CausalEGM.util.Semi_acic_sampler

class CausalEGM.util.Semi_acic_sampler(batch_size=32, path='../data/ACIC_2018', ufid='d5bd8e4814904c58a79d7cdcd7c2a1bb')[source]

ACIC 2018 competition dataset (binary treatment) sampler (inherited from Base_sampler).

Parameters:

batch_size – Int object denoting the batch size for mini-batch training. Default: 32.
path – Str object denoting the path to the original dataset.
ufid – Str object denoting the unique id of a specific semi-synthetic setting.

Examples

>>> from CausalEGM import Semi_acic_sampler
>>> import numpy as np
>>> x = np.random.normal(size=(2000,))
>>> y = np.random.normal(size=(2000,))
>>> v = np.random.normal(size=(2000,100))
>>> ds = Semi_acic_sampler(path='../data/ACIC_2018',ufid='d5bd8e4814904c58a79d7cdcd7c2a1bb')

__init__(batch_size=32, path='../data/ACIC_2018', ufid='d5bd8e4814904c58a79d7cdcd7c2a1bb')[source]

Methods

`__init__`([batch_size, path, ufid])
`create_idx_generator`(sample_size[, random_seed])
`load_all`()
`next_batch`()