mads-datasets 0.3.10

Datasets for the master applied data science

MADS Datasets Library

This library provides the functionality to download, process, and stream several datasets.

Installation

This library has been published on PyPi and can be installed with pip, conda, pdm or poetry.

# Install with pip
pip install mads_datasets

# Install with poetry
poetry add mads_datasets

# install with pdm
pdm add mads_datasets

Data Types

Currently, it supports the following datasets:

• SUNSPOTS Time-Series data, 3000 monthly sunspot observations from 1749
• IMDB Text data, 50k movie reviews with positive and negative sentiment labels
• FLOWERS Image data, about 3000 large and complex images of 5 flowers
• FASHION MNIST Image data, 60k images sized 28x28 pixels
• GESTURES Time-Series data with x, y and z accelerometer data for 20 gestures.
• IRIS dataset, 150 observations of 4 features of 3 iris flower species
• PENGUINS dataset, an alternative to Iris with 344 penguins on multiple islands.
• FAVORITA dataset, 125 million sales records of 50k products in 54 stores.

An additional type is SECURE, which is used for datasets that are not publicly available. See examples below.

Usage

After installation, import the necessary components:

from mads_datasets import DatasetFactoryProvider, DatasetType

You can create a specific dataset factory using the DatasetFactoryProvider.

For instance, to create a factory for the Fashion MNIST dataset:

fashion_factory = DatasetFactoryProvider.create_factory(DatasetType.FASHION)

With the factory, you can download the data, create datasets and provide the datasets wrapped in datastreamers in one command:

streamers = mnistfactory.create_datastreamer(batchsize=32)
train = streamers["train"]
X, y = next(train.stream())

This will do multiple things:

• it downloads the data to the default location ~/.cache/mads_datasets if not already present
• it checks the hash of the file
• it transforms the file into a dataset that implements the DatasetProtocol:

class DatasetProtocol(Protocol):
    def __len__(self) -> int:
        ...

    def __getitem__(self, idx: int) -> Any:
        ...

• it creates a streamer.

The train.stream() command wil return a generator that yields batches of data.

You could also create a dataset directly:

dataset = fashion_factory.create_dataset()

Or just download the data:

fashion_factory.download_data()

Examples

Default cache location

fashion_factory.datadir

This shows you the default location of all datasets. Storing the datasets in a central place makes it much easier to manage your storage over multiple projects where you might want to reuse the same dataset multiple times.

It also makes it much easier to clean you storage, as you can just delete the entire directory.

In general, it is bad practice to store the raw data in your git repo. Using a central location avoids this (as would adding your data folder to .gitignore). Instead of storing data in git, provide the user with a script

• that downloads the raw data
• preprocesses the raw data into something that fits your needs

If you want to change the default location of the cache, give the factory a datadir argument like this:

from pathlib import Path
fashion_factory = DatasetFactoryProvider.create_factory(
    dataset_type=DatasetType.FASHION,
    datadir=Path("~/path/to/alternative/folder")
    )

Now, instead of using ~/.cache/mads_datasets, the data will be stored in ~/path/to/alternative/folder which can be anywhere you have access to.

using Secure datasets

from mads_datasets.settings import SecureDatasetSettings
from mads_datasets import DatasetFactoryProvider, DatasetType

garbagesettings = SecureDatasetSettings(
    dataset_url="https://gitlab.com/api/v4/projects/12345/repository/files/filename.extension/raw?lfs=true",
    filename="garbage.parq",
    name="garbage",
    keyaccount="gitlab-MADS-PAT",
    keyname="gitlab-MADS-PAT",
    digest="b5ee4ab8723e0d97e0eefa12e347d04e",
    unzip=False,
)
garbagedata = DatasetFactoryProvider.create_factory(settings=garbagesettings, dataset_type=DatasetType.SECURE)
garbagedata.download_data()

For secure datasets, you will have to provide a SecureDatasetSettings object. Currently, this is implemented for the gitlab API.

You will have to provide an url where you change 12345 with the project ID (settings>general). You should also create a PAT (settings> access tokens).

Change filename.extension with your filename (eg. garbage.parq). The first time you run this, you will have to provide your PAT. It will be stored in your keyring as gitlab-MADS-PAT, or whatever you set as keyaccount and keyname.

The digest can be left out; the first time you run this, you will get the digest and you can add it to make sure the file is not corrupted.

If your file needs unzipping, set unzip=True.