wild-time-data 0.1.1

Installation

Wild-Time-Data is available via PyPI.

pip install wild-time-data

Usage

The following code will return a PyTorch dataset for the training partition of the arXiv dataset in 2023. The data will be downloaded to wild-time-data/ unless it was downloaded into this folder before.

from wild_time_data import load_dataset

load_dataset(dataset_name="arxiv", time_step=2023, split="train", data_dir="wild-time-data")

In the following we provide details about the available argument options.

• dataset_name: The options are arxiv, drug, fmow, huffpost, and yearbook. This list can be accessed via

  from wild_time_data import list_datasets
  
  list_datasets()

• time_step: Most datasets are grouped by year, this argument will allow you to access the data from different time intervals. The range differs from dataset to dataset. Use following command to get a list of available time steps:

  from wild_time_data import available_time_steps
  
  available_time_steps("arxiv")

• split: Selects the partition. Can either be train or test.

• transform: Defines custom transformations on the predictors of a data point. Can be used to normalize, augment or tokenize data. By default, no transformation is applied for text datasets, image are converted to Tensors via transforms.ToTensor(), and the data for Drug is one-hot encoded. Additionally, FMoW images are normalized. The default transformation can be accessed via

  from wild_time_data import default_transform
  
  default_transform("huffpost")

• target_transform: Same as transform but for labels. By default, no transformation is applied.

• in_memory: If set to True, all data is loaded in memory. By default, data is loaded from disk which might be slower but requires less memory. For all datasets but FMoW in_memory=True should work on most hardware.

• data_dir: Location where to store the data. By default it will be downloaded to ~/wild-time-data/.

Other Useful Functions

Several other functions can be imported from wild_time_data.

from wild_time_data import available_time_steps, input_dim, list_datasets, num_outputs

• available_time_steps: Given the dataset name, a sorted list of available time steps is returned. Example: available_time_steps("huffpost") returns [2012, 2013, 2014, 2015, 2016, 2017, 2018].

• default_transform: Given the dataset name, the transformation which is applied to the predictors unless a custom transformation is passed. Example: default_transform("yearbook") returns transfroms.ToTensor(). If the return value is None, no transformation is applied. In order to override a default transformation, pass transform=lambda x: x to load_dataset.

• input_dim: Given the dataset name, the input dimensionality is returned. For image datasets the shape of the image is returned. For text datasets the maximum number of words separated by spaces is returned. Example: input_dim("yearbook") returns (1, 32, 32).

• list_datasets: Returns the list of all available datasets. Example: list_datasets() returns ["arxiv", "drug", "fmow", "huffpost", "yearbook"].

• num_outputs: Given the dataset name, either the number of classes is returned or it returns 1. In cases where 1 is returned, this indicates that this is a regression dataset. Example: num_outputs("arxiv") returns 172.

FMoW Dataset

If you want to use the FMoW dataset, please follow the instructions to prepare it.

Licenses

All additional code for Wild-Time-Data is available under the Apache 2.0 license. The license for each Wild-Time dataset is listed below:

• arXiv: CC0: Public Domain

• Drug-BA: MIT License

• FMoW: The Functional Map of the World Challenge Public License

• Huffpost: CC0: Public Domain

• Yearbook: MIT License

Furthermore, this repository is loosely based on the Wild-Time repository which is licensed under the MIT License.