Minor fixes

README.md

@@ -14,7 +14,7 @@ This code implements the state-of-the-art Knowledge Graph Embedding [algorithms]
 * An example configuration file (complex.json) can be found in the repository.
 ### Specifications
 * "batch_size": Train Batch Size,
-* "entity_dim": Embedding Dimension (must be specified),
+* "ent_dim": Embedding Dimension (must be specified),
 * "exp_name": Experiment Name,
 * "is_dev": True if you want to test on validation data (must be specified),
 * "is_typed": True if you want to use Type Regularizer (default False),

constants.py

@@ -32,4 +32,4 @@
 
 fb13_ents=75043
 fb13_rels=13
-cat_file='/home/mitarb/kotnis/Code/kge-rl/entity_cat.cpkl'
+cat_file='./entity_cat.cpkl'

experiment_runner.py

@@ -72,7 +72,7 @@ def train(config,exp_name,data_path,resume=False,tune=False):
     state = None
     if resume or tune:
         params_path = os.path.join(results_dir, '{}_params.pt'.format(config['model']))
-        model.load_state_dict(torch.load(params_path))
+        model.load_state_dict(torch.load(params_path, map_location='cpu'))
     if resume:
         state_path = os.path.join(results_dir,'{}_optim_state.pt'.format(config['model']))
         state = torch.load(state_path)

negative_sampling.py

@@ -155,13 +155,13 @@ class Typed_Sampler(Static_Sampler):
     def __init__(self,triples,num_samples,results_dir,filtered=True):
         super(Typed_Sampler, self).__init__(triples,num_samples,filtered)
         print("\tNeg. Sampler: Typed, num_samples: {}, filtered: {}".format(num_samples, filtered))
-        self.ent_index = pickle.load(open(os.path.join(results_dir, constants.entity_ind)))
+        self.ent_index = pickle.load(open(os.path.join(results_dir, constants.entity_ind), 'rb'))
         self.ent_cats,self.cat_ents = self.load_cats()
 
 
     def load_cats(self):
         ent_cats, cat_ents = dict(),dict()
-        all_cats = pickle.load(open(constants.cat_file))
+        all_cats = pickle.load(open(constants.cat_file, 'rb'))
         for k,v in all_cats.iteritems():
             ent_cats[self.ent_index[k]] = v
             for c in v:
@@ -244,9 +244,9 @@ def get_entity(self,ex,is_target):
     def load_rescal(self):
         from models import Rescal
         import torch
-        path = "/home/mitarb/kotnis/Data/neg_sampling/wordnet/corrupt/rescal/rescal_100/rescal_params.pt"
+        path = "/home/sch/schoenitzer/nsp/kge-rl/data/FB15k/rescal-random-100/rescal_params.pt"
         model =  Rescal(constants.fb15k_ents,constants.fb15k_rels,100)
-        state_dict = torch.load(path)
+        state_dict = torch.load(path, map_location='cpu')
         model.load_state_dict(state_dict)
         model.eval()
         return model

setup.py

@@ -2,5 +2,5 @@
 from Cython.Build import cythonize
 
 setup(
-    ext_modules = cythonize("/home/kotnis/code/kge-rl/sample_list.pyx")
+    ext_modules = cythonize("./sample_list.pyx")
 )