regenerating KB

examples/pipeline/wiki_entity_linking/kb_creator.py

@@ -14,6 +14,7 @@ from . import wikidata_processor as wd
 INPUT_DIM = 300  # dimension of pre-trained vectors
 DESC_WIDTH = 64
 
+
 def create_kb(nlp, max_entities_per_alias, min_occ,
               entity_def_output, entity_descr_output,
               count_input, prior_prob_input, to_print=False):
@@ -25,8 +26,7 @@ def create_kb(nlp, max_entities_per_alias, min_occ,
 
     if read_raw_data:
         print()
-        print("1. _read_wikidata_entities", datetime.datetime.now())
+        print(" * _read_wikidata_entities", datetime.datetime.now())
-        print()
         title_to_id, id_to_descr = wd.read_wikidata_entities_json(limit=None)
 
         # write the title-ID and ID-description mappings to file
@@ -40,8 +40,8 @@ def create_kb(nlp, max_entities_per_alias, min_occ,
     title_list = list(title_to_id.keys())
 
     # TODO: remove this filter (just for quicker testing of code)
-    title_list = title_list[0:342]
+    # title_list = title_list[0:342]
-    title_to_id = {t: title_to_id[t] for t in title_list}
+    # title_to_id = {t: title_to_id[t] for t in title_list}
 
     entity_list = [title_to_id[x] for x in title_list]
 
@@ -49,29 +49,28 @@ def create_kb(nlp, max_entities_per_alias, min_occ,
     description_list = [id_to_descr.get(x, "No description defined") for x in entity_list]
 
     print()
-    print("2. _get_entity_frequencies", datetime.datetime.now())
+    print(" * _get_entity_frequencies", datetime.datetime.now())
     print()
     entity_frequencies = wp.get_entity_frequencies(count_input=count_input, entities=title_list)
 
     print()
-    print("3. train entity encoder", datetime.datetime.now())
+    print(" * train entity encoder", datetime.datetime.now())
     print()
 
     encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
     encoder.train(description_list=description_list, to_print=True)
     print()
 
-    print("4. get entity embeddings", datetime.datetime.now())
+    print(" * get entity embeddings", datetime.datetime.now())
     print()
     embeddings = encoder.apply_encoder(description_list)
 
     print()
-    print("5. adding", len(entity_list), "entities", datetime.datetime.now())
+    print(" * adding", len(entity_list), "entities", datetime.datetime.now())
-    print()
     kb.set_entities(entity_list=entity_list, prob_list=entity_frequencies, vector_list=embeddings)
 
     print()
-    print("6. adding aliases", datetime.datetime.now())
+    print(" * adding aliases", datetime.datetime.now())
     print()
     _add_aliases(kb, title_to_id=title_to_id,
                  max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,

examples/pipeline/wiki_entity_linking/train_descriptions.py

@@ -17,7 +17,7 @@ class EntityEncoder:
 
     DROP = 0
     EPOCHS = 5
-    STOP_THRESHOLD = 0.1
+    STOP_THRESHOLD = 0.04
 
     BATCH_SIZE = 1000
 
@@ -127,7 +127,7 @@ class EntityEncoder:
         return loss, gradients
 
     def _test_encoder(self):
-        """ Test encoder on some dummy examples """
+        # Test encoder on some dummy examples
         desc_A1 = "Fictional character in The Simpsons"
         desc_A2 = "Simpsons - fictional human"
         desc_A3 = "Fictional character in The Flintstones"

examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py

@@ -22,7 +22,7 @@ ENTITY_COUNTS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_freq.csv'
 ENTITY_DEFS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_defs.csv'
 ENTITY_DESCR = 'C:/Users/Sofie/Documents/data/wikipedia/entity_descriptions.csv'
 
-KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb'
+KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb_1/kb'
 NLP_1_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_1'
 NLP_2_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_2'
 
@@ -56,14 +56,14 @@ def run_pipeline():
     create_wp_training = False
 
     # train the EL pipe
-    train_pipe = True
+    train_pipe = False
     measure_performance = False
 
     # test the EL pipe on a simple example
-    to_test_pipeline = True
+    to_test_pipeline = False
 
     # write the NLP object, read back in and test again
-    test_nlp_io = True
+    test_nlp_io = False
 
     # STEP 1 : create prior probabilities from WP
     # run only once !