auto retry

data/icews14_both/about.txt

@@ -0,0 +1,15 @@
+# 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

data/icews14_both/time_map.dict

@@ -0,0 +1,209 @@
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data/wikidata12k_both/about.txt

@@ -0,0 +1,15 @@
+# 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

data/wikidata12k_both/relations.dict

@@ -0,0 +1,423 @@
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data/wikidata12k_both/time_map.dict

@@ -0,0 +1,71 @@
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data/yago11k_both/about.txt

@@ -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: both
+nonames: False

data/yago11k_both/relations.dict

@@ -0,0 +1,177 @@
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data/yago11k_both/time_map.dict

@@ -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

main.py

@@ -6,6 +6,7 @@ import logging.config
 
 import torch
 import numpy as np
+import time
 
 from collections import defaultdict as ddict
 from pprint import pprint
@@ -21,7 +22,7 @@ from models import ComplEx, ConvE, HypER, InteractE, FouriER, TuckER
 
 class Main(object):
 
-    def __init__(self, params):
+    def __init__(self, params, logger):
         """
         Constructor of the runner class
         Parameters
@@ -34,11 +35,9 @@ 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')
@@ -76,7 +75,7 @@ class Main(object):
         ent_set, rel_set = OrderedSet(), OrderedSet()
         for split in ['train', 'test', 'valid']:
             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)
                 rel_set.add(rel)
                 ent_set.add(obj)
@@ -108,7 +107,7 @@ class Main(object):
 
         for split in ['train', 'test', 'valid']:
             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'))
                 sub, rel, obj = self.ent2id[sub], self.rel2id[rel], self.ent2id[obj]
                 self.data[split].append((sub, rel, obj))
 
@@ -634,9 +633,10 @@ 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
 
@@ -685,9 +685,23 @@ 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:
-        model.fit()
+        while True:
+            try:
+                model = Main(args, logger)
+                model.fit()
+            except Exception as e:
+                try:
+                    del model
+                except Exception:
+                    pass
+                time.sleep(30)
+                continue
+            break

run.sh

@@ -24,3 +24,20 @@ PID: 4503
 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 --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 &
+___
+nohup python main.py --name iceboth --data icews14_both --gpu 0 >run_log/iceboth.log 2>&1 &
+PID: 12416

visualization_util.py

@@ -0,0 +1,61 @@
+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 = {}
+    print(paths)
+    for path in paths:
+        print(path)
+        learning_curve = []
+        lines = open(path, 'r').readlines()
+        max_epoch = -1
+        for line in lines:
+            matched = re.match(r'[0-9\- :,]*\[INFO\] - \[Epoch ([0-9]+)\].*Valid MRR: ([0-9\.]+).*', line)
+            if matched:
+                this_epoch = int(matched.group(1))
+                if (this_epoch > max_epoch):
+                    learning_curve.append(float(matched.group(2)))
+                    max_epoch = this_epoch
+                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
+
+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))