From 093443097c23bb33b0539348e13e878ea0bf55e6 Mon Sep 17 00:00:00 2001 From: Christian Versloot Date: Tue, 1 Dec 2020 09:26:47 +0100 Subject: [PATCH] Add Wine Quality dataset, #6 --- README.md | 20 ++++- assets/wine_quality.jpg | Bin 0 -> 126707 bytes extra_keras_datasets/__init__.py | 3 +- extra_keras_datasets/wine_quality.py | 117 +++++++++++++++++++++++++++ setup.py | 2 +- 5 files changed, 139 insertions(+), 3 deletions(-) create mode 100644 assets/wine_quality.jpg create mode 100644 extra_keras_datasets/wine_quality.py diff --git a/README.md b/README.md index f9f32d7..24fd3f8 100644 --- a/README.md +++ b/README.md @@ -31,6 +31,7 @@ _The names TensorFlow, Keras, as well as related names, marks, emblems and image * [SVHN-Extra](#svhn-extra) * [STL-10](#stl-10) * [Iris](#iris) + * [Wine Quality dataset](#wine-quality-dataset) - [Contributors and other references](#contributors-and-other-references) - [License](#license) @@ -42,7 +43,7 @@ This package makes use of the TensorFlow 2.x package and specifically `tensorflo * `pip install tensorflow` ### Installation procedure -Installing is really easy, and can be done with [PIP](https://pypi.org/project/extra-keras-datasets/): `pip install extra-keras-datasets`. +Installing is really easy, and can be done with [PIP](https://pypi.org/project/extra-keras-datasets/): `pip install extra-keras-datasets`. The package depends on `numpy`, `scipy`, `pandas` and `scikit-learn`, which will be automatically installed. ## Datasets @@ -192,6 +193,21 @@ from extra_keras_datasets import iris --- +### Wine Quality dataset +This dataset presents wine qualities related to red and white vinho verde wine samples, from the north of Portugal. According to the creators, "[the] goal is to model wine quality based on physicochemical tests". Various chemical properties of the wine are available as well (`inputs`) as well as the quality score (`targets`) for the wine. + +* Input structure: (fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol, wine type) +* Target structure: quality score between 0 and 10 + +``` +from extra_keras_datasets import wine_quality +(input_train, target_train), (input_test, target_test) = wine_quality.load_data(which_data='both', test_split=0.2, shuffle=True) +``` + + + +--- + ## Contributors and other references * **EMNIST dataset:** * Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: an extension of MNIST to handwritten letters. Retrieved from http://arxiv.org/abs/1702.05373 @@ -204,6 +220,8 @@ from extra_keras_datasets import iris * Coates, A., Ng, A., & Lee, H. (2011, June). An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 215-223). Retrieved from http://cs.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf * **Iris dataset:** * Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950). +* **Wine Quality dataset:** + * P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009. ## License The licenseable parts of this repository are licensed under a [MIT License](./LICENSE), so you're free to use this repo in your machine learning projects / blogs / exercises, and so on. 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