This script generates a dataset similar to the Multi-MNIST dataset described in .
 Eslami, SM Ali, et al. “Attend, infer, repeat: Fast scene understanding with generative models.” Advances in Neural Information Processing Systems. 2016.
Load a dataset of hourly origin-destination ridership counts for every pair of BART stations during the years 2011-2019.
This downloads the dataset the first time it is called. On subsequent calls this reads from a local cached file
.pkl.bz2. This attempts to download a preprocessed compressed cached file maintained by the Pyro team. On cache hit this should be very fast. On cache miss this falls back to downloading the original data source and preprocessing the dataset, requiring about 350MB of file transfer, storing a few GB of temp files, and taking upwards of 30 minutes.
a dataset is a dictionary with fields:
”stations”: a list of strings of station names
datetime.datetimefor the first observaion
torch.FloatTensorof ridership counts, with shape
(num_hours, len(stations), len(stations)).
Nextstrain SARS-CoV-2 counts¶
- load_nextstrain_counts(map_location=None) dict [source]¶
Loads a SARS-CoV-2 dataset.
The original dataset is a preprocessed intermediate
metadata.tsv.gzavailable via nextstrain. The
metadata.tsv.gzfile was then aggregated to (month,location,lineage) and (lineage,mutation) bins by the Broad Institute’s preprocessing script.
- class MNIST(root: str, train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)[source]¶
- mirrors = ['https://d2hg8soec8ck9v.cloudfront.net/datasets/mnist/', 'http://yann.lecun.com/exdb/mnist/', 'https://ossci-datasets.s3.amazonaws.com/mnist/']¶
- get_data_loader(dataset_name, data_dir, batch_size=1, dataset_transforms=None, is_training_set=True, shuffle=True)[source]¶