TensorFlow 1 version

Generates a word rank-based probabilistic sampling table.

tf.keras.preprocessing.sequence.make_sampling_table(
    size, sampling_factor=1e-05
)

Used for generating the sampling_table argument for skipgrams. sampling_table[i] is the probability of sampling the word i-th most common word in a dataset (more common words should be sampled less frequently, for balance).

The sampling probabilities are generated according to the sampling distribution used in word2vec:

p(word) = (min(1, sqrt(word_frequency / sampling_factor) /
    (word_frequency / sampling_factor)))

We assume that the word frequencies follow Zipf's law (s=1) to derive a numerical approximation of frequency(rank):

frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank)) where gamma is the Euler-Mascheroni constant.

Arguments

size: Int, number of possible words to sample.
sampling_factor: The sampling factor in the word2vec formula.

Returns

A 1D Numpy array of length `size` where the ith entry
is the probability that a word of rank i should be sampled.