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update viz util

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This commit is contained in:
Cong Thanh Vu 2023-06-24 02:59:32 +00:00
parent 53443a4026
commit 45ce0c995b
73 changed files with 1374675 additions and 304886 deletions
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15
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# triples: 86517
# entities: 7128
# relations: 12409
# timesteps: 208
# test triples: 8218
# valid triples: 8193
# train triples: 70106
Measure method: N/A
Target Size : 0
Grow Factor: 0
Shrink Factor: 0
Epsilon Factor: 0
Search method: N/A
filter_dupes: both
nonames: False

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# triples: 231529
# entities: 12554
# relations: 423
# timesteps: 70
# test triples: 16195
# valid triples: 16707
# train triples: 198627
Measure method: N/A
Target Size : 423
Grow Factor: 0
Shrink Factor: 4.0
Epsilon Factor: 0
Search method: N/A
filter_dupes: both
nonames: False

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0 P131[0-0]
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60 P131[60-60]
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63 P131[63-63]
64 P131[64-64]
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80 P39[58-58]
81 P39[59-59]
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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]
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113 P54[61-61]
114 P54[62-62]
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157 P31[35-35]
158 P31[36-36]
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182 P31[60-60]
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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]
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201 P463[35-35]
202 P463[36-36]
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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]
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243 P108[29-36]
244 P39[0-3]
245 P17[47-48]
246 P166[21-23]
247 P793[46-69]
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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]
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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]
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292 P26[62-69]
293 P1411[56-58]
294 P1435[37-38]
295 P166[60-61]
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305 P39[19-22]
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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]
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343 P26[0-19]
344 P1435[17-24]
345 P39[23-26]
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352 P579[24-35]
353 P190[62-65]
354 P1435[53-54]
355 P1376[0-18]
356 P27[0-14]
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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]
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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]
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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]

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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

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# 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

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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

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0 P131[0-0]
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62 P131[62-62]
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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]

19271
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71
data/wikidata12k_old/time_map.dict Normal file
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@ -0,0 +1,71 @@
0 19 19
1 20 1643
2 1644 1790
3 1791 1816
4 1817 1855
5 1856 1871
6 1872 1893
7 1894 1905
8 1906 1913
9 1914 1918
10 1919 1920
11 1921 1924
12 1925 1929
13 1930 1933
14 1934 1937
15 1938 1941
16 1942 1945
17 1946 1948
18 1949 1950
19 1951 1953
20 1954 1956
21 1957 1959
22 1960 1961
23 1962 1963
24 1964 1965
25 1966 1967
26 1968 1968
27 1969 1970
28 1971 1972
29 1973 1974
30 1975 1976
31 1977 1978
32 1979 1980
33 1981 1982
34 1983 1983
35 1984 1984
36 1985 1985
37 1986 1986
38 1987 1987
39 1988 1988
40 1989 1989
41 1990 1990
42 1991 1991
43 1992 1992
44 1993 1993
45 1994 1994
46 1995 1995
47 1996 1996
48 1997 1997
49 1998 1998
50 1999 1999
51 2000 2000
52 2001 2001
53 2002 2002
54 2003 2003
55 2004 2004
56 2005 2005
57 2006 2006
58 2007 2007
59 2008 2008
60 2009 2009
61 2010 2010
62 2011 2011
63 2012 2012
64 2013 2013
65 2014 2014
66 2015 2015
67 2016 2016
68 2017 2017
69 2018 2020
70 2021 2021

252339
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15
data/yago/about.txt Normal file
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@ -0,0 +1,15 @@
# 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

10526
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data/yago/relations.dict Normal file
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@ -0,0 +1,177 @@
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]

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data/yago/time_map.dict Normal file
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@ -0,0 +1,60 @@
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

63925
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data/yago11k/indices_test.txt Normal file
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@ -0,0 +1,793 @@
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
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606
607
613
614
616
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618
619
621
623
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628
638
641
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648
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659
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663
664
676
677
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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
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7
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15
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# 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: both
nonames: False

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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]

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data/yago11k_both/time_map.dict Normal file
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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

63925
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22
log/fb_one_to_x
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@ -1964,3 +1964,25 @@
2023-05-04 08:27:31,384 - fb_one_to_x - [INFO] - [E:34| 1500]: Train Loss:0.0027362, Val MRR:0.33574, fb_one_to_x 2023-05-04 08:27:31,384 - fb_one_to_x - [INFO] - [E:34| 1500]: Train Loss:0.0027362, Val MRR:0.33574, fb_one_to_x
2023-05-04 08:29:20,404 - fb_one_to_x - [INFO] - [E:34| 1600]: Train Loss:0.0027362, Val MRR:0.33574, fb_one_to_x 2023-05-04 08:29:20,404 - fb_one_to_x - [INFO] - [E:34| 1600]: Train Loss:0.0027362, Val MRR:0.33574, fb_one_to_x
2023-05-04 08:31:12,139 - fb_one_to_x - [INFO] - [E:34| 1700]: Train Loss:0.0027362, Val MRR:0.33574, fb_one_to_x 2023-05-04 08:31:12,139 - fb_one_to_x - [INFO] - [E:34| 1700]: Train Loss:0.0027362, Val MRR:0.33574, fb_one_to_x
2023-05-04 08:55:56,065 - fb_one_to_x - [INFO] - {'dataset': 'FB15k-237', 'name': 'fb_one_to_x', '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': 0.1, 'drop': 0.2, '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}
2023-05-04 08:56:07,953 - fb_one_to_x - [INFO] - [Test, Tail_Batch Step 0] fb_one_to_x
2023-05-04 08:56:53,173 - fb_one_to_x - [INFO] - [Test, Tail_Batch Step 100] fb_one_to_x
2023-05-04 08:57:20,187 - fb_one_to_x - [INFO] - [Test, Head_Batch Step 0] fb_one_to_x
2023-05-04 08:58:08,090 - fb_one_to_x - [INFO] - [Test, Head_Batch Step 100] fb_one_to_x
2023-05-04 08:58:36,338 - fb_one_to_x - [INFO] - [Evaluating Epoch 0 test]:
MRR: Tail : 0.43029, Head : 0.23256, Avg : 0.33142
MR: Tail : 149.91, Head : 288.48, Avg : 219.2
Hit-1: Tail : 0.33563, Head : 0.14883, Avg : 0.24223
Hit-3: Tail : 0.47068, Head : 0.25515, Avg : 0.36292
Hit-10: Tail : 0.61952, Head : 0.40096, Avg : 0.51024
2023-05-04 09:03:55,555 - fb_one_to_x - [INFO] - {'dataset': 'FB15k-237', 'name': 'fb_one_to_x', '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': 0.1, 'drop': 0.2, '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}
2023-05-04 09:04:07,491 - fb_one_to_x - [INFO] - [Test, Tail_Batch Step 0] fb_one_to_x
2023-05-04 09:04:52,620 - fb_one_to_x - [INFO] - [Test, Tail_Batch Step 100] fb_one_to_x
2023-05-04 09:05:19,645 - fb_one_to_x - [INFO] - [Test, Head_Batch Step 0] fb_one_to_x
2023-05-04 09:06:07,591 - fb_one_to_x - [INFO] - [Test, Head_Batch Step 100] fb_one_to_x
2023-05-04 09:06:35,660 - fb_one_to_x - [INFO] - [Evaluating Epoch 0 test]:
MRR: Tail : 0.43029, Head : 0.23256, Avg : 0.33142
MR: Tail : 149.91, Head : 288.48, Avg : 219.2
Hit-1: Tail : 0.33563, Head : 0.14883, Avg : 0.24223
Hit-3: Tail : 0.47068, Head : 0.25515, Avg : 0.36292
Hit-10: Tail : 0.61952, Head : 0.40096, Avg : 0.51024

27
main.py
View File

@ -3,6 +3,7 @@ import uuid
import argparse import argparse
import logging import logging
import logging.config import logging.config
import time
import torch import torch
import numpy as np import numpy as np
@ -76,7 +77,7 @@ class Main(object):
ent_set, rel_set = OrderedSet(), OrderedSet() ent_set, rel_set = OrderedSet(), OrderedSet()
for split in ['train', 'test', 'valid']: for split in ['train', 'test', 'valid']:
for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)): for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)):
sub, rel, obj = map(str.lower, line.strip().split('\t')) sub, rel, obj, *_ = map(str.lower, line.strip().split('\t'))
ent_set.add(sub) ent_set.add(sub)
rel_set.add(rel) rel_set.add(rel)
ent_set.add(obj) ent_set.add(obj)
@ -107,14 +108,20 @@ class Main(object):
sr2o = ddict(set) sr2o = ddict(set)
for split in ['train', 'test', 'valid']: for split in ['train', 'test', 'valid']:
for line in open('./data/{}/{}.txt'.format(self.p.dataset, split)): samples = 0
sub, rel, obj = map(str.lower, line.strip().split('\t')) for i, line in enumerate(open('./data/{}/{}.txt'.format(self.p.dataset, split))):
sub, rel, obj, rel_type, *_ = map(str.lower, line.strip().split('\t'))
if (split == 'test' and self.p.rel_type is not None):
if rel_type != self.p.rel_type:
continue
sub, rel, obj = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj] sub, rel, obj = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj]
self.data[split].append((sub, rel, obj)) self.data[split].append((sub, rel, obj))
if split == 'train': if split == 'train':
sr2o[(sub, rel)].add(obj) sr2o[(sub, rel)].add(obj)
sr2o[(obj, rel+self.p.num_rel)].add(sub) sr2o[(obj, rel+self.p.num_rel)].add(sub)
samples += 1
print(split.capitalize() + ': ' + str(samples) + ' samples')
self.data = dict(self.data) self.data = dict(self.data)
self.sr2o = {k: list(v) for k, v in sr2o.items()} self.sr2o = {k: list(v) for k, v in sr2o.items()}
@ -152,6 +159,8 @@ class Main(object):
{'triple': (obj, rel_inv, sub), 'label': self.sr2o_all[(obj, rel_inv)]}) {'triple': (obj, rel_inv, sub), 'label': self.sr2o_all[(obj, rel_inv)]})
self.triples = dict(self.triples) 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): def get_data_loader(dataset_class, split, batch_size, shuffle=True):
return DataLoader( return DataLoader(
@ -626,6 +635,7 @@ if __name__ == "__main__":
parser.add_argument('--test_only', action='store_true', default=False) parser.add_argument('--test_only', action='store_true', default=False)
parser.add_argument('--grid_search', 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() args = parser.parse_args()
@ -690,4 +700,13 @@ if __name__ == "__main__":
model.load_model(save_path) model.load_model(save_path)
model.evaluate('test') model.evaluate('test')
else: else:
model.fit() while True:
try:
model.fit()
except Exception as e:
print(e)
time.sleep(30)
del model
model = Main(args)
continue
break

14
run.sh
View File

@ -24,3 +24,17 @@ PID: 4503
test: testrun_d542676f test: testrun_d542676f
--- ---
nohup python main.py --gpu 3 --data WN18RR --drop 0.0 --drop_path 0.0 >run_log/fnet-wn.log 2>&1 & nohup python main.py --gpu 3 --data WN18RR --drop 0.0 --drop_path 0.0 >run_log/fnet-wn.log 2>&1 &
---
nohup python main.py --name ice0003 --lr 0.0003 --data icews14 --gpu 1 >run_log/ice0003.log 2>&1 &
PID: 3076
tail -f -n 200 run_log/ice0003.log
---
nohup python main.py --name ice0003_2 --lr 0.00003 --data icews14 --gpu 3 >run_log/ice0003_2.log 2>&1 &
PID: 3390
tail -f -n 200 run_log/ice0003_2.log
---
nohup python main.py --name ice00001 --lr 0.00001 --data icews14 --gpu 2 >run_log/ice00001.log 2>&1 &
PID:
___
nohup python main.py --name ice001 --lr 0.001 --data icews14 --gpu 3 >run_log/0.001.log 2>&1 &

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run_log/icews14/0.0001.out Normal file
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425
run_log/wikidata12k/0.001.log Normal file
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@ -0,0 +1,425 @@
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

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import argparse
import re
import os
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
def extract_learning_curves(args):
paths = args.log_path.split(',')
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 = {}
for path in paths:
learning_curve = []
lines = open(path, 'r').readlines()
for line in lines:
matched = re.match(r'[0-9\- :,]*\[INFO\] - \[Epoch ([0-9]+)\].*Valid MRR: ([0-9\.]+).*', line)
if matched:
learning_curve.append(float(matched.group(2)))
if int(matched.group(1)) >= args.num_epochs:
break
learning_curves[os.path.basename(path)] = learning_curve
return learning_curves
def draw_learning_curves(args, learning_curves):
for name in learning_curves.keys():
epochs = np.arange(len(learning_curves[name]))
matched = re.match(r'(.*)\..*', name)
if matched:
label = matched.group(1)
else:
label = name
plt.plot(epochs, learning_curves[name], label = label)
plt.xlabel("Epochs")
plt.ylabel("MRR")
plt.legend(title=args.legend_title)
plt.savefig(os.path.join(args.out_path, str(round(datetime.utcnow().timestamp() * 1000)) + '.' + args.fig_filetype))
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Parser For Arguments", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--task', default = None, type=str)
parser.add_argument('--log_path', type=str, default=None)
parser.add_argument('--out_path', type=str, default=None)
parser.add_argument('--num_epochs', type=int, default=200)
parser.add_argument('--legend_title', type=str, default="Learning rate")
parser.add_argument('--fig_filetype', type=str, default="svg")
args = parser.parse_args()
if (args.task == 'learning_curve'):
draw_learning_curves(args, extract_learning_curves(args))

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

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