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pedrojlazevedo
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Feb 29, 2020
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import jsonlines | ||
import json | ||
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train_file = "data/subsample_train.jsonl" | ||
train_relevant_file = "data/subsample_train_relevant_docs.jsonl" | ||
train_predictions_file = "predictions/predictions_train.jsonl" | ||
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train_file = jsonlines.open(train_file) | ||
train_relevant_file = jsonlines.open(train_relevant_file) | ||
train_predictions_file = jsonlines.open(train_predictions_file) | ||
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train_set = [] | ||
train_relevant = [] | ||
train_prediction = [] | ||
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for lines in train_file: | ||
lines['claim'] = lines['claim'].replace("-LRB-", " ( ") | ||
lines['claim'] = lines['claim'].replace("-RRB-", " ) ") | ||
train_set.append(lines) | ||
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for lines in train_relevant_file: | ||
lines['claim'] = lines['claim'].replace("-LRB-", " ( ") | ||
lines['claim'] = lines['claim'].replace("-RRB-", " ) ") | ||
train_relevant.append(lines) | ||
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# All claims | ||
stop = 0 | ||
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# List with dicts with all important data | ||
''' | ||
id : id of the claim | ||
verifiable : boolean of 1 and 0 with respective meaning | ||
docs : set of documents that verify the claim | ||
docs_sep : set of documents seperated | ||
sentences: list of tuples of <doc, line> | ||
''' | ||
gold_data = [] | ||
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for claim in train_set: | ||
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# init gold dict | ||
gold_dict = {'id': claim['id']} | ||
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if claim['verifiable'] == "VERIFIABLE": | ||
gold_dict['verifiable'] = 1 | ||
else: | ||
gold_dict['verifiable'] = 0 | ||
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# get gold inputs | ||
gold_documents = set() | ||
gold_documents_seperated = set() | ||
sentences_pair = set() | ||
evidences = claim['evidence'] | ||
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for evidence in evidences: | ||
doc_name = '' | ||
if len(evidence) > 1: # needs more than 1 doc to be verifiable | ||
for e in evidence: | ||
doc_name += str(e[2]) | ||
doc_name += " " | ||
sentences_pair.add((str(e[2]), str(e[3]))) # add gold sentences | ||
gold_documents_seperated.add(str(e[2])) # add the document | ||
doc_name = doc_name[:-1] # erase the last blank space | ||
else: | ||
doc_name = str(evidence[0][2]) | ||
gold_documents_seperated.add(str(evidence[0][2])) | ||
sentences_pair.add((str(evidence[0][2]), str(evidence[0][3]))) | ||
gold_documents.add(doc_name) | ||
gold_dict['docs'] = gold_documents | ||
gold_dict['evidences'] = sentences_pair | ||
gold_dict['docs_sep'] = gold_documents_seperated | ||
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gold_data.append(gold_dict) | ||
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# flag to stop if needed | ||
stop += 1 | ||
if stop == -1: | ||
break | ||
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gold_data = dict((item['id'], item) for item in gold_data) | ||
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stop = 0 | ||
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doc_found = 0 | ||
doc_noise = 0 | ||
gold_doc_found = 0 | ||
gold_doc_not_found = 0 | ||
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precision_correct = 0 | ||
precision_incorrect = 0 | ||
recall_correct = 0 | ||
recall_incorrect = 0 | ||
specificity = 0 | ||
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total_claim = 0 | ||
for claim in train_relevant: | ||
_id = claim['id'] | ||
gold_dict = gold_data.get(_id) | ||
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# no search is needed... no information on gold dict about retrieval | ||
if not gold_dict['verifiable']: | ||
continue | ||
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# document analysis | ||
doc_correct = 0 | ||
doc_incorrect = 0 | ||
gold_incorrect = 0 | ||
docs = set() | ||
gold_docs = gold_dict['docs_sep'] | ||
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for doc in claim['predicted_pages']: | ||
if doc in gold_docs: | ||
doc_correct += 1 | ||
else: | ||
doc_incorrect += 1 | ||
docs.add(doc) | ||
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precision_correct += doc_correct / len(docs) | ||
precision_incorrect += doc_incorrect / len(docs) | ||
recall_correct += doc_correct / len(gold_docs) | ||
recall_incorrect += doc_incorrect / len(gold_docs) | ||
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for gold_doc in gold_docs: | ||
if gold_doc not in docs: | ||
gold_incorrect += 1 | ||
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specificity += gold_incorrect / len(gold_docs) | ||
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if doc_correct > 0: | ||
doc_found += 1 | ||
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# sentence analysis TODO: check sentences | ||
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# TODO: create all possible pair in order to see if it appears in gold_dict['docs'] | ||
# claim['predicted_sentences'] | ||
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# flag to stop if needed | ||
total_claim += 1 | ||
stop += 1 | ||
if stop == -1: | ||
break | ||
precision_correct /= total_claim | ||
precision_incorrect /= total_claim | ||
recall_correct /= total_claim | ||
recall_incorrect /= total_claim | ||
specificity /= total_claim | ||
doc_found /= total_claim | ||
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print("Precision (Document Retrieved):\t\t\t\t\t\t " + str(precision_correct)) # precision | ||
print("Fall-out (incorrect documents):\t\t\t " + str(precision_incorrect)) # precision | ||
print("Recall (Relevant Documents):\t\t\t\t\t\t " + str(recall_correct)) # recall | ||
print("Percentage of gold documents NOT found:\t\t\t\t " + str(recall_incorrect)) # recall | ||
print("Fall-out: " + str(specificity)) | ||
print("Percentage of at least one document found correctly: " + str(doc_found)) # recall |
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