Angelos Chatzimparmpas
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MIT License |
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Copyright (c) 2020 Angelos Chatzimparmpas |
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Permission is hereby granted, free of charge, to any person obtaining a copy |
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of this software and associated documentation files (the "Software"), to deal |
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in the Software without restriction, including without limitation the rights |
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
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copies of the Software, and to permit persons to whom the Software is |
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furnished to do so, subject to the following conditions: |
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The above copyright notice and this permission notice shall be included in all |
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copies or substantial portions of the Software. |
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
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SOFTWARE. |
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# StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics |
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This Git repository contains the code that accompanies the research paper "StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics". The details of the experiments and the research outcome are described in [the paper](https://doi.org/10.1109/TVCG.2020.3030352). |
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**Note:** StackGenVis is optimized to work better for standard resolutions (such as 1440p/QHD (Quad High Definition)). Any other resolution might need manual adjustment of your browser's zoom level to work properly. |
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**Note:** The tag `paper-version` matches the implementation at the time of the paper's publication. The current version might look significantly different depending on how much time has passed since then. |
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**Note:** As any other software, the code is not bug free. There might be limitations in the views and functionalities of the tool that could be addressed in a future code update. |
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# Data Sets # |
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All publicly available data sets used in the paper are in the `data` folder, formatted as comma separated values (csv). |
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Most of them are available online from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/index.php): Iris and Heart Disease. We also used a collection of data related to sentiment/stance detection in texts. This data set is not included due to permission issues, since it was parsed from well-known social media platforms by our group. |
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# Requirements # |
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For the backend: |
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- [Python 3](https://www.python.org/downloads/) |
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- [Flask](https://palletsprojects.com/p/flask/) |
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- Other packages: `pymongo`, `numpy`, `scipy`, `scikit-learn`, `sk-dist`, `eli5`, and `pandas`. |
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You can install all the backend requirements with the following command: |
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``` |
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pip install -r requirements.txt |
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``` |
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For the frontend: |
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- [Node.js](https://nodejs.org/en/) |
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- [D3.js](https://d3js.org/) |
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- [Plotly.js](https://github.com/plotly/plotly.js/) |
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There is no need to install anything for the frontend, since all modules are in the repository. |
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# Usage # |
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Below is an example of how you can get StackGenVis running using Python for both frontend and backend. The frontend is written in JavaScript/HTML, so it could be hosted in any other web server of your preference. The only hard requirement (currently) is that both frontend and backend must be running on the same machine. |
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``` |
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# first terminal: hosting the visualization side (client) |
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# with Node.js |
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cd frontend |
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npm run dev |
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``` |
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``` |
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# second terminal: hosting the computational side (server) |
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FLASK_APP=run.py flask run |
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# (optional) recommendation: use insertMongo script to add a data set in Mongo database |
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# for Python3 |
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python3 insertMongo.py |
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``` |
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Then, open your browser and point it to `localhost:8080`. We recommend using an up-to-date version of Google Chrome. |
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# Hyper-Parameters per Algorithm # |
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**Base classifiers:** |
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- **K-Nearest Neighbor:** {'n_neighbors': list(range(1, 25)), 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']} |
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- **Support Vector Machine:** {'C': list(np.arange(0.1,4.43,0.11)), 'kernel': ['rbf','linear', 'poly', 'sigmoid']} |
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- **Gaussian Naive Bayes:** {'var_smoothing': list(np.arange(0.00000000001,0.0000001,0.0000000002))} |
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- **Multilayer Perceptron:** {'alpha': list(np.arange(0.00001,0.001,0.0002)), 'tol': list(np.arange(0.00001,0.001,0.0004)), 'max_iter': list(np.arange(100,200,100)), 'activation': ['relu', 'identity', 'logistic', 'tanh'], 'solver' : ['adam', 'sgd']} |
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- **Logistic Regression:** {'C': list(np.arange(0.5,2,0.075)), 'max_iter': list(np.arange(50,250,50)), 'solver': ['lbfgs', 'newton-cg', 'sag', 'saga'], 'penalty': ['l2', 'none']} |
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- **Linear Discriminant Analysis:** {'shrinkage': list(np.arange(0,1,0.01)), 'solver': ['lsqr', 'eigen']} |
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- **Quadratic Discriminant Analysis:** {'reg_param': list(np.arange(0,1,0.02)), 'tol': list(np.arange(0.00001,0.001,0.0002))} |
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- **Random Forests:** {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']} |
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- **Extra Trees:** {'n_estimators': list(range(60, 140)), 'criterion': ['gini', 'entropy']} |
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- **Adaptive Boosting:** {'n_estimators': list(range(40, 80)), 'learning_rate': list(np.arange(0.1,2.3,1.1)), 'algorithm': ['SAMME.R', 'SAMME']} |
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- **Gradient Boosting:** {'n_estimators': list(range(85, 115)), 'learning_rate': list(np.arange(0.01,0.23,0.11)), 'criterion': ['friedman_mse', 'mse', 'mae']} |
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**Meta-learner**: |
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- **Logistic Regression** with the default Sklearn hyper-parameters. By that time, the core hyper-parameter tuples were: C=1.0, max_iter=100, solver='lbfgs', and penalty='l2'. |
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# Corresponding Author # |
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For any questions with regard to the implementation or the paper, feel free to contact [Angelos Chatzimparmpas](mailto:angelos.chatzimparmpas@lnu.se). |
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Reference in new issue