torch_concepts.data.BnLearnDataset

class BnLearnDataset(name: str, root: str | None = None, seed: int = 42, n_gen: int = 10000, concept_subset: list | None = None, label_descriptions: dict | None = None, autoencoder_kwargs: dict | None = None)

Dataset class for the Asia dataset from bnlearn.

This dataset represents a small expert system that models the relationship between traveling to Asia, smoking habits, and various lung diseases.

__init__(name: str, root: str | None = None, seed: int = 42, n_gen: int = 10000, concept_subset: list | None = None, label_descriptions: dict | None = None, autoencoder_kwargs: dict | None = None)

Methods

__init__(name[, root, seed, n_gen, ...])
add_exogenous(name, value[, convert_precision])
add_scaler(key, scaler)	Add a scaler for preprocessing a specific tensor.
build()	Eventually build the dataset from raw data to self.root_dir folder.
collate(samples)	Collate samples into a batch, re-annotating the ground-truth concepts.
download()	Downloads dataset's files to the self.root_dir folder.
load()	Loads raw dataset and preprocess data.
load_raw()	Loads raw dataset without any data preprocessing.
maybe_build()
maybe_download()
remove_exogenous(name)
set_concepts(concepts)	Set concept annotations for the dataset.
set_graph(graph)	Set the adjacency matrix of the causal graph between concepts as a pandas DataFrame.

Attributes

annotations	Annotations for the concepts in the dataset.
concept_names	List of concept names in the dataset.
exogenous	Mapping of dataset's exogenous variables.
graph	Adjacency matrix of the causal graph between concepts.
has_concepts	Whether the dataset has concept annotations.
has_exogenous	Whether the dataset has exogenous information.
n_concepts	Number of concepts in the dataset.
n_exogenous	Number of exogenous variables in the dataset.
n_features	Shape of features in dataset's input (excluding number of samples).
n_samples	Number of samples in the dataset.
processed_filenames	List of processed filenames that will be created during build step.
processed_paths	The absolute paths of the processed files that must be present in order to skip building.
raw_filenames	List of raw filenames that need to be present in the raw directory for the dataset to be considered present.
raw_paths	The absolute paths of the raw files that must be present in order to skip downloading.
root_dir
shape	Shape of the input tensor.