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baseline performances: oracle KB, random and prior prob
This commit is contained in:
svlandeg
parent
24db1392b9
commit
6332af40de
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@ -5,11 +5,8 @@ import os
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import re
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import bz2
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import datetime
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from os import listdir
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from examples.pipeline.wiki_entity_linking import run_el
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from spacy.gold import GoldParse
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from spacy.matcher import PhraseMatcher
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from . import wikipedia_processor as wp, kb_creator
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"""
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@ -17,7 +14,7 @@ Process Wikipedia interlinks to generate a training dataset for the EL algorithm
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"""
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# ENTITY_FILE = "gold_entities.csv"
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ENTITY_FILE = "gold_entities_100000.csv" # use this file for faster processing
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ENTITY_FILE = "gold_entities_1000000.csv" # use this file for faster processing
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def create_training(entity_def_input, training_output):
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@ -58,7 +55,6 @@ def _process_wikipedia_texts(wp_to_id, training_output, limit=None):
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if cnt % 1000000 == 0:
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print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
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clean_line = line.strip().decode("utf-8")
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# print(clean_line)
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if clean_line == "<revision>":
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reading_revision = True
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@ -121,7 +117,6 @@ text_regex = re.compile(r'(?<=<text xml:space=\"preserve\">).*(?=</text)')
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def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_text, training_output):
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found_entities = False
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# print("Processing", article_id, article_title)
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# ignore meta Wikipedia pages
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if article_title.startswith("Wikipedia:"):
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@ -134,13 +129,8 @@ def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_te
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if text.startswith("#REDIRECT"):
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return
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# print()
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# print(text)
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# get the raw text without markup etc, keeping only interwiki links
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clean_text = _get_clean_wp_text(text)
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# print()
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# print(clean_text)
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# read the text char by char to get the right offsets of the interwiki links
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final_text = ""
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@ -295,68 +285,62 @@ def is_dev(article_id):
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return article_id.endswith("3")
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def read_training_entities(training_output, dev, limit):
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entityfile_loc = training_output + "/" + ENTITY_FILE
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entries_per_article = dict()
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article_ids = set()
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with open(entityfile_loc, mode='r', encoding='utf8') as file:
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for line in file:
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if not limit or len(article_ids) < limit:
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fields = line.replace('\n', "").split(sep='|')
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article_id = fields[0]
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if dev == is_dev(article_id) and article_id != "article_id":
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article_ids.add(article_id)
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alias = fields[1]
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wp_title = fields[2]
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start = fields[3]
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end = fields[4]
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entries_by_offset = entries_per_article.get(article_id, dict())
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entries_by_offset[start + "-" + end] = (alias, wp_title)
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entries_per_article[article_id] = entries_by_offset
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return entries_per_article
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def read_training(nlp, training_dir, dev, limit):
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# This method provides training examples that correspond to the entity annotations found by the nlp object
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print("reading training entities")
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entries_per_article = read_training_entities(training_output=training_dir, dev=dev, limit=limit)
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print("done reading training entities")
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entityfile_loc = training_dir + "/" + ENTITY_FILE
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data = []
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for article_id, entries_by_offset in entries_per_article.items():
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file_name = article_id + ".txt"
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try:
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# parse the article text
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with open(os.path.join(training_dir, file_name), mode="r", encoding='utf8') as file:
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text = file.read()
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article_doc = nlp(text)
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gold_entities = list()
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for ent in article_doc.ents:
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start = ent.start_char
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end = ent.end_char
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# we assume the data is written sequentially
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current_article_id = None
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current_doc = None
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gold_entities = list()
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ents_by_offset = dict()
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skip_articles = set()
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total_entities = 0
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entity_tuple = entries_by_offset.get(str(start) + "-" + str(end), None)
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if entity_tuple:
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alias, wp_title = entity_tuple
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if ent.text != alias:
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print("Non-matching entity in", article_id, start, end)
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else:
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gold_entities.append((start, end, wp_title))
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with open(entityfile_loc, mode='r', encoding='utf8') as file:
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for line in file:
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if not limit or len(data) < limit:
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if len(data) > 0 and len(data) % 50 == 0:
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print("Read", total_entities, "entities in", len(data), "articles")
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fields = line.replace('\n', "").split(sep='|')
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article_id = fields[0]
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alias = fields[1]
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wp_title = fields[2]
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start = fields[3]
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end = fields[4]
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if gold_entities:
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gold = GoldParse(doc=article_doc, links=gold_entities)
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data.append((article_doc, gold))
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if dev == is_dev(article_id) and article_id != "article_id" and article_id not in skip_articles:
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if not current_doc or (current_article_id != article_id):
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# store the data from the previous article
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if gold_entities and current_doc:
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gold = GoldParse(doc=current_doc, links=gold_entities)
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data.append((current_doc, gold))
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total_entities += len(gold_entities)
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except Exception as e:
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print("Problem parsing article", article_id)
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print(e)
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raise e
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# parse the new article text
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file_name = article_id + ".txt"
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try:
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with open(os.path.join(training_dir, file_name), mode="r", encoding='utf8') as f:
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text = f.read()
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current_doc = nlp(text)
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for ent in current_doc.ents:
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ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent.text
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except Exception as e:
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print("Problem parsing article", article_id, e)
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current_article_id = article_id
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gold_entities = list()
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# repeat checking this condition in case an exception was thrown
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if current_doc and (current_article_id == article_id):
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found_ent = ents_by_offset.get(start + "_" + end, None)
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if found_ent:
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if found_ent != alias:
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skip_articles.add(current_article_id)
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else:
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gold_entities.append((int(start), int(end), wp_title))
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print("Read", total_entities, "entities in", len(data), "articles")
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return data
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@ -64,7 +64,8 @@ def run_pipeline():
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to_test_pipeline = True
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# write the NLP object, read back in and test again
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test_nlp_io = False
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to_write_nlp = True
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to_read_nlp = True
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# STEP 1 : create prior probabilities from WP
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# run only once !
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@ -133,7 +134,7 @@ def run_pipeline():
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if train_pipe:
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print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
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train_limit = 10
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train_limit = 5
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dev_limit = 2
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train_data = training_set_creator.read_training(nlp=nlp_2,
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@ -166,46 +167,42 @@ def run_pipeline():
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)
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batchnr += 1
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except Exception as e:
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print("Error updating batch", e)
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print("Error updating batch:", e)
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raise(e)
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losses['entity_linker'] = losses['entity_linker'] / batchnr
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print("Epoch, train loss", itn, round(losses['entity_linker'], 2))
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if batchnr > 0:
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losses['entity_linker'] = losses['entity_linker'] / batchnr
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print("Epoch, train loss", itn, round(losses['entity_linker'], 2))
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dev_data = training_set_creator.read_training(nlp=nlp_2,
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training_dir=TRAINING_DIR,
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dev=True,
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limit=dev_limit)
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print("Dev testing on", len(dev_data), "articles")
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print()
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print("Dev testing on", len(dev_data), "articles")
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if len(dev_data) and measure_performance:
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print()
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print("STEP 7: performance measurement of Entity Linking pipe", datetime.datetime.now())
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print()
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acc_random, acc_random_by_label, acc_prior, acc_prior_by_label, acc_oracle, acc_oracle_by_label = _measure_baselines(dev_data, kb_2)
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print("dev acc oracle:", round(acc_oracle, 3), [(x, round(y, 3)) for x, y in acc_oracle_by_label.items()])
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print("dev acc random:", round(acc_random, 3), [(x, round(y, 3)) for x, y in acc_random_by_label.items()])
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print("dev acc prior:", round(acc_prior, 3), [(x, round(y, 3)) for x, y in acc_prior_by_label.items()])
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# print(" measuring accuracy 1-1")
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 1
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dev_acc_1_1, dev_acc_1_1_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc combo:", round(dev_acc_1_1, 3), [(x, round(y, 3)) for x, y in dev_acc_1_1_dict.items()])
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train_acc_1_1, train_acc_1_1_dict = _measure_accuracy(train_data, el_pipe)
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print("train acc combo:", round(train_acc_1_1, 3), [(x, round(y, 3)) for x, y in train_acc_1_1_dict.items()])
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# baseline using only prior probabilities
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el_pipe.context_weight = 0
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el_pipe.prior_weight = 1
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dev_acc_0_1, dev_acc_0_1_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc prior:", round(dev_acc_0_1, 3), [(x, round(y, 3)) for x, y in dev_acc_0_1_dict.items()])
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train_acc_0_1, train_acc_0_1_dict = _measure_accuracy(train_data, el_pipe)
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print("train acc prior:", round(train_acc_0_1, 3), [(x, round(y, 3)) for x, y in train_acc_0_1_dict.items()])
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dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc combo:", round(dev_acc_combo, 3), [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
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# using only context
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 0
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dev_acc_1_0, dev_acc_1_0_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc context:", round(dev_acc_1_0, 3), [(x, round(y, 3)) for x, y in dev_acc_1_0_dict.items()])
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train_acc_1_0, train_acc_1_0_dict = _measure_accuracy(train_data, el_pipe)
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print("train acc context:", round(train_acc_1_0, 3), [(x, round(y, 3)) for x, y in train_acc_1_0_dict.items()])
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dev_acc_context, dev_acc_1_0_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc context:", round(dev_acc_context, 3), [(x, round(y, 3)) for x, y in dev_acc_1_0_dict.items()])
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print()
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# reset for follow-up tests
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@ -219,7 +216,7 @@ def run_pipeline():
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run_el_toy_example(nlp=nlp_2)
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print()
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if test_nlp_io:
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if to_write_nlp:
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print()
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print("STEP 9: testing NLP IO", datetime.datetime.now())
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print()
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@ -229,9 +226,10 @@ def run_pipeline():
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print("reading from", NLP_2_DIR)
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nlp_3 = spacy.load(NLP_2_DIR)
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print()
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print("running toy example with NLP 2")
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run_el_toy_example(nlp=nlp_3)
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if to_read_nlp:
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print()
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print("running toy example with NLP 2")
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run_el_toy_example(nlp=nlp_3)
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print()
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print("STOP", datetime.datetime.now())
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@ -270,6 +268,80 @@ def _measure_accuracy(data, el_pipe):
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except Exception as e:
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print("Error assessing accuracy", e)
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acc, acc_by_label = calculate_acc(correct_by_label, incorrect_by_label)
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return acc, acc_by_label
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def _measure_baselines(data, kb):
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random_correct_by_label = dict()
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random_incorrect_by_label = dict()
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oracle_correct_by_label = dict()
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oracle_incorrect_by_label = dict()
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prior_correct_by_label = dict()
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prior_incorrect_by_label = dict()
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docs = [d for d, g in data if len(d) > 0]
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golds = [g for d, g in data if len(d) > 0]
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for doc, gold in zip(docs, golds):
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try:
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correct_entries_per_article = dict()
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for entity in gold.links:
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start, end, gold_kb = entity
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correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
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for ent in doc.ents:
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ent_label = ent.label_
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start = ent.start_char
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end = ent.end_char
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gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
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# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
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if gold_entity is not None:
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candidates = kb.get_candidates(ent.text)
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oracle_candidate = ""
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best_candidate = ""
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random_candidate = ""
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if candidates:
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scores = list()
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for c in candidates:
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scores.append(c.prior_prob)
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if c.entity_ == gold_entity:
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oracle_candidate = c.entity_
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best_index = scores.index(max(scores))
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best_candidate = candidates[best_index].entity_
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random_candidate = random.choice(candidates).entity_
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if gold_entity == best_candidate:
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prior_correct_by_label[ent_label] = prior_correct_by_label.get(ent_label, 0) + 1
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else:
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prior_incorrect_by_label[ent_label] = prior_incorrect_by_label.get(ent_label, 0) + 1
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if gold_entity == random_candidate:
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random_correct_by_label[ent_label] = random_correct_by_label.get(ent_label, 0) + 1
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else:
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random_incorrect_by_label[ent_label] = random_incorrect_by_label.get(ent_label, 0) + 1
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if gold_entity == oracle_candidate:
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oracle_correct_by_label[ent_label] = oracle_correct_by_label.get(ent_label, 0) + 1
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else:
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oracle_incorrect_by_label[ent_label] = oracle_incorrect_by_label.get(ent_label, 0) + 1
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except Exception as e:
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print("Error assessing accuracy", e)
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acc_prior, acc_prior_by_label = calculate_acc(prior_correct_by_label, prior_incorrect_by_label)
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acc_random, acc_random_by_label = calculate_acc(random_correct_by_label, random_incorrect_by_label)
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acc_oracle, acc_oracle_by_label = calculate_acc(oracle_correct_by_label, oracle_incorrect_by_label)
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return acc_random, acc_random_by_label, acc_prior, acc_prior_by_label, acc_oracle, acc_oracle_by_label
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def calculate_acc(correct_by_label, incorrect_by_label):
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acc_by_label = dict()
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total_correct = 0
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total_incorrect = 0
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@ -303,18 +375,25 @@ def run_el_toy_example(nlp):
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"The main character in Doug's novel is the man Arthur Dent, " \
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"but Douglas doesn't write about George Washington or Homer Simpson."
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doc = nlp(text)
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print(text)
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for ent in doc.ents:
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print("ent", ent.text, ent.label_, ent.kb_id_)
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print()
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# Q4426480 is her husband, Q3568763 her tutor
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text = "Ada Lovelace was the countess of Lovelace. She is known for her programming work on the analytical engine."\
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"Ada Lovelace loved her husband William King dearly. " \
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"Ada Lovelace was tutored by her favorite physics tutor William King."
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# Q4426480 is her husband
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text = "Ada Lovelace was the countess of Lovelace. She is known for her programming work on the analytical engine. "\
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"She loved her husband William King dearly. "
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doc = nlp(text)
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print(text)
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for ent in doc.ents:
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print("ent", ent.text, ent.label_, ent.kb_id_)
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print()
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# Q3568763 is her tutor
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text = "Ada Lovelace was the countess of Lovelace. She is known for her programming work on the analytical engine. "\
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"She was tutored by her favorite physics tutor William King."
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doc = nlp(text)
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print(text)
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for ent in doc.ents:
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print("ent", ent.text, ent.label_, ent.kb_id_)
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