keras/src/datasets/reuters.py/load_data
def load_data(
    path="reuters.npz",
    num_words=None,
    skip_top=0,
    maxlen=None,
    test_split=0.2,
    seed=113,
    start_char=1,
    oov_char=2,
    index_from=3,
):
    """Loads the Reuters newswire classification dataset.

    This is a dataset of 11,228 newswires from Reuters, labeled over 46 topics.

    This was originally generated by parsing and preprocessing the classic
    Reuters-21578 dataset, but the preprocessing code is no longer packaged
    with Keras. See this
    [GitHub discussion](https://github.com/keras-team/keras/issues/12072)
    for more info.

    Each newswire is encoded as a list of word indexes (integers).
    For convenience, words are indexed by overall frequency in the dataset,
    so that for instance the integer "3" encodes the 3rd most frequent word in
    the data. This allows for quick filtering operations such as:
    "only consider the top 10,000 most
    common words, but eliminate the top 20 most common words".

    As a convention, "0" does not stand for a specific word, but instead is used
    to encode any unknown word.

    Args:
        path: where to cache the data (relative to `~/.keras/dataset`).
        num_words: integer or None. Words are
            ranked by how often they occur (in the training set) and only
            the `num_words` most frequent words are kept. Any less frequent word
            will appear as `oov_char` value in the sequence data. If None,
            all words are kept. Defaults to `None`.
        skip_top: skip the top N most frequently occurring words
            (which may not be informative). These words will appear as
            `oov_char` value in the dataset. 0 means no words are
            skipped. Defaults to `0`.
        maxlen: int or None. Maximum sequence length.
            Any longer sequence will be truncated. None means no truncation.
            Defaults to `None`.
        test_split: Float between `0.` and `1.`. Fraction of the dataset to be
            used as test data. `0.2` means that 20% of the dataset is used as
            test data. Defaults to `0.2`.
        seed: int. Seed for reproducible data shuffling.
        start_char: int. The start of a sequence will be marked with this
            character. 0 is usually the padding character. Defaults to `1`.
        oov_char: int. The out-of-vocabulary character.
            Words that were cut out because of the `num_words` or
            `skip_top` limits will be replaced with this character.
        index_from: int. Index actual words with this index and higher.

    Returns:
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

    **`x_train`, `x_test`**: lists of sequences, which are lists of indexes
      (integers). If the num_words argument was specific, the maximum
      possible index value is `num_words - 1`. If the `maxlen` argument was
      specified, the largest possible sequence length is `maxlen`.

    **`y_train`, `y_test`**: lists of integer labels (1 or 0).

    **Note**: The 'out of vocabulary' character is only used for
    words that were present in the training set but are not included
    because they're not making the `num_words` cut here.
    Words that were not seen in the training set but are in the test set
    have simply been skipped.
    """
    origin_folder = (
        "https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
    )
    path = get_file(
        fname=path,
        origin=origin_folder + "reuters.npz",
        file_hash=(  # noqa: E501
            "d6586e694ee56d7a4e65172e12b3e987c03096cb01eab99753921ef915959916"
        ),
    )
    with np.load(path, allow_pickle=True) as f:
        xs, labels = f["x"], f["y"]

    rng = np.random.RandomState(seed)
    indices = np.arange(len(xs))
    rng.shuffle(indices)
    xs = xs[indices]
    labels = labels[indices]

    if start_char is not None:
        xs = [[start_char] + [w + index_from for w in x] for x in xs]
    elif index_from:
        xs = [[w + index_from for w in x] for x in xs]

    if maxlen:
        xs, labels = remove_long_seq(maxlen, xs, labels)

    if not num_words:
        num_words = max(max(x) for x in xs)

    # by convention, use 2 as OOV word
    # reserve 'index_from' (=3 by default) characters:
    # 0 (padding), 1 (start), 2 (OOV)
    if oov_char is not None:
        xs = [
            [w if skip_top <= w < num_words else oov_char for w in x]
            for x in xs
        ]
    else:
        xs = [[w for w in x if skip_top <= w < num_words] for x in xs]

    idx = int(len(xs) * (1 - test_split))
    x_train, y_train = np.array(xs[:idx], dtype="object"), np.array(
        labels[:idx]
    )
    x_test, y_test = np.array(xs[idx:], dtype="object"), np.array(labels[idx:])

    return (x_train, y_train), (x_test, y_test)