# adult-dataset
A PyTorch dataset wrapper for the
[Adult (Census Income)](https://archive.ics.uci.edu/dataset/2/adult) dataset.
Adult is a popular dataset in machine learning fairness research.
This package provides the `adult.Adult` class:
a`torch.utils.data.Datasets` loading and, optionally, downloading the
Adult dataset.
It can be used like the `MNIST` dataset in
[torchvision](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html?highlight=mnist#torchvision.datasets.MNIST).
Beyond `adult.Adult`, this package also provides `adult.AdultRaw`,
which works just as `adult.Adult`, but
does not standardize the features in the dataset and does not apply one-hot encoding.
## Installation
```shell
pip install adult-dataset
```
## Basic Usage
```python
from adult import Adult
# load (if necessary, download) the Adult training dataset
train_set = Adult(root="datasets", download=True)
# load the test set
test_set = Adult(root="datasets", train=False, download=True)
inputs, target = train_set[0] # retrieve the first sample of the training set
# iterate over the training set
for inputs, target in iter(train_set):
... # Do something with a single sample
# use a PyTorch data loader
from torch.utils.data import DataLoader
loader = DataLoader(test_set, batch_size=32, shuffle=True)
for epoch in range(100):
for inputs, targets in iter(loader):
... # Do something with a batch of samples
```
## Advanced Usage
Turn off status messages while downloading the dataset:
```python
Adult(root=..., output_fn=None)
```
Use the `logging` module for logging status messages while downloading the
dataset instead of placing the status messages on `sys.stdout`.
```python
import logging
Adult(root=..., output_fn=logging.info)
```
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"description": "# adult-dataset\nA PyTorch dataset wrapper for the \n[Adult (Census Income)](https://archive.ics.uci.edu/dataset/2/adult) dataset.\nAdult is a popular dataset in machine learning fairness research. \n\nThis package provides the `adult.Adult` class:\na`torch.utils.data.Datasets` loading and, optionally, downloading the\nAdult dataset.\nIt can be used like the `MNIST` dataset in\n[torchvision](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html?highlight=mnist#torchvision.datasets.MNIST).\n\nBeyond `adult.Adult`, this package also provides `adult.AdultRaw`,\nwhich works just as `adult.Adult`, but\ndoes not standardize the features in the dataset and does not apply one-hot encoding.\n\n## Installation\n```shell\npip install adult-dataset\n```\n\n## Basic Usage\n```python\nfrom adult import Adult\n\n# load (if necessary, download) the Adult training dataset \ntrain_set = Adult(root=\"datasets\", download=True)\n# load the test set\ntest_set = Adult(root=\"datasets\", train=False, download=True)\n\ninputs, target = train_set[0] # retrieve the first sample of the training set\n\n# iterate over the training set\nfor inputs, target in iter(train_set):\n ... # Do something with a single sample\n\n# use a PyTorch data loader\nfrom torch.utils.data import DataLoader\n\nloader = DataLoader(test_set, batch_size=32, shuffle=True)\nfor epoch in range(100):\n for inputs, targets in iter(loader):\n ... # Do something with a batch of samples\n```\n\n## Advanced Usage\n\nTurn off status messages while downloading the dataset:\n```python\nAdult(root=..., output_fn=None)\n```\n\nUse the `logging` module for logging status messages while downloading the\ndataset instead of placing the status messages on `sys.stdout`.\n```python\nimport logging\n\nAdult(root=..., output_fn=logging.info)\n```\n",
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