import numpy as np
import tensorflow as tf
[docs]
def mutate_categorical(X: np.ndarray,
rate: float = None,
seed: int = 0,
feature_range: tuple = (0, 255)) -> tf.Tensor:
"""
Randomly change integer feature values to values within a set range
with a specified permutation rate.
Parameters
----------
X
Batch of data to be perturbed.
rate
Permutation rate (between 0 and 1).
seed
Random seed.
feature_range
Min and max range for perturbed features.
Returns
-------
Array with perturbed data.
"""
frange = (feature_range[0] + 1, feature_range[1] + 1)
shape = X.shape
n_samples = np.prod(shape)
mask = tf.random.categorical(
tf.math.log([[1. - rate, rate]]),
n_samples,
seed=seed,
dtype=tf.int32
)
mask = tf.reshape(mask, shape)
possible_mutations = tf.random.uniform(
shape,
minval=frange[0],
maxval=frange[1],
dtype=tf.int32,
seed=seed + 1
)
X = tf.math.floormod(tf.cast(X, tf.int32) + mask * possible_mutations, frange[1])
return tf.cast(X, tf.float32)