def load_pandas_df()

in utils_nlp/dataset/squad.py [0:0]

• 62 lines of code
• 12 McCabe index (conditional complexity)

def load_pandas_df(local_cache_path=".", squad_version="v1.1", file_split="train"):
    """Loads the SQuAD dataset in pandas data frame.

    Args:
        local_cache_path (str, optional): Path to load the data from. If the file doesn't exist,
            download it first. Defaults to the current directory.
        squad_version (str, optional): Version of the SQuAD dataset, accepted values are: 
            "v1.1" and "v2.0". Defaults to "v1.1".
        file_split (str, optional): Dataset split to load, accepted values are: "train" and "dev".
            Defaults to "train".
    """

    if file_split not in ["train", "dev"]:
        raise ValueError("file_split should be either train or dev")

    URL = URL_DICT[squad_version][file_split]
    file_name = URL.split("/")[-1]
    maybe_download(URL, file_name, local_cache_path)

    file_path = os.path.join(local_cache_path, file_name)

    with open(file_path, "r", encoding="utf-8") as reader:
        input_data = json.load(reader)["data"]

    paragraph_text_list = []
    question_text_list = []
    answer_start_list = []
    answer_text_list = []
    qa_id_list = []
    is_impossible_list = []
    for entry in input_data:
        for paragraph in entry["paragraphs"]:
            paragraph_text = paragraph["context"]

            for qa in paragraph["qas"]:
                qas_id = qa["id"]
                question_text = qa["question"]
                answer_offset = None
                is_impossible = False

                if squad_version == "v2.0":
                    is_impossible = qa["is_impossible"]

                if file_split == "train":
                    if (len(qa["answers"]) != 1) and (not is_impossible):
                        raise ValueError(
                            "For training, each question should have exactly 1 answer."
                        )
                    if not is_impossible:
                        answer = qa["answers"][0]
                        orig_answer_text = answer["text"]
                        answer_offset = answer["answer_start"]
                    else:
                        orig_answer_text = ""
                else:
                    if not is_impossible:
                        orig_answer_text = []
                        answer_offset = []
                        for answer in qa["answers"]:
                            orig_answer_text.append(answer["text"])
                            answer_offset.append(answer["answer_start"])
                    else:
                        orig_answer_text = ""

                paragraph_text_list.append(paragraph_text)
                question_text_list.append(question_text)
                answer_start_list.append(answer_offset)
                answer_text_list.append(orig_answer_text)
                qa_id_list.append(qas_id)
                is_impossible_list.append(is_impossible)

    output_df = pd.DataFrame(
        {
            "doc_text": paragraph_text_list,
            "question_text": question_text_list,
            "answer_start": answer_start_list,
            "answer_text": answer_text_list,
            "qa_id": qa_id_list,
            "is_impossible": is_impossible_list,
        }
    )

    return output_df