How Keras IMDB dataset data is preprocessed?

Question

I'm working on a problem of sentiment analysis and have a dataset, which is very similar to Kears imdb dataset. When I load Keras’s imdb dataset, it returned sequence of word index.

(X_train, y_train), (X_test, y_test) = imdb.load_data()
X_train[0]
[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 22665, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 21631, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 31050, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]

But, I want to understand, how this sequence is constructed. In my dataset, I used CountVectorizer, with ngram_range=(1,2) in my dataset to tokenize words, but I want to try to replicate Keras approach.

Answer 1

The words in the imdb dataset is replaced with an integer representing how frequently they occur in the dataset. When you are calling the load_data function for the first time it will download the dataset.

To see how the value is calculated, let's take a snippet of code from the source code(link is provided at the end)

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

x_train is the numpy array from list xs of length x_train;

xs is the list formed from all the words in x_train and x_test by first extracting each item (movie review) from the dataset and then extracting the words. The position of each words are then added to index_from which specifies the actual index to start from (defaults to 3) and then added to starting character (1 by default so that the values start from 1 as padding will be done with zeros)

numpy arrays x_train, y_train, x_test, y_test formed in a similar manner and returned by the load_data function.

The source code is available here.

https://github.com/keras-team/keras/blob/master/keras/datasets/imdb.py

Answer 2

As explained here

1. Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). e.g a sentence is preprocessed like I am coming home => [ 1, 3, 11, 15]. Here 1 is the vocabulary index for the word I

2. words are indexed by overall frequency in the dataset. i.e if you are using a CountVectorizer, you need to sort the vocabulary in the descending order of the frequency. Then the resulting order of words corresponding to their vocabulary indices.