mads-datasets 0.3.5

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.

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())

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

You could also create a dataset directly:

dataset = fashion_factory.create_dataset()

Or download the data:

fashion_factory.download_data()