How to use the asreview.data.ASReviewData function in asreview

def test_no_seed():
    n_test_max = 100
    as_data = ASReviewData.from_file(data_fp)
    n_priored = np.zeros(len(as_data), dtype=int)

    for _ in range(n_test_max):
        reviewer = get_reviewer(
            data_fp, mode="simulate", model="nb", state_file=None,
            init_seed=None, n_prior_excluded=1, n_prior_included=1)
        assert len(reviewer.start_idx) == 2
        n_priored[reviewer.start_idx] += 1
        if np.all(n_priored > 0):
            return
    raise ValueError(f"Error getting all priors in {n_test_max} iterations.")

if isinstance(dataset, (str, PurePath)):
        dataset = [dataset]

    if isinstance(included_dataset, (str, PurePath)):
        included_dataset = [included_dataset]

    if isinstance(excluded_dataset, (str, PurePath)):
        excluded_dataset = [excluded_dataset]

    if isinstance(prior_dataset, (str, PurePath)):
        prior_dataset = [prior_dataset]

    as_data = ASReviewData()
    # Find the URL of the datasets if the dataset is an example dataset.
    for data in dataset:
        as_data.append(ASReviewData.from_file(find_data(data)))

    if new:
        as_data.labels = np.full((len(as_data),), LABEL_NA, dtype=int)
    for data in included_dataset:
        as_data.append(ASReviewData.from_file(
            find_data(data), data_type="included"))
    for data in excluded_dataset:
        as_data.append(ASReviewData.from_file(
            find_data(data), data_type="excluded"))
    for data in prior_dataset:
        as_data.append(ASReviewData.from_file(
            find_data(data), data_type="prior"))
    return as_data

def read_data(project_id):
    """Get ASReviewData object of the dataset"""
    dataset = get_data_file_path(project_id)
    return ASReviewData.from_file(dataset)

def create_as_data(dataset, included_dataset=[], excluded_dataset=[],
                   prior_dataset=[], new=False):
    """Create ASReviewData object from multiple datasets."""
    if isinstance(dataset, (str, PurePath)):
        dataset = [dataset]

    if isinstance(included_dataset, (str, PurePath)):
        included_dataset = [included_dataset]

    if isinstance(excluded_dataset, (str, PurePath)):
        excluded_dataset = [excluded_dataset]

    if isinstance(prior_dataset, (str, PurePath)):
        prior_dataset = [prior_dataset]

    as_data = ASReviewData()
    # Find the URL of the datasets if the dataset is an example dataset.
    for data in dataset:
        as_data.append(ASReviewData.from_file(find_data(data)))

    if new:
        as_data.labels = np.full((len(as_data),), LABEL_NA, dtype=int)
    for data in included_dataset:
        as_data.append(ASReviewData.from_file(
            find_data(data), data_type="included"))
    for data in excluded_dataset:
        as_data.append(ASReviewData.from_file(
            find_data(data), data_type="excluded"))
    for data in prior_dataset:
        as_data.append(ASReviewData.from_file(
            find_data(data), data_type="prior"))
    return as_data

Useful if some parts should be kept/thrown away.

        Arguments
        ---------
        idx: list, np.ndarray
            Record ids that should be kept.

        Returns
        -------
        ASReviewData:
            Slice of itself.
        """
        if self.df is None:
            raise ValueError("Cannot slice empty ASReviewData object.")

        return ASReviewData(self.df[idx], data_name="sliced")