Ramon Meffert commited on
Commit
07cae66
1 Parent(s): 492106d

Remove old code

Browse files
Files changed (1) hide show
  1. main.py +0 -89
main.py CHANGED
@@ -125,92 +125,3 @@ if __name__ == '__main__':
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  os.makedirs("./results/", exist_ok=True)
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  f1_results.to_csv("./results/f1_scores.csv")
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  em_results.to_csv("./results/em_scores.csv")
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-
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- # TODO evaluation and storing of results
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-
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- # # Initialize retriever
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- # retriever = FaissRetriever(paragraphs)
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- # # retriever = ESRetriever(paragraphs)
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-
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- # # Retrieve example
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- # # random.seed(111)
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- # random_index = random.randint(0, len(questions_test["question"])-1)
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- # example_q = questions_test["question"][random_index]
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- # example_a = questions_test["answer"][random_index]
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-
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- # scores, result = retriever.retrieve(example_q)
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- # reader_input = context_to_reader_input(result)
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-
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- # # TODO: use new code from query.py to clean this up
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- # # Initialize reader
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- # answers = reader.read(example_q, reader_input)
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-
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- # # Calculate softmaxed scores for readable output
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- # sm = torch.nn.Softmax(dim=0)
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- # document_scores = sm(torch.Tensor(
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- # [pred.relevance_score for pred in answers]))
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- # span_scores = sm(torch.Tensor(
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- # [pred.span_score for pred in answers]))
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-
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- # print(example_q)
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- # for answer_i, answer in enumerate(answers):
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- # print(f"[{answer_i + 1}]: {answer.text}")
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- # print(f"\tDocument {answer.doc_id}", end='')
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- # print(f"\t(score {document_scores[answer_i] * 100:.02f})")
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- # print(f"\tSpan {answer.start_index}-{answer.end_index}", end='')
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- # print(f"\t(score {span_scores[answer_i] * 100:.02f})")
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- # print() # Newline
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-
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- # # print(f"Example q: {example_q} answer: {result['text'][0]}")
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-
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- # # for i, score in enumerate(scores):
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- # # print(f"Result {i+1} (score: {score:.02f}):")
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- # # print(result['text'][i])
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-
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- # # Determine best answer we want to evaluate
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- # highest, highest_index = 0, 0
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- # for i, value in enumerate(span_scores):
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- # if value + document_scores[i] > highest:
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- # highest = value + document_scores[i]
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- # highest_index = i
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-
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- # # Retrieve exact match and F1-score
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- # exact_match, f1_score = evaluate(
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- # example_a, answers[highest_index].text)
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- # print(f"Gold answer: {example_a}\n"
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- # f"Predicted answer: {answers[highest_index].text}\n"
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- # f"Exact match: {exact_match:.02f}\n"
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- # f"F1-score: {f1_score:.02f}")
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-
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- # Calculate overall performance
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- # total_f1 = 0
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- # total_exact = 0
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- # total_len = len(questions_test["question"])
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- # start_time = time.time()
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- # for i, question in enumerate(questions_test["question"]):
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- # print(question)
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- # answer = questions_test["answer"][i]
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- # print(answer)
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- #
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- # scores, result = retriever.retrieve(question)
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- # reader_input = result_to_reader_input(result)
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- # answers = reader.read(question, reader_input)
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- #
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- # document_scores = sm(torch.Tensor(
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- # [pred.relevance_score for pred in answers]))
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- # span_scores = sm(torch.Tensor(
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- # [pred.span_score for pred in answers]))
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- #
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- # highest, highest_index = 0, 0
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- # for j, value in enumerate(span_scores):
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- # if value + document_scores[j] > highest:
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- # highest = value + document_scores[j]
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- # highest_index = j
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- # print(answers[highest_index])
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- # exact_match, f1_score = evaluate(answer, answers[highest_index].text)
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- # total_f1 += f1_score
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- # total_exact += exact_match
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- # print(f"Total time:", round(time.time() - start_time, 2), "seconds.")
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- # print(total_f1)
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- # print(total_exact)
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- # print(total_f1/total_len)
 
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  os.makedirs("./results/", exist_ok=True)
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  f1_results.to_csv("./results/f1_scores.csv")
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  em_results.to_csv("./results/em_scores.csv")