chemprop.data.samplers

Classes

SeededSampler	A :class`SeededSampler` is a class for iterating through a dataset in a randomly seeded
ClassBalanceSampler	A ClassBalanceSampler samples data from a MolGraphDataset such that

Module Contents

class chemprop.data.samplers.SeededSampler(N, seed)

Bases: torch.utils.data.Sampler

A :class`SeededSampler` is a class for iterating through a dataset in a randomly seeded fashion

Parameters:

• N (int)

• seed (int)

idxs

rg

__iter__()

an iterator over indices to sample.

Return type:

Iterator[int]

__len__()

the number of indices that will be sampled.

Return type:

int

class chemprop.data.samplers.ClassBalanceSampler(Y, seed=None, shuffle=False)

Bases: torch.utils.data.Sampler

A ClassBalanceSampler samples data from a MolGraphDataset such that positive and negative classes are equally sampled

Parameters:

• dataset (MolGraphDataset) – the dataset from which to sample

• seed (int) – the random seed to use for shuffling (only used when shuffle is True)

• shuffle (bool, default=False) – whether to shuffle the data during sampling

• Y (numpy.ndarray)

shuffle = False

rg

pos_idxs

neg_idxs

length

__iter__()

an iterator over indices to sample.

Return type:

Iterator[int]

__len__()

the number of indices that will be sampled.

Return type:

int