ISubGVQA.sampling.methods.simple

Attributes

DISABLE
MODE

Classes

Layer

Functions

levelwiseSL(levels, idx2primesub, data, theta)
levelwiseMars(levels, idx2primesub, data, theta, parents)
log1mexp(x)
levelOrder(beta)
gumbel_keys(w, time_sampled)
sample_subset(w, k, time_sampled)	Args:

Module Contents

ISubGVQA.sampling.methods.simple.DISABLE = False

ISubGVQA.sampling.methods.simple.MODE = 'default'

ISubGVQA.sampling.methods.simple.levelwiseSL(levels: List[torch.Tensor], idx2primesub: torch.Tensor, data: torch.Tensor, theta: torch.Tensor)

ISubGVQA.sampling.methods.simple.levelwiseMars(levels: List[torch.Tensor], idx2primesub: torch.Tensor, data: torch.Tensor, theta: torch.Tensor, parents: torch.Tensor)

ISubGVQA.sampling.methods.simple.log1mexp(x)

ISubGVQA.sampling.methods.simple.levelOrder(beta)

Return type:

List[List[int]]

ISubGVQA.sampling.methods.simple.gumbel_keys(w, time_sampled)

ISubGVQA.sampling.methods.simple.sample_subset(w, k, time_sampled)

Args:

w (Tensor): Float Tensor of weights for each element. In gumbel mode

these are interpreted as log probabilities

k (int): number of elements in the subset sample

class ISubGVQA.sampling.methods.simple.Layer(n, k, device, root='./simple_configs')

id = 0

parents

levels = []

true_indices

literal_indices

literal_mask

pos_literals

idx2primesub

__call__(log_probs, k)

log_pr(log_probs)

sample(lit_weights, k, time_sampled=1)