Remove old code

main.py

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