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UBC-MDS / canadian_heritage_funding

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Canadian Heritage Funding Size for Art Projects

About

We attempt to build a multi-class classification model which can use features not indicative of artistic merit, such as location, audience, and discipline to predict the funding size granted by the Canadian Heritage Fund (The Fund). We used four popular algorithms for classification questions, including logistics regression, Naive Bayes, Support Vector Classification (SVC) and Random Forest initially. We also used dummyclassifer as a base case. Then, we selected Random Forest as the best algorithm for our question based on each model’s cross-validation scores. We then further conducted hyperparameter optimization on the Random Forest model. Our model performs reasonably well comparing to the base case dummyclassifer with a macro average f-1 score of 0.7 and a weighted-average f-1 score of 0.69. However, we also observed that the model performs worse at classifying funding sizes in range of $12.0-23.0K and $23.0-50.0K comparing to classifying other ranges. Thus, we suggest further study to improve this classification model.

The data set used in this project is provided by the Department of Canadian Heritage (DCH) available on the Government of Canada’s Open Data website and can be found here). Each row of the data set represents an art project funded by the Fund and provides the project’s name, location information (community, city, region and province), presenter information (associated organizations, disciplines, festival or series presentations, etc.), grant or contribution, and audience. The size of the funding approved for each art project is reported by the Fund.

Report

The final report can be found here.

Usage

Creating the environment

conda env create --file environment.yaml

Run the following command from the environment where you installed JupyterLab.

conda install nb_conda_kernels

If you are a windows user, run the following command inside the newly created environment to install vega-lite.

npm install -g vega vega-cli vega-lite canvas

For M1 mac users, make sure you are using the x86 version of conda and not the arm64 version. See here and here for more info.

To replicate the analysis

Clone this Github repository, install the dependencies, and run the following commands at the command line/terminal from the root directory of the project:

make all

To reset the repo to the original state, and delete all results files and report, run the following commands at the command line/terminal from the root directory of the project:

make clean

Dependencies

A complete list of dependencies is available here.
- Python 3.9.7 and Python packages:
- docopt==0.6.1
- pandas==1.3.3
- numpy==1.21.2
- altair_saver=0.5.0
- altair=4.1.0
- scikit-learn=1.0

Replicating the analysis using Docker

If you would like to replicate the analysis using Docker, follow the steps below:

From the root of this project, run the following command to replicate the analysis:

docker-compose run --rm report-env make -C //home//jovyan//work all

To reset the project to the original state, and delete all result files and report, run the following command:

docker-compose run --rm report-env make -C //home//jovyan//work clean

Makefile dependency diagram

License

All Open Government data - Canada is made available under the Open Government License Canada. For a summary of the license see here. If reusing please provide attribution and link to this webpage.

References

“Grouped Arts Evaluation: Canada Arts Presentation Fund, Canada Cultural Spaces Fund, and Canada Cultural Investment Fund 2013-14 to 2017-18_2019.” 2019. The Government of Canada. https://www.canada.ca/en/canadian-heritage/corporate/publications/evaluations/grouped-art-evaluation.html#a1.

Harris, Charles R., K. Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, et al. 2020. “Array Programming with NumPy.” Nature 585: 357–62. https://doi.org/10.1038/s41586-020-2649-2.

McKinney, Wes et al. 2010. “Data Structures for Statistical Computing in Python.” In Proceedings of the 9th Python in Science Conference, 445:51–56. Austin, TX.

Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12 (Oct): 2825–30.

Santini, Lauryn. 2013. “Public Funding of the Visual Arts in Canada: Keeping Creativity at an Arm’s Length.” PhD thesis, Sotheby’s Institute of Art-New York. Van Rossum, Guido, and Fred L. Drake. 2009. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace.