claudson/ai/lib/python3.12/site-packages/keras/src/datasets/imdb.py

"""IMDB sentiment classification dataset."""

import json

import numpy as np

from keras.src.api_export import keras_export
from keras.src.utils.file_utils import get_file
from keras.src.utils.python_utils import remove_long_seq


@keras_export("keras.datasets.imdb.load_data")
def load_data(
    path="imdb.npz",
    num_words=None,
    skip_top=0,
    maxlen=None,
    seed=113,
    start_char=1,
    oov_char=2,
    index_from=3,
    **kwargs,
):
    """Loads the [IMDB dataset](https://ai.stanford.edu/~amaas/data/sentiment/).

    This is a dataset of 25,000 movies reviews from IMDB, labeled by sentiment
    (positive/negative). Reviews have been preprocessed, and each review 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 the pad token.

    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. When 0, 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`.
        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 + "imdb.npz",
        file_hash=(  # noqa: E501
            "69664113be75683a8fe16e3ed0ab59fda8886cb3cd7ada244f7d9544e4676b9f"
        ),
    )
    with np.load(path, allow_pickle=True) as f:
        x_train, labels_train = f["x_train"], f["y_train"]
        x_test, labels_test = f["x_test"], f["y_test"]

    rng = np.random.RandomState(seed)
    indices = np.arange(len(x_train))
    rng.shuffle(indices)
    x_train = x_train[indices]
    labels_train = labels_train[indices]

    indices = np.arange(len(x_test))
    rng.shuffle(indices)
    x_test = x_test[indices]
    labels_test = labels_test[indices]

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

    if maxlen:
        x_train, labels_train = remove_long_seq(maxlen, x_train, labels_train)
        x_test, labels_test = remove_long_seq(maxlen, x_test, labels_test)
        if not x_train or not x_test:
            raise ValueError(
                "After filtering for sequences shorter than maxlen="
                f"{str(maxlen)}, no sequence was kept. Increase maxlen."
            )

    xs = x_train + x_test
    labels = np.concatenate([labels_train, labels_test])

    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 = len(x_train)
    x_train, y_train = np.array(xs[:idx], dtype="object"), labels[:idx]
    x_test, y_test = np.array(xs[idx:], dtype="object"), labels[idx:]
    return (x_train, y_train), (x_test, y_test)


@keras_export("keras.datasets.imdb.get_word_index")
def get_word_index(path="imdb_word_index.json"):
    """Retrieves a dict mapping words to their index in the IMDB dataset.

    Args:
        path: where to cache the data (relative to `~/.keras/dataset`).

    Returns:
        The word index dictionary. Keys are word strings, values are their
        index.

    Example:

    ```python
    # Use the default parameters to keras.datasets.imdb.load_data
    start_char = 1
    oov_char = 2
    index_from = 3
    # Retrieve the training sequences.
    (x_train, _), _ = keras.datasets.imdb.load_data(
        start_char=start_char, oov_char=oov_char, index_from=index_from
    )
    # Retrieve the word index file mapping words to indices
    word_index = keras.datasets.imdb.get_word_index()
    # Reverse the word index to obtain a dict mapping indices to words
    # And add `index_from` to indices to sync with `x_train`
    inverted_word_index = dict(
        (i + index_from, word) for (word, i) in word_index.items()
    )
    # Update `inverted_word_index` to include `start_char` and `oov_char`
    inverted_word_index[start_char] = "[START]"
    inverted_word_index[oov_char] = "[OOV]"
    # Decode the first sequence in the dataset
    decoded_sequence = " ".join(inverted_word_index[i] for i in x_train[0])
    ```
    """
    origin_folder = (
        "https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
    )
    path = get_file(
        fname=path,
        origin=origin_folder + "imdb_word_index.json",
        file_hash="bfafd718b763782e994055a2d397834f",
    )
    with open(path) as f:
        return json.load(f)