-334	P166[16-20]
+334	P166[16-20]
-335	P166[37-40]
+335	P166[37-40]
-336	P1435[47-48]
+336	P1435[47-48]
-337	P17[0-3]
+337	P17[0-3]
-338	P26[47-55]
+338	P26[47-55]
-339	P1435[49-50]
+339	P1435[49-50]
-340	P1435[25-28]
+340	P1435[25-28]
-341	P150[4-9]
+341	P150[4-9]
-342	P102[63-69]
+342	P102[63-69]
-343	P26[0-19]
+343	P26[0-19]
-344	P1435[17-24]
+344	P1435[17-24]
-345	P39[23-26]
+345	P39[23-26]
-346	P1435[51-52]
+346	P1435[51-52]
-347	P39[7-11]
+347	P39[7-11]
-348	P69[12-15]
+348	P69[12-15]
-349	P69[24-31]
+349	P69[24-31]
-350	P102[0-23]
+350	P102[0-23]
-351	P39[43-44]
+351	P39[43-44]
-352	P579[24-35]
+352	P579[24-35]
-353	P190[62-65]
+353	P190[62-65]
-354	P1435[53-54]
+354	P1435[53-54]
-355	P1376[0-18]
+355	P1376[0-18]
-356	P27[0-14]
+356	P27[0-14]
-357	P463[12-15]
+357	P463[12-15]
-358	P166[33-36]
+358	P166[33-36]
-359	P102[32-39]
+359	P102[32-39]
-360	P17[4-7]
+360	P17[4-7]
-361	P190[30-41]
+361	P190[30-41]
-362	P166[24-28]
+362	P166[24-28]
-363	P190[66-69]
+363	P190[66-69]
-364	P69[42-69]
+364	P69[42-69]
-365	P1435[55-56]
+365	P1435[55-56]
-366	P54[31-33]
+366	P54[31-33]
-367	P39[45-46]
+367	P39[45-46]
-368	P17[12-15]
+368	P17[12-15]
-369	P1435[57-58]
+369	P1435[57-58]
-370	P54[19-26]
+370	P54[19-26]
-371	P2962[51-54]
+371	P2962[51-54]
-372	P2962[67-69]
+372	P2962[67-69]
-373	P1435[59-60]
+373	P1435[59-60]
-374	P579[44-56]
+374	P579[44-56]
-375	P1435[61-62]
+375	P1435[61-62]
-376	P166[41-44]
+376	P166[41-44]
-377	P17[19-22]
+377	P17[19-22]
-378	P1376[19-38]
+378	P1376[19-38]
-379	P17[23-26]
+379	P17[23-26]
-380	P1376[48-69]
+380	P1376[48-69]
-381	P463[22-23]
+381	P463[22-23]
-382	P17[27-30]
+382	P17[27-30]
-383	P1435[63-64]
+383	P1435[63-64]
-384	P69[0-3]
+384	P69[0-3]
-385	P1435[66-67]
+385	P1435[66-67]
-386	P17[35-38]
+386	P17[35-38]
-387	P69[8-11]
+387	P69[8-11]
-388	P1435[68-69]
+388	P1435[68-69]
-389	P17[31-34]
+389	P17[31-34]
-390	P102[46-53]
+390	P102[46-53]
-391	P27[60-69]
+391	P27[60-69]
-392	P579[57-69]
+392	P579[57-69]
-393	P69[4-7]
+393	P69[4-7]
-394	P1411[7-14]
+394	P1411[7-14]
-395	P551[0-35]
+395	P551[0-35]
-396	P108[0-28]
+396	P108[0-28]
-397	P17[8-11]
+397	P17[8-11]
-398	P1411[38-47]
+398	P1411[38-47]
-399	P17[43-46]
+399	P17[43-46]
-400	P17[49-52]
+400	P17[49-52]
-401	P166[64-69]
+401	P166[64-69]
-402	P1435[29-32]
+402	P1435[29-32]
-403	P54[38-39]
+403	P54[38-39]
-404	P39[27-30]
+404	P39[27-30]
-405	P2962[55-58]
+405	P2962[55-58]
-406	P463[24-25]
+406	P463[24-25]
-407	P17[39-42]
+407	P17[39-42]
-408	P17[53-56]
+408	P17[53-56]
-409	P17[66-69]
+409	P17[66-69]
-410	P17[62-65]
+410	P17[62-65]
-411	P1411[15-23]
+411	P1411[15-23]
-412	P166[48-51]
+412	P166[48-51]
-413	P27[15-29]
+413	P27[15-29]
-414	P150[56-63]
+414	P150[56-63]
-415	P27[39-51]
+415	P27[39-51]
-416	P39[47-48]
+416	P39[47-48]
-417	P166[29-32]
+417	P166[29-32]
-418	P39[12-18]
+418	P39[12-18]
-419	P166[54-57]
+419	P166[54-57]
-420	P551[36-69]
+420	P551[36-69]
-421	P579[0-15]
+421	P579[0-15]
-422	P102[54-62]
+422	P102[54-62]

data/wikidata12k/time_map.dict

@@ -1,71 +1,71 @@
-0	19	19
+0	19	19
-1	20	1643
+1	20	1643
-2	1644	1790
+2	1644	1790
-3	1791	1816
+3	1791	1816
-4	1817	1855
+4	1817	1855
-5	1856	1871
+5	1856	1871
-6	1872	1893
+6	1872	1893
-7	1894	1905
+7	1894	1905
-8	1906	1913
+8	1906	1913
-9	1914	1918
+9	1914	1918
-10	1919	1920
+10	1919	1920
-11	1921	1924
+11	1921	1924
-12	1925	1929
+12	1925	1929
-13	1930	1933
+13	1930	1933
-14	1934	1937
+14	1934	1937
-15	1938	1941
+15	1938	1941
-16	1942	1945
+16	1942	1945
-17	1946	1948
+17	1946	1948
-18	1949	1950
+18	1949	1950
-19	1951	1953
+19	1951	1953
-20	1954	1956
+20	1954	1956
-21	1957	1959
+21	1957	1959
-22	1960	1961
+22	1960	1961
-23	1962	1963
+23	1962	1963
-24	1964	1965
+24	1964	1965
-25	1966	1967
+25	1966	1967
-26	1968	1968
+26	1968	1968
-27	1969	1970
+27	1969	1970
-28	1971	1972
+28	1971	1972
-29	1973	1974
+29	1973	1974
-30	1975	1976
+30	1975	1976
-31	1977	1978
+31	1977	1978
-32	1979	1980
+32	1979	1980
-33	1981	1982
+33	1981	1982
-34	1983	1983
+34	1983	1983
-35	1984	1984
+35	1984	1984
-36	1985	1985
+36	1985	1985
-37	1986	1986
+37	1986	1986
-38	1987	1987
+38	1987	1987
-39	1988	1988
+39	1988	1988
-40	1989	1989
+40	1989	1989
-41	1990	1990
+41	1990	1990
-42	1991	1991
+42	1991	1991
-43	1992	1992
+43	1992	1992
-44	1993	1993
+44	1993	1993
-45	1994	1994
+45	1994	1994
-46	1995	1995
+46	1995	1995
-47	1996	1996
+47	1996	1996
-48	1997	1997
+48	1997	1997
-49	1998	1998
+49	1998	1998
-50	1999	1999
+50	1999	1999
-51	2000	2000
+51	2000	2000
-52	2001	2001
+52	2001	2001
-53	2002	2002
+53	2002	2002
-54	2003	2003
+54	2003	2003
-55	2004	2004
+55	2004	2004
-56	2005	2005
+56	2005	2005
-57	2006	2006
+57	2006	2006
-58	2007	2007
+58	2007	2007
-59	2008	2008
+59	2008	2008
-60	2009	2009
+60	2009	2009
-61	2010	2010
+61	2010	2010
-62	2011	2011
+62	2011	2011
-63	2012	2012
+63	2012	2012
-64	2013	2013
+64	2013	2013
-65	2014	2014
+65	2014	2014
-66	2015	2015
+66	2015	2015
-67	2016	2016
+67	2016	2016
-68	2017	2017
+68	2017	2017
-69	2018	2020
+69	2018	2020
-70	2021	2021
+70	2021	2021

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
-67
-69
-70
-72
-76
-78
-79
-83
-86
-87
-89
-101
-102
-103
-108
-111
-112
-119
-121
-122
-126
-128
-129
-132
-134
-138
-141
-144
-146
-153
-154
-155
-156
-158
-159
-160
-161
-162
-164
-165
-166
-168
-173
-175
-176
-177
-182
-184
-185
-186
-187
-188
-190
-192
-193
-201
-202
-208
-209
-211
-213
-215
-216
-217
-222
-227
-229
-235
-239
-240
-242
-243
-245
-246
-247
-251
-252
-254
-257
-261
-263
-266
-268
-271
-279
-282
-292
-300
-303
-305
-308
-309
-311
-313
-316
-319
-322
-324
-325
-329
-331
-332
-333
-334
-337
-339
-342
-343
-346
-347
-348
-349
-350
-352
-353
-355
-357
-361
-362
-363
-367
-371
-373
-378
-379
-383
-384
-385
-389
-392
-394
-395
-396
-397
-399
-400
-402
-403
-407
-409
-415
-416
-420
-421
-422
-428
-429
-432
-433
-440
-442
-443
-450
-452
-459
-463
-464
-466
-471
-472
-476
-480
-484
-489
-490
-493
-494
-495
-500
-503
-507
-509
-515
-519
-520
-521
-525
-528
-529
-533
-534
-539
-541
-542
-548
-550
-556
-559
-563
-566
-567
-569
-573
-575
-576
-579
-582
-585
-588
-592
-593
-594
-596
-597
-598
-599
-603
-604
-605
-606
-607
-613
-614
-616
-617
-618
-619
-621
-623
-624
-625
-628
-638
-641
-642
-648
-651
-659
-660
-661
-663
-664
-676
-677
-678
-680
-682
-686
-688
-689
-691
-694
-698
-704
-707
-708
-712
-713
-716
-719
-723
-724
-726
-728
-732
-741
-742
-743
-744
-745
-746
-750
-752
-755
-759
-762
-764
-767
-768
-770
-772
-775
-777
-780
-782
-785
-789
-799
-800
-801
-802
-804
-805
-810
-811
-816
-822
-823
-826
-829
-832
-834
-835
-838
-839
-842
-847
-848
-850
-851
-852
-856
-861
-862
-865
-867
-868
-869
-874
-876
-882
-883
-884
-885
-891
-893
-898
-899
-906
-909
-910
-911
-912
-920
-923
-924
-926
-928
-934
-938
-941
-942
-943
-944
-945
-951
-954
-956
-957
-958
-960
-961
-963
-964
-968
-970
-975
-976
-977
-979
-981
-988
-989
-992
-993
-995
-997
-1005
-1008
-1009
-1012
-1013
-1014
-1015
-1023
-1029
-1032
-1038
-1044
-1045
-1052
-1053
-1055
-1057
-1060
-1061
-1065
-1066
-1074
-1077
-1079
-1080
-1082
-1083
-1085
-1086
-1089
-1090
-1091
-1095
-1104
-1107
-1111
-1114
-1121
-1124
-1126
-1127
-1128
-1131
-1132
-1139
-1140
-1142
-1143
-1145
-1148
-1150
-1157
-1163
-1164
-1168
-1170
-1171
-1172
-1173
-1179
-1182
-1186
-1189
-1190
-1191
-1194
-1196
-1198
-1201
-1204
-1206
-1208
-1217
-1220
-1223
-1228
-1231
-1232
-1235
-1236
-1237
-1238
-1240
-1246
-1247
-1249
-1252
-1258
-1260
-1265
-1266
-1273
-1274
-1278
-1279
-1280
-1284
-1286
-1287
-1288
-1289
-1290
-1293
-1294
-1295
-1297
-1298
-1301
-1303
-1304
-1305
-1307
-1308
-1309
-1314
-1318
-1319
-1323
-1325
-1327
-1328
-1333
-1337
-1340
-1341
-1343
-1345
-1346
-1347
-1349
-1350
-1351
-1358
-1364
-1365
-1367
-1368
-1369
-1370
-1373
-1375
-1376
-1378
-1380
-1381
-1382
-1385
-1387
-1390
-1391
-1394
-1396
-1397
-1399
-1400
-1406
-1409
-1412
-1416
-1417
-1418
-1420
-1423
-1425
-1428
-1430
-1431
-1432
-1437
-1438
-1439
-1444
-1447
-1450
-1454
-1456
-1457
-1460
-1464
-1465
-1469
-1473
-1474
-1475
-1477
-1479
-1488
-1490
-1493
-1494
-1497
-1500
-1502
-1503
-1504
-1505
-1507
-1508
-1510
-1514
-1515
-1520
-1522
-1523
-1526
-1547
-1549
-1553
-1556
-1557
-1558
-1562
-1563
-1564
-1565
-1570
-1571
-1574
-1575
-1579
-1591
-1592
-1594
-1601
-1604
-1605
-1606
-1608
-1609
-1613
-1618
-1619
-1620
-1621
-1632
-1634
-1635
-1636
-1642
-1643
-1648
-1650
-1652
-1653
-1660
-1661
-1662
-1666
-1669
-1670
-1676
-1677
-1682
-1683
-1690
-1692
-1693
-1697
-1698
-1702
-1703
-1706
-1709
-1711
-1713
-1715
-1717
-1721
-1724
-1725
-1729
-1730
-1733
-1734
-1735
-1736
-1741
-1745
-1746
-1748
-1749
-1751
-1755
-1761
-1763
-1766
-1767
-1768
-1769
-1773
-1775
-1777
-1778
-1783
-1789
-1790
-1792
-1793
-1795
-1800
-1803
-1805
-1809
-1812
-1815
-1816
-1819
-1820
-1822
-1823
-1824
-1825
-1828
-1831
-1833
-1834
-1835
-1836
-1837
-1842
-1848
-1849
-1852
-1853
-1854
-1856
-1857
-1858
-1859
-1861
-1864
-1865
-1869
-1873
-1874
-1876
-1877
-1882
-1883
-1884
-1885
-1888
-1889
-1890
-1892
-1894
-1896
-1899
-1902
-1903
-1905
-1908
-1910
-1913
-1914
-1915
-1920
-1928
-1931
-1936
-1938
-1941
-1942
-1944
-1946
-1947
-1948
-1954
-1956
-1958
-1961
-1966
-1968
-1969
-1971
-1972
-1977
-1979
-1985
-1986
-1987
-1988
-1989
-1990
-1999
-2001
-2005
-2009
-2010
-2012
-2013
-2014
-2015
-2017
-2018
-2022
-2023
-2028
-2032
-2036
-2037
-2038
-2041
-2042
-2043
-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
-59
-60
-66
-69
-72
-76
-78
-81
-83
-84
-85
-88
-89
-94
-100
-102
-105
-106
-107
-108
-109
-115
-116
-121
-123
-124
-127
-128
-133
-135
-137
-138
-141
-144
-156
-157
-159
-161
-168
-171
-172
-174
-175
-176
-181
-182
-186
-188
-189
-190
-191
-195
-197
-198
-200
-201
-204
-208
-212
-215
-216
-217
-218
-219
-220
-222
-224
-225
-227
-229
-230
-233
-236
-239
-240
-242
-243
-244
-246
-247
-250
-251
-253
-254
-255
-256
-257
-261
-265
-266
-271
-273
-274
-275
-276
-279
-280
-282
-284
-287
-289
-292
-296
-297
-299
-300
-302
-308
-311
-312
-315
-316
-317
-320
-321
-322
-326
-331
-333
-335
-336
-337
-339
-344
-345
-346
-347
-351
-352
-353
-354
-355
-358
-359
-362
-364
-366
-368
-373
-376
-388
-390
-392
-393
-394
-395
-397
-398
-401
-403
-406
-407
-409
-410
-412
-413
-415
-416
-418
-420
-421
-425
-435
-437
-443
-444
-445
-446
-448
-453
-455
-456
-462
-463
-468
-470
-471
-477
-478
-481
-485
-490
-491
-493
-498
-505
-510
-513
-514
-515
-516
-521
-523
-527
-529
-531
-534
-535
-536
-537
-538
-543
-547
-550
-555
-557
-558
-559
-560
-561
-565
-566
-567
-570
-575
-577
-580
-581
-582
-593
-594
-595
-597
-599
-602
-605
-609
-612
-614
-616
-618
-620
-625
-628
-632
-633
-635
-636
-643
-644
-645
-646
-647
-648
-652
-662
-663
-669
-670
-672
-673
-681
-682
-685
-687
-689
-693
-697
-700
-702
-707
-711
-716
-717
-719
-727
-728
-729
-730
-732
-733
-734
-738
-740
-744
-751
-754
-755
-756
-760
-761
-762
-763
-766
-768
-770
-771
-772
-773
-774
-775
-776
-777
-779
-781
-782
-784
-786
-788
-789
-792
-795
-799
-800
-805
-808
-810
-812
-815
-817
-820
-821
-825
-827
-829
-831
-832
-833
-834
-836
-837
-840
-841
-842
-848
-849
-850
-851
-852
-855
-858
-860
-861
-866
-869
-871
-872
-877
-878
-880
-881
-882
-884
-885
-886
-891
-893
-897
-899
-901
-908
-911
-913
-919
-921
-925
-927
-929
-934
-937
-938
-945
-947
-948
-950
-957
-962
-963
-965
-969
-971
-976
-977
-978
-985
-986
-989
-990
-991
-993
-996
-997
-998
-1003
-1005
-1006
-1007
-1010
-1011
-1013
-1014
-1019
-1022
-1024
-1025
-1028
-1031
-1032
-1034
-1038
-1040
-1042
-1043
-1048
-1049
-1050
-1051
-1056
-1057
-1059
-1062
-1063
-1065
-1066
-1071
-1072
-1074
-1075
-1076
-1080
-1081
-1082
-1085
-1086
-1087
-1090
-1095
-1096
-1098
-1099
-1101
-1105
-1108
-1109
-1110
-1112
-1117
-1119
-1121
-1122
-1123
-1125
-1129
-1133
-1139
-1143
-1145
-1147
-1151
-1153
-1154
-1158
-1159
-1162
-1165
-1167
-1169
-1171
-1175
-1177
-1178
-1180
-1181
-1182
-1185
-1187
-1188
-1193
-1197
-1199
-1217
-1218
-1220
-1221
-1225
-1227
-1230
-1232
-1233
-1235
-1236
-1237
-1238
-1239
-1244
-1246
-1247
-1248
-1262
-1263
-1264
-1266
-1267
-1269
-1271
-1272
-1273
-1276
-1277
-1278
-1280
-1284
-1291
-1292
-1293
-1295
-1296
-1297
-1299
-1300
-1301
-1305
-1307
-1309
-1312
-1322
-1325
-1330
-1334
-1335
-1337
-1338
-1340
-1342
-1346
-1347
-1350
-1351
-1356
-1357
-1358
-1359
-1366
-1368
-1370
-1371
-1373
-1376
-1379
-1382
-1383
-1384
-1385
-1386
-1387
-1389
-1393
-1394
-1398
-1403
-1404
-1409
-1411
-1413
-1415
-1420
-1421
-1423
-1426
-1429
-1431
-1433
-1434
-1439
-1444
-1446
-1447
-1456
-1457
-1461
-1467
-1469
-1472
-1477
-1481
-1493
-1494
-1496
-1498
-1499
-1501
-1503
-1506
-1507
-1508
-1510
-1511
-1512
-1516
-1521
-1523
-1527
-1528
-1529
-1530
-1535
-1537
-1540
-1541
-1542
-1546
-1552
-1559
-1562
-1563
-1564
-1567
-1572
-1574
-1577
-1581
-1587
-1589
-1594
-1601
-1603
-1613
-1614
-1616
-1617
-1618
-1619
-1620
-1621
-1623
-1624
-1625
-1626
-1631
-1632
-1634
-1635
-1636
-1638
-1640
-1642
-1643
-1645
-1647
-1648
-1650
-1651
-1652
-1655
-1656
-1658
-1661
-1662
-1663
-1665
-1666
-1667
-1668
-1669
-1674
-1675
-1685
-1686
-1695
-1697
-1698
-1699
-1706
-1713
-1716
-1717
-1718
-1723
-1724
-1725
-1729
-1730
-1731
-1732
-1733
-1734
-1738
-1741
-1744
-1749
-1761
-1762
-1769
-1772
-1774
-1776
-1777
-1781
-1784
-1787
-1789
-1790
-1791
-1798
-1804
-1805
-1807
-1808
-1811
-1812
-1816
-1821
-1824
-1827
-1831
-1834
-1835
-1836
-1842
-1847
-1852
-1855
-1856
-1859
-1861
-1862
-1865
-1866
-1871
-1872
-1874
-1876
-1877
-1878
-1879
-1880
-1881
-1885
-1887
-1888
-1889
-1891
-1892
-1894
-1895
-1899
-1900
-1907
-1911
-1913
-1914
-1915
-1917
-1919
-1920
-1923
-1924
-1925
-1927
-1934
-1939
-1940
-1941
-1948
-1951
-1953
-1957
-1958
-1959
-1961
-1962
-1965
-1966
-1967
-1971
-1973
-1975
-1979
-1981
-1983
-1989
-1996
-1997
-2000
-2004
-2007
-2009
-2011
-2012
-2016
-2017
-2019
-2021
-2024
-2029
-2040
-2042
-2044
-2048

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.logger = logger
-            self.p.name, self.p.log_dir, self.p.config_dir)
 
         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')
@@ -84,7 +85,7 @@ 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")):
@@ -108,20 +109,14 @@ class Main(object):
         sr2o = ddict(set)
 
         for split in ['train', 'test', 'valid']:
-            samples = 0
+            for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)):
-            for i, line in enumerate(open('./data/{}/{}.txt'.format(self.p.dataset, split))):
+                sub, rel, obj, *_ = map(str.lower, line.replace('\xa0', '').strip().split('\t'))
-                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))
 
                 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')
         self.data = dict(self.data)
 
         self.sr2o = {k: list(v) for k, v in sr2o.items()}
@@ -159,8 +154,6 @@ class Main(object):
                     {'triple': (obj, rel_inv, sub), 'label': self.sr2o_all[(obj, rel_inv)]})
 
         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(
@@ -415,6 +408,13 @@ 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')
@@ -422,9 +422,21 @@ class Main(object):
                 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 +451,8 @@ 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):
@@ -635,7 +648,6 @@ 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 +656,10 @@ 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
 
@@ -695,18 +708,27 @@ 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:
+        model = Main(args, logger)
-            try:
+        model.fit()
-                model.fit()
+        # while True:
-            except Exception as e:
+        #     try:
-                print(e)
+        #         model = Main(args, logger)
-                time.sleep(30)
+        #         model.fit()
-                del model
+        #     except Exception as e:
-                model = Main(args)
+        #         print(e)
-                continue
+        #         traceback.print_exc()
-            break
+        #         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):
@@ -435,6 +435,50 @@ class TuckER(torch.nn.Module):
 
         return pred
 
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(2 * dim)
+
+    def forward(self, x):
+        """
+        x: B, C, H, W
+        """
+        B, C, H, W = x.shape
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.reduction(x)
+        x = self.norm(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        flops += H * W * self.dim // 2
+        return flops
 
 class FouriER(torch.nn.Module):
     def __init__(self, params, hid_drop = None, embed_dim = None):
@@ -488,9 +532,10 @@ class FouriER(torch.nn.Module):
         self.patch_embed = PatchEmbed(in_chans=channels, patch_size=self.p.patch_size, 
                                       embed_dim=self.p.embed_dim, stride=4, padding=2)
         network = []
-        layers = [4, 4, 12, 4]
+        layers = [2, 2, 6, 2]
-        embed_dims = [self.p.embed_dim, 128, 320, 128]
+        embed_dims = [self.p.embed_dim, 320, 256, 128]
-        mlp_ratios = [4, 4, 4, 4]
+        mlp_ratios = [4, 4, 8, 12]
+        num_heads = [4, 4, 4, 4]
         downsamples = [True, True, True, True]
         pool_size=3
         act_layer=nn.GELU
@@ -502,6 +547,7 @@ class FouriER(torch.nn.Module):
         down_patch_size=3
         down_stride=2
         down_pad=1
+        window_size = 4
         num_classes=self.p.embed_dim
         for i in range(len(layers)):
             stage = basic_blocks(embed_dims[i], i, layers, 
@@ -510,7 +556,9 @@ class FouriER(torch.nn.Module):
                                  drop_rate=drop_rate, 
                                  drop_path_rate=drop_path_rate,
                                  use_layer_scale=use_layer_scale, 
-                                 layer_scale_init_value=layer_scale_init_value)
+                                 layer_scale_init_value=layer_scale_init_value,
+                                 num_heads=num_heads[i], input_resolution=(image_h // (2**i), image_w // (2**i)),
+                                 window_size=window_size, shift_size=0)
             network.append(stage)
             if i >= len(layers) - 1:
                 break
@@ -522,6 +570,7 @@ class FouriER(torch.nn.Module):
                         padding=down_pad, 
                         in_chans=embed_dims[i], embed_dim=embed_dims[i+1]
                         )
+                    # PatchMerging(dim=embed_dims[i+1])
                     )
 
         self.network = nn.ModuleList(network)
@@ -687,7 +736,7 @@ def basic_blocks(dim, index, layers,
                  pool_size=3, mlp_ratio=4., 
                  act_layer=nn.GELU, norm_layer=GroupNorm, 
                  drop_rate=.0, drop_path_rate=0., 
-                 use_layer_scale=True, layer_scale_init_value=1e-5):
+                 use_layer_scale=True, layer_scale_init_value=1e-5, num_heads = 4, input_resolution = None, window_size = 4, shift_size = 2):
     """
     generate PoolFormer blocks for a stage
     return: PoolFormer blocks 
@@ -702,11 +751,176 @@ def basic_blocks(dim, index, layers,
             drop=drop_rate, drop_path=block_dpr, 
             use_layer_scale=use_layer_scale, 
             layer_scale_init_value=layer_scale_init_value, 
+            num_heads=num_heads, input_resolution = input_resolution,
+            window_size=window_size, shift_size=shift_size
             ))
     blocks = nn.Sequential(*blocks)
 
     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:
+            try:
+                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)
+            except:
+                pass
+            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):
     """
@@ -724,14 +938,18 @@ class PoolFormerBlock(nn.Module):
     """
     def __init__(self, dim, pool_size=3, mlp_ratio=4., 
                  act_layer=nn.GELU, norm_layer=GroupNorm, 
-                 drop=0., drop_path=0., 
+                 drop=0., drop_path=0., num_heads=4,
-                 use_layer_scale=True, layer_scale_init_value=1e-5):
+                 use_layer_scale=True, layer_scale_init_value=1e-5, input_resolution = None, window_size = 4, shift_size = 2):
 
         super().__init__()
 
         self.norm1 = norm_layer(dim)
         #self.token_mixer = Pooling(pool_size=pool_size)
-        self.token_mixer = FNetBlock()
+        # self.token_mixer = FNetBlock()
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.input_resolution = input_resolution
+        self.token_mixer = WindowAttention(dim=dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, attn_drop=0.2, proj_drop=0.1)
         self.norm2 = norm_layer(dim)
         mlp_hidden_dim = int(dim * mlp_ratio)
         self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, 
@@ -746,17 +964,52 @@ class PoolFormerBlock(nn.Module):
                 layer_scale_init_value * torch.ones((dim)), requires_grad=True)
             self.layer_scale_2 = nn.Parameter(
                 layer_scale_init_value * torch.ones((dim)), requires_grad=True)
+            
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, 1, H, W))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, :, h, w] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
 
     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.shift_size > 0:
+            x = torch.roll(x_attn, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = x_attn
         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

@@ -1,4 +1,6 @@
 torch==1.12.1+cu116
 ordered-set==4.1.0
 numpy==1.21.5
-einops==0.4.1
+einops==0.4.1
+pandas
+timm==0.9.16

run.sh

@@ -37,4 +37,7 @@ nohup python main.py --name ice00001 --lr 0.00001 --data icews14 --gpu 2 >run_lo
 PID:
 
 ___
-nohup python main.py --name ice001 --lr 0.001 --data icews14 --gpu 3 >run_log/0.001.log 2>&1 &
+nohup python main.py --name ice001 --lr 0.001 --data icews14 --gpu 3 >run_log/0.001.log 2>&1 &
+___
+nohup python main.py --name iceboth --data icews14_both --gpu 0 >run_log/iceboth.log 2>&1 &
+PID: 21984

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:
-                learning_curve.append(float(matched.group(2)))
+                this_epoch = int(matched.group(1))
-                if int(matched.group(1)) >= args.num_epochs:
+                if (this_epoch > max_epoch):
+                    learning_curve.append(float(matched.group(2)))
+                    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))