import numpy as np
from .base import ScoredQuerySampler
from .typeutils import RandomStateType, check_random_state
[docs]class RandomSampler(ScoredQuerySampler):
"""Randomly select samples
Args:
batch_size : Number of samples to select.
random_state : The seed of the pseudo random number generator to use
when shuffling the data. If int, random_state is the seed used by
the random number generator; If RandomState instance, random_state
is the random number generator; If None (defdault), the random
number generator is the RandomState instance used by `np.random`.
Attributes:
random_state : The random state used by the sampler.
"""
def __init__(self, batch_size: int, random_state: RandomStateType = None):
super().__init__(batch_size=batch_size)
self.random_state = random_state
[docs] def fit(self, X: np.array = None, y: np.array = None) -> 'RandomSampler':
"""Sets the random state
Args:
X: Labeled samples of shape (n_samples, n_features).
y: Labels of shape (n_samples).
Returns:
The object itself
"""
self.random_state = check_random_state(self.random_state)
return self
[docs] def score_samples(self, X: np.array) -> np.array:
return self.random_state.rand(X.shape[0])