# coding: utf-8 from __future__ import unicode_literals import random from spacy.util import minibatch, compounding from bin.wiki_entity_linking import training_set_creator, kb_creator, wikipedia_processor as wp from bin.wiki_entity_linking.kb_creator import DESC_WIDTH import spacy from spacy.kb import KnowledgeBase import datetime """ Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm. """ PRIOR_PROB = 'C:/Users/Sofie/Documents/data/wikipedia/prior_prob.csv' 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_1/kb' NLP_1_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_1' NLP_2_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_2' TRAINING_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_data_nel/' MAX_CANDIDATES = 10 MIN_ENTITY_FREQ = 20 MIN_PAIR_OCC = 5 EPOCHS = 10 DROPOUT = 0.1 LEARN_RATE = 0.005 L2 = 1e-6 def run_pipeline(): print("START", datetime.datetime.now()) print() nlp_1 = spacy.load('en_core_web_lg') nlp_2 = None kb_2 = None # one-time methods to create KB and write to file to_create_prior_probs = False to_create_entity_counts = False to_create_kb = False # read KB back in from file to_read_kb = True to_test_kb = False # create training dataset create_wp_training = False # train the EL pipe train_pipe = True measure_performance = True # test the EL pipe on a simple example to_test_pipeline = True # write the NLP object, read back in and test again to_write_nlp = False to_read_nlp = False # STEP 1 : create prior probabilities from WP # run only once ! if to_create_prior_probs: print("STEP 1: to_create_prior_probs", datetime.datetime.now()) wp.read_wikipedia_prior_probs(prior_prob_output=PRIOR_PROB) print() # STEP 2 : deduce entity frequencies from WP # run only once ! if to_create_entity_counts: print("STEP 2: to_create_entity_counts", datetime.datetime.now()) wp.write_entity_counts(prior_prob_input=PRIOR_PROB, count_output=ENTITY_COUNTS, to_print=False) print() # STEP 3 : create KB and write to file # run only once ! if to_create_kb: print("STEP 3a: to_create_kb", datetime.datetime.now()) kb_1 = kb_creator.create_kb(nlp_1, max_entities_per_alias=MAX_CANDIDATES, min_entity_freq=MIN_ENTITY_FREQ, min_occ=MIN_PAIR_OCC, entity_def_output=ENTITY_DEFS, entity_descr_output=ENTITY_DESCR, count_input=ENTITY_COUNTS, prior_prob_input=PRIOR_PROB, to_print=False) print("kb entities:", kb_1.get_size_entities()) print("kb aliases:", kb_1.get_size_aliases()) print() print("STEP 3b: write KB and NLP", datetime.datetime.now()) kb_1.dump(KB_FILE) nlp_1.to_disk(NLP_1_DIR) print() # STEP 4 : read KB back in from file if to_read_kb: print("STEP 4: to_read_kb", datetime.datetime.now()) nlp_2 = spacy.load(NLP_1_DIR) kb_2 = KnowledgeBase(vocab=nlp_2.vocab, entity_vector_length=DESC_WIDTH) kb_2.load_bulk(KB_FILE) print("kb entities:", kb_2.get_size_entities()) print("kb aliases:", kb_2.get_size_aliases()) print() # test KB if to_test_kb: check_kb(kb_2) print() # STEP 5: create a training dataset from WP if create_wp_training: print("STEP 5: create training dataset", datetime.datetime.now()) training_set_creator.create_training(entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR) # STEP 6: create and train the entity linking pipe el_pipe = nlp_2.create_pipe(name='entity_linker', config={}) el_pipe.set_kb(kb_2) nlp_2.add_pipe(el_pipe, last=True) other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"] with nlp_2.disable_pipes(*other_pipes): # only train Entity Linking optimizer = nlp_2.begin_training() optimizer.learn_rate = LEARN_RATE optimizer.L2 = L2 if train_pipe: print("STEP 6: training Entity Linking pipe", datetime.datetime.now()) train_limit = 25000 dev_limit = 5000 train_data = training_set_creator.read_training(nlp=nlp_2, training_dir=TRAINING_DIR, dev=False, limit=train_limit) print("Training on", len(train_data), "articles") print() dev_data = training_set_creator.read_training(nlp=nlp_2, training_dir=TRAINING_DIR, dev=True, limit=dev_limit) print("Dev testing on", len(dev_data), "articles") print() if not train_data: print("Did not find any training data") else: for itn in range(EPOCHS): random.shuffle(train_data) losses = {} batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001)) batchnr = 0 with nlp_2.disable_pipes(*other_pipes): for batch in batches: try: docs, golds = zip(*batch) nlp_2.update( docs, golds, sgd=optimizer, drop=DROPOUT, losses=losses, ) batchnr += 1 except Exception as e: print("Error updating batch:", e) if batchnr > 0: with el_pipe.model.use_params(optimizer.averages): el_pipe.context_weight = 1 el_pipe.prior_weight = 0 dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe) losses['entity_linker'] = losses['entity_linker'] / batchnr print("Epoch, train loss", itn, round(losses['entity_linker'], 2), " / dev acc context avg", round(dev_acc_context, 3)) # STEP 7: measure the performance of our trained pipe on an independent dev set if len(dev_data) and measure_performance: print() print("STEP 7: performance measurement of Entity Linking pipe", datetime.datetime.now()) print() counts, acc_r, acc_r_label, acc_p, acc_p_label, acc_o, acc_o_label = _measure_baselines(dev_data, kb_2) print("dev counts:", sorted(counts)) print("dev acc oracle:", round(acc_o, 3), [(x, round(y, 3)) for x, y in acc_o_label.items()]) print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_label.items()]) print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_label.items()]) with el_pipe.model.use_params(optimizer.averages): # measuring combined accuracy (prior + context) el_pipe.context_weight = 1 el_pipe.prior_weight = 1 dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe) print("dev acc combo avg:", round(dev_acc_combo, 3), [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()]) # using only context el_pipe.context_weight = 1 el_pipe.prior_weight = 0 dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe) print("dev acc context avg:", round(dev_acc_context, 3), [(x, round(y, 3)) for x, y in dev_acc_context_dict.items()]) print() # reset for follow-up tests el_pipe.context_weight = 1 el_pipe.prior_weight = 1 # STEP 8: apply the EL pipe on a toy example if to_test_pipeline: print() print("STEP 8: applying Entity Linking to toy example", datetime.datetime.now()) print() run_el_toy_example(nlp=nlp_2) print() # STEP 9: write the NLP pipeline (including entity linker) to file if to_write_nlp: print() print("STEP 9: testing NLP IO", datetime.datetime.now()) print() print("writing to", NLP_2_DIR) nlp_2.to_disk(NLP_2_DIR) print() print("reading from", NLP_2_DIR) nlp_3 = spacy.load(NLP_2_DIR) # verify that the IO has gone correctly if to_read_nlp: print() print("running toy example with NLP 2") run_el_toy_example(nlp=nlp_3) print() print("STOP", datetime.datetime.now()) def _measure_accuracy(data, el_pipe): correct_by_label = dict() incorrect_by_label = dict() docs = [d for d, g in data if len(d) > 0] docs = el_pipe.pipe(docs) golds = [g for d, g in data if len(d) > 0] for doc, gold in zip(docs, golds): try: correct_entries_per_article = dict() for entity in gold.links: start, end, gold_kb = entity correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb for ent in doc.ents: ent_label = ent.label_ pred_entity = ent.kb_id_ start = ent.start_char end = ent.end_char gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None) # the gold annotations are not complete so we can't evaluate missing annotations as 'wrong' if gold_entity is not None: if gold_entity == pred_entity: correct = correct_by_label.get(ent_label, 0) correct_by_label[ent_label] = correct + 1 else: incorrect = incorrect_by_label.get(ent_label, 0) incorrect_by_label[ent_label] = incorrect + 1 except Exception as e: print("Error assessing accuracy", e) acc, acc_by_label = calculate_acc(correct_by_label, incorrect_by_label) return acc, acc_by_label def _measure_baselines(data, kb): # Measure 3 performance baselines: random selection, prior probabilities, and 'oracle' prediction for upper bound counts_by_label = dict() random_correct_by_label = dict() random_incorrect_by_label = dict() oracle_correct_by_label = dict() oracle_incorrect_by_label = dict() prior_correct_by_label = dict() prior_incorrect_by_label = dict() docs = [d for d, g in data if len(d) > 0] golds = [g for d, g in data if len(d) > 0] for doc, gold in zip(docs, golds): try: correct_entries_per_article = dict() for entity in gold.links: start, end, gold_kb = entity correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb for ent in doc.ents: ent_label = ent.label_ start = ent.start_char end = ent.end_char gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None) # the gold annotations are not complete so we can't evaluate missing annotations as 'wrong' if gold_entity is not None: counts_by_label[ent_label] = counts_by_label.get(ent_label, 0) + 1 candidates = kb.get_candidates(ent.text) oracle_candidate = "" best_candidate = "" random_candidate = "" if candidates: scores = list() for c in candidates: scores.append(c.prior_prob) if c.entity_ == gold_entity: oracle_candidate = c.entity_ best_index = scores.index(max(scores)) best_candidate = candidates[best_index].entity_ random_candidate = random.choice(candidates).entity_ if gold_entity == best_candidate: prior_correct_by_label[ent_label] = prior_correct_by_label.get(ent_label, 0) + 1 else: prior_incorrect_by_label[ent_label] = prior_incorrect_by_label.get(ent_label, 0) + 1 if gold_entity == random_candidate: random_correct_by_label[ent_label] = random_correct_by_label.get(ent_label, 0) + 1 else: random_incorrect_by_label[ent_label] = random_incorrect_by_label.get(ent_label, 0) + 1 if gold_entity == oracle_candidate: oracle_correct_by_label[ent_label] = oracle_correct_by_label.get(ent_label, 0) + 1 else: oracle_incorrect_by_label[ent_label] = oracle_incorrect_by_label.get(ent_label, 0) + 1 except Exception as e: print("Error assessing accuracy", e) acc_prior, acc_prior_by_label = calculate_acc(prior_correct_by_label, prior_incorrect_by_label) acc_random, acc_random_by_label = calculate_acc(random_correct_by_label, random_incorrect_by_label) acc_oracle, acc_oracle_by_label = calculate_acc(oracle_correct_by_label, oracle_incorrect_by_label) return counts_by_label, acc_random, acc_random_by_label, acc_prior, acc_prior_by_label, acc_oracle, acc_oracle_by_label def calculate_acc(correct_by_label, incorrect_by_label): acc_by_label = dict() total_correct = 0 total_incorrect = 0 all_keys = set() all_keys.update(correct_by_label.keys()) all_keys.update(incorrect_by_label.keys()) for label in sorted(all_keys): correct = correct_by_label.get(label, 0) incorrect = incorrect_by_label.get(label, 0) total_correct += correct total_incorrect += incorrect if correct == incorrect == 0: acc_by_label[label] = 0 else: acc_by_label[label] = correct / (correct + incorrect) acc = 0 if not (total_correct == total_incorrect == 0): acc = total_correct / (total_correct + total_incorrect) return acc, acc_by_label def check_kb(kb): for mention in ("Bush", "Douglas Adams", "Homer", "Brazil", "China"): candidates = kb.get_candidates(mention) print("generating candidates for " + mention + " :") for c in candidates: print(" ", c.prior_prob, c.alias_, "-->", c.entity_ + " (freq=" + str(c.entity_freq) + ")") print() def run_el_toy_example(nlp): text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \ "Douglas reminds us to always bring our towel, even in China or Brazil. " \ "The main character in Doug's novel is the man Arthur Dent, " \ "but Douglas doesn't write about George Washington or Homer Simpson." doc = nlp(text) print(text) for ent in doc.ents: print("ent", ent.text, ent.label_, ent.kb_id_) print() # Q4426480 is her husband text = "Ada Lovelace was the countess of Lovelace. She's known for her programming work on the analytical engine. "\ "She loved her husband William King dearly. " doc = nlp(text) print(text) for ent in doc.ents: print("ent", ent.text, ent.label_, ent.kb_id_) print() # Q3568763 is her tutor text = "Ada Lovelace was the countess of Lovelace. She's known for her programming work on the analytical engine. "\ "She was tutored by her favorite physics tutor William King." doc = nlp(text) print(text) for ent in doc.ents: print("ent", ent.text, ent.label_, ent.kb_id_) if __name__ == "__main__": run_pipeline()