Resolved pre-commit failures

seed_embeddings/OpenKE/generate_embedding_ray.py

@@ -24,6 +24,7 @@
 from ray.train import RunConfig, CheckpointConfig
 from ray.tune.schedulers import ASHAScheduler
 from ray.tune.search.optuna import OptunaSearch
+
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
 
@@ -207,7 +208,7 @@ def findRep(src, dest, index_dir, src_type="json"):
         "neg_rel": tune.randint(1, 30),
         "bern": tune.randint(0, 2),
         "opt_method": tune.choice(["SGD", "Adam"]),
-        #"opt_method": tune.choice(["SGD", "Adagrad", "Adam", "Adadelta"]),
+        # "opt_method": tune.choice(["SGD", "Adagrad", "Adam", "Adadelta"]),
         "is_analogy": arg_conf.is_analogy,
         "link_pred": arg_conf.link_pred,
         "use_gpu": arg_conf.use_gpu,
@@ -224,7 +225,7 @@ def findRep(src, dest, index_dir, src_type="json"):
         scheduler = ASHAScheduler(
             time_attr="training_iteration",
             max_t=arg_conf.epoch,
-            grace_period=15,                   
+            grace_period=15,
             reduction_factor=3,
             metric="AnalogiesScore",
             mode="max",
@@ -233,7 +234,7 @@ def findRep(src, dest, index_dir, src_type="json"):
         scheduler = ASHAScheduler(
             time_attr="training_iteration",
             max_t=arg_conf.epoch,
-            grace_period= 15,              
+            grace_period=15,
             reduction_factor=3,
             metric="hit1",
             mode="max",
@@ -242,26 +243,26 @@ def findRep(src, dest, index_dir, src_type="json"):
         scheduler = ASHAScheduler(
             time_attr="training_iteration",
             max_t=arg_conf.epoch,
-            grace_period= 15,       
-            reduction_factor=3,   
+            grace_period=15,
+            reduction_factor=3,
             metric="loss",
             mode="min",
         )
     if arg_conf.use_gpu:
         train_with_resources = tune.with_resources(
-            train, resources={"cpu": 0, "gpu": 0.0625}                                           
+            train, resources={"cpu": 0, "gpu": 0.0625}
         )
     else:
         train_with_resources = tune.with_resources(
             train, resources={"cpu": 10, "gpu": 0}
         )
-    
+
     tuner = tune.Tuner(
         train_with_resources,
         param_space=search_space,
         tune_config=TuneConfig(
-            search_alg=OptunaSearch(metric="loss",mode="min"),
-            max_concurrent_trials=16, 
+            search_alg=OptunaSearch(metric="loss", mode="min"),
+            max_concurrent_trials=16,
             scheduler=scheduler,
             num_samples=512,
         ),
@@ -275,7 +276,7 @@ def findRep(src, dest, index_dir, src_type="json"):
         ),
     )
     results = tuner.fit()
-    
+
     # Write the best result to a file, best_result.txt
     best_result = None
     if arg_conf.is_analogy:
@@ -293,24 +294,20 @@ def findRep(src, dest, index_dir, src_type="json"):
         best_result = results.get_best_result(metric="AnalogiesScore", mode="max")
 
     elif arg_conf.link_pred:
-        
+
         with open(os.path.join(search_space["index_dir"], "best_result.txt"), "a") as f:
-            f.write(
-                "\n" + str(results.get_best_result(metric="hit1", mode="max"))
-            )
-
+            f.write("\n" + str(results.get_best_result(metric="hit1", mode="max")))
+
         print(
             "Best Config Based on Hit1 : ",
             results.get_best_result(metric="hit1", mode="max"),
         )
         best_result = results.get_best_result(metric="hit1", mode="max")
     else:
-        
+
         with open(os.path.join(search_space["index_dir"], "best_result.txt"), "a") as f:
-            f.write(
-                "\n" + str(results.get_best_result(metric="loss", mode="min"))
-            )
-
+            f.write("\n" + str(results.get_best_result(metric="loss", mode="min")))
+
         print(
             "Best Config Based on Loss : ",
             results.get_best_result(metric="loss", mode="min"),
@@ -338,9 +335,9 @@ def findRep(src, dest, index_dir, src_type="json"):
         ),
     )
     best_checkpoint_path = best_result.checkpoint.path
-    print("best_checkpoint_path is: ",best_checkpoint_path)
+    print("best_checkpoint_path is: ", best_checkpoint_path)
     file_name = os.listdir(best_checkpoint_path)[0]
-    print("file_name is: ",file_name)
+    print("file_name is: ", file_name)
     if file_name.endswith(".ckpt"):
         # Construct full file path
         source_file = os.path.join(best_checkpoint_path, file_name)
@@ -357,7 +354,7 @@ def findRep(src, dest, index_dir, src_type="json"):
                 margin,
             ),
         )
-        print("embeddings_path: ",embeddings_path)
+        print("embeddings_path: ", embeddings_path)
         findRep(outfile, embeddings_path, index_dir, src_type="ckpt")
     else:
         print("No .ckpt file found in the source directory.")
@@ -366,4 +363,4 @@ def findRep(src, dest, index_dir, src_type="json"):
         print(result)
     del results
 
-    print("Training finished...")
+    print("Training finished...")